diff --git a/README.md b/README.md
index e858774..44ceb06 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-![pylint](https://img.shields.io/badge/PyLint-9.52-yellow?logo=python&logoColor=white)
+![pylint](https://img.shields.io/badge/PyLint-9.45-yellow?logo=python&logoColor=white)
 # Quantum Computing Application Specifications
 
 Documentation of Applications for Quantum Computers
diff --git a/notebooks/HighTemperatureSuperConductorExample.ipynb b/notebooks/HighTemperatureSuperConductorExample.ipynb
index a8fcbb4..35c635a 100644
--- a/notebooks/HighTemperatureSuperConductorExample.ipynb
+++ b/notebooks/HighTemperatureSuperConductorExample.ipynb
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The Fermi-Hubbard model is often used to predict the behavior of the electrons of proposed superconductors.  It has the advantage of being simply stated while describing a variety of phenomena relating to the phases of real-world materials such as the transition between superconducting and Mott insulator phases.  In order to understand the behavior of the Fermi-Hubbard model at high temperatures, it is first critical to understand the phase diagram of the Fermi-Hubbard model at absolute zero (with no heat-bath present).  To achieve this, one must understand the ground state energy of the model with various filling coefficients.  In this notebook, we will show the necessary steps perform this analysis on a quantum computer.\n",
+    "The Fermi-Hubbard model is often used to predict the behavior of the electrons of proposed superconductors.  It has the advantage of being simply stated while describing a variety of phenomena relating to the phases of real-world materials such as the transition between superconducting and Mott insulator phases.  In order to understand the behavior of the Fermi-Hubbard model at high temperatures, it is first critical to understand the phase diagram of the Fermi-Hubbard model at absolute zero (with no heat-bath present).  To achieve this, one must understand the ground state energy of the model with various filling coefficients.  In this notebook, we will show the necessary steps to perform this analysis on a quantum computer.\n",
     "\n",
     "The most basic form of the Fermi-Hubbard model with a single band can be represented through the following equation:\n",
     "\n",
@@ -34,7 +34,7 @@
     "  + U \\sum n_{i, \\uparrow} n_{i, \\downarrow}\n",
     "\\end{equation}\n",
     "\n",
-    "where $c^{\\dagger}_{i, \\uparrow}$ is the fermionic creation operator on site $i$ with a spin $\\uparrow$ and $c_{i, \\uparrow}$ is the fermionic annihilation operator on site $i$ with a spin $\\uparrow$.  The number operator on site $i$ with spin $\\uparrow$ is given by $n_{i, \\uparrow} = c^{\\dagger}_{i, \\uparrow} c_{i, \\uparrow}$.  We will assume that the interactions are occurring on a 2-d square lattice, defining the site connectivity $(i,j)$  The coefficients $t_{i,j}$ represent the affinity for tunnelling between two orbitals at sites $i$ and $j$ and the coefficient $U$ represents the strength of the Coulomb interaction between electrons on the same orbital.\n",
+    "where $c^{\\dagger}_{i, \\uparrow}$ is the fermionic creation operator on site $i$ with a spin $\\uparrow$ and $c_{i, \\uparrow}$ is the fermionic annihilation operator on site $i$ with a spin $\\uparrow$.  The number operator on site $i$ with spin $\\uparrow$ is given by $n_{i, \\uparrow} = c^{\\dagger}_{i, \\uparrow} c_{i, \\uparrow}$.  We will assume that the interactions are occurring on a 2-d square lattice, defining the site connectivity $(i,j)$. The coefficients $t_{i,j}$ represent the affinity for tunnelling between two orbitals at sites $i$ and $j$ and the coefficient $U$ represents the strength of the Coulomb interaction between electrons on the same orbital.\n",
     "\n",
     "There are 3 steps to estimate the ground state energy of this Hamiltonian:\n",
     "\n",
@@ -50,20 +50,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 20,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jonhas/anaconda3/envs/other_qca/lib/python3.11/site-packages/attr/_make.py:918: RuntimeWarning: Running interpreter doesn't sufficiently support code object introspection.  Some features like bare super() or accessing __class__ will not work with slotted classes.\n",
-      "  set_closure_cell(cell, cls)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import time\n",
     "\n",
@@ -75,12 +66,13 @@
     "\n",
     "from qca.utils.utils import EstimateMetaData\n",
     "from qca.utils.algo_utils import gsee_resource_estimation\n",
-    "from qca.utils.hamiltonian_utils import generate_two_orbital_nx, nx_to_two_orbital_hamiltonian"
+    "from qca.utils.hamiltonian_utils import (generate_two_orbital_nx, nx_to_two_orbital_hamiltonian,\n",
+    "     generate_three_orbital_nx, nx_to_three_orbital_hamiltonian)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 21,
    "metadata": {
     "tags": []
    },
@@ -88,6 +80,9 @@
    "source": [
     "#Generating a 20x20 Fermi Hubbard model with a single band.  The ratio between Tunneling and Coulomb parameters can be swept to search for the appropriate mean electron filling.\n",
     "n = 20\n",
+    "####START UNCOMMENT FOR TESTING\n",
+    "n = 2\n",
+    "####END UNCOMMENT FOR TESTING\n",
     "tunneling = 1\n",
     "coulomb = 8\n",
     "ham_one_band = of.fermi_hubbard(n, n, tunneling=tunneling, coulomb=coulomb, periodic=False) #returns an aperiodic fermionic hamiltonian"
@@ -179,18 +174,18 @@
      "output_type": "stream",
      "text": [
       "Estimating one_band\n",
-      "Time to generate circuit for GSEE: 0.0008551250000436994 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.common_gates.HPowGate circuit: 0.00019104200009678607 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.common_gates.HPowGate circuit to Clifford+T: 0.0033219580000150017 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.identity.IdentityGate circuit: 1.4458999885391677e-05 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.identity.IdentityGate circuit to Clifford+T: 7.165999932112754e-06 seconds\n",
-      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit: 9.066699999493721e-05 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit to Clifford+T: 8.924999997361738e-05 seconds\n",
-      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit: 93.75897133299986 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit to Clifford+T: 78.74259358400013 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.measurement_gate.MeasurementGate circuit: 0.07056762499996694 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.measurement_gate.MeasurementGate circuit to Clifford+T: 9.099999988393392e-05 seconds\n",
-      "Time to estimate one_band: 241.37923841700012\n"
+      "Time to generate circuit for GSEE: 8.066699956543744e-05 seconds\n",
+      "   Time to decompose high level <class 'cirq.ops.common_gates.HPowGate circuit: 0.00020158299594186246 seconds \n",
+      "   Time to transform decomposed <class 'cirq.ops.common_gates.HPowGate circuit to Clifford+T: 0.0004812920233234763 seconds\n",
+      "   Time to decompose high level <class 'cirq.ops.identity.IdentityGate circuit: 2.72089964710176e-05 seconds \n",
+      "   Time to transform decomposed <class 'cirq.ops.identity.IdentityGate circuit to Clifford+T: 9.29099041968584e-06 seconds\n",
+      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit: 0.00012083398178219795 seconds \n",
+      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit to Clifford+T: 0.00011850000009872019 seconds\n",
+      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit: 0.049456957989605144 seconds \n",
+      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit to Clifford+T: 0.36098670901264995 seconds\n",
+      "   Time to decompose high level <class 'cirq.ops.measurement_gate.MeasurementGate circuit: 0.00025645800633355975 seconds \n",
+      "   Time to transform decomposed <class 'cirq.ops.measurement_gate.MeasurementGate circuit to Clifford+T: 7.162499241530895e-05 seconds\n",
+      "Time to estimate one_band: 0.6886332079884596\n"
      ]
     }
    ],
@@ -204,6 +199,7 @@
     "    init_state=init_state_one_band,\n",
     "    precision_order=1,\n",
     "    bits_precision=bits_precision_one_band,\n",
+    "    phase_offset=one_band_phase_offset,\n",
     "    circuit_name='one_band',\n",
     "    metadata=one_band_metadata,\n",
     "    write_circuits=True\n",
@@ -245,7 +241,7 @@
     "\\end{split}\n",
     "\\end{equation}\n",
     "\n",
-    "where $t_1 = -1.0, t_2 = 1.3, t_3 = t_4 = -0.85$ with operators $d^{\\dagger}_{i,\\alpha,\\sigma}$ and $d_{i,\\alpha,\\sigma}$ respectively create or annihilate electrons on an atom at site $i$, with orbital $\\alpha$, and spin $\\sigma$.  The operator $n^{\\alpha}_{i}$ represents the number operator of an atom at a given orbital, i.e. $n^x_i = \\sum_{\\sigma} \\left( d^{\\dagger}_{i,x,\\sigma} d_{i,x,\\sigma} \\right)$. The indices in the Hamiltonian are assigned in the following manner: the index $i$ will correspond to a 2-tuple $(m,n)$ indicating the x and y coordinates of the atom in the lattice.  Since there are 2 orbitals, $\\alpha \\in \\{x,y\\}$.  Lastly, $\\sigma \\in \\{\\uparrow, \\downarrow \\}$.  For the sake of simplicity of coding, we will remap these labels to integer values, meaning we will assign indices $m \\in L_x, n \\in L_y, a \\in \\mathbb{Z}^2, s \\in \\mathbb{Z}^2$ where $L_x$ is the $x$ dimension of the square lattice, and $L_y$ is the $y$ dimension of the square lattice.  In the summations, $\\hat{x}$ and $\\hat{y}$ correspond to adjacent sites in the lattice, rather than the orbitals.  The summation over unit vectors $\\hat{\\mu}$ and $\\hat{\\nu}$ correspond to the summing over unit vectors in all 8 cardinal and intercardinal directions without double counting.\n",
+    "where $t_1 = -1.0, t_2 = 1.3, t_3 = t_4 = -0.85$ with operators $d^{\\dagger}_{i,\\alpha,\\sigma}$ and $d_{i,\\alpha,\\sigma}$ respectively create or annihilate electrons on an atom at site $i$, with orbital $\\alpha$, and spin $\\sigma$.  The operator $n^{\\alpha}_{i}$ represents the number operator of an atom at a given orbital, i.e. $n^x_i = \\sum_{\\sigma} \\left( d^{\\dagger}_{i,x,\\sigma} d_{i,x,\\sigma} \\right)$. The indices in the Hamiltonian are assigned in the following manner: the index $i$ will correspond to a 2-tuple $(m,n)$ indicating the x and y coordinates of the atom in the lattice.  Since there are 2 orbitals, $\\alpha \\in \\{x,y\\}$.  Lastly, $\\sigma \\in \\{\\uparrow, \\downarrow \\}$.  For the sake of simplicity of coding, we will remap these labels to integer values, meaning we will assign indices $m \\in L_x, n \\in L_y, a \\in \\mathbb{Z}^2, s \\in \\mathbb{Z}^2$ where $L_x$ is the $x$ dimension of the square lattice, and $L_y$ is the $y$ dimension of the square lattice.  In the summations, $\\hat{x}$ and $\\hat{y}$ correspond to adjacent sites in the lattice, rather than the orbitals.  The summation over unit vectors $\\hat{\\mu}$ and $\\hat{\\nu}$ correspond to the summing over unit vectors $\\hat{x}$ and $\\hat{y}$.  Note that where a summation is over $\\hat{\\mu}$ and $\\hat{\\nu}$, that it refers to both possible cross-plaquette interactions (next nearest neighbor interactions), but it does not refer to interactions between either nearest neighbors or next nearest neighbors two unit distances away in a single direction.\n",
     "\n",
     "We will consider two instances: one which represents the current capabilities of the best classical solvers (a 6x7 lattice), and one which represents the ideal capabilities of a quantum solver (a 20x20 lattice) of sites.  We want to obtain the results with a precision on the order of $10^{-5}$, so we will use 16 bits of precision."
    ]
@@ -257,9 +253,16 @@
     "tags": []
    },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[((0, 0, 0, 0), (0, 1, 0, 0), {'label': '-t1'}), ((0, 0, 0, 0), (1, 0, 0, 0), {'label': '-t2'}), ((0, 0, 0, 0), (1, 1, 0, 0), {'label': '-t3'}), ((0, 0, 0, 0), (1, 1, 1, 0), {'label': '+t4'}), ((0, 0, 0, 1), (0, 1, 0, 1), {'label': '-t1'}), ((0, 0, 0, 1), (1, 0, 0, 1), {'label': '-t2'}), ((0, 0, 0, 1), (1, 1, 0, 1), {'label': '-t3'}), ((0, 0, 0, 1), (1, 1, 1, 1), {'label': '+t4'}), ((0, 0, 1, 0), (1, 0, 1, 0), {'label': '-t1'}), ((0, 0, 1, 0), (0, 1, 1, 0), {'label': '-t2'}), ((0, 0, 1, 0), (1, 1, 1, 0), {'label': '-t3'}), ((0, 0, 1, 0), (1, 1, 0, 0), {'label': '+t4'}), ((0, 0, 1, 1), (1, 0, 1, 1), {'label': '-t1'}), ((0, 0, 1, 1), (0, 1, 1, 1), {'label': '-t2'}), ((0, 0, 1, 1), (1, 1, 1, 1), {'label': '-t3'}), ((0, 0, 1, 1), (1, 1, 0, 1), {'label': '+t4'}), ((0, 1, 0, 0), (1, 1, 0, 0), {'label': '-t2'}), ((0, 1, 0, 0), (1, 0, 0, 0), {'label': '-t3'}), ((0, 1, 0, 0), (1, 0, 1, 0), {'label': '-t4'}), ((0, 1, 0, 1), (1, 1, 0, 1), {'label': '-t2'}), ((0, 1, 0, 1), (1, 0, 0, 1), {'label': '-t3'}), ((0, 1, 0, 1), (1, 0, 1, 1), {'label': '-t4'}), ((0, 1, 1, 0), (1, 1, 1, 0), {'label': '-t1'}), ((0, 1, 1, 0), (1, 0, 1, 0), {'label': '-t3'}), ((0, 1, 1, 0), (1, 0, 0, 0), {'label': '-t4'}), ((0, 1, 1, 1), (1, 1, 1, 1), {'label': '-t1'}), ((0, 1, 1, 1), (1, 0, 1, 1), {'label': '-t3'}), ((0, 1, 1, 1), (1, 0, 0, 1), {'label': '-t4'}), ((1, 0, 0, 0), (1, 1, 0, 0), {'label': '-t1'}), ((1, 0, 0, 1), (1, 1, 0, 1), {'label': '-t1'}), ((1, 0, 1, 0), (1, 1, 1, 0), {'label': '-t2'}), ((1, 0, 1, 1), (1, 1, 1, 1), {'label': '-t2'})]\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -273,7 +276,9 @@
     "pos = get_node_attributes(g_example, 'pos')\n",
     "edge_labels = dict([((n1, n2), d['label']) for n1, n2, d in g_example.edges(data=True)]);\n",
     "draw(g_example, pos)\n",
-    "draw_networkx_edge_labels(g_example,pos, edge_labels = edge_labels);"
+    "draw_networkx_edge_labels(g_example,pos, edge_labels = edge_labels);\n",
+    "\n",
+    "print(g_example.edges(data=True))"
    ]
   },
   {
@@ -287,84 +292,84 @@
      "data": {
       "text/plain": [
        "-1.0 [0^ 0] +\n",
-       "-0.85 [0^ 4] +\n",
-       "-0.85 [0^ 8] +\n",
+       "1.0 [0^ 4] +\n",
+       "-1.3 [0^ 8] +\n",
        "-0.85 [0^ 12] +\n",
        "0.85 [0^ 14] +\n",
        "-1.0 [1^ 1] +\n",
-       "-0.85 [1^ 5] +\n",
-       "-0.85 [1^ 9] +\n",
+       "1.0 [1^ 5] +\n",
+       "-1.3 [1^ 9] +\n",
        "-0.85 [1^ 13] +\n",
        "0.85 [1^ 15] +\n",
        "-1.0 [2^ 2] +\n",
-       "-0.85 [2^ 6] +\n",
-       "-0.85 [2^ 10] +\n",
+       "-1.3 [2^ 6] +\n",
+       "1.0 [2^ 10] +\n",
        "0.85 [2^ 12] +\n",
        "-0.85 [2^ 14] +\n",
        "-1.0 [3^ 3] +\n",
-       "-0.85 [3^ 7] +\n",
-       "-0.85 [3^ 11] +\n",
+       "-1.3 [3^ 7] +\n",
+       "1.0 [3^ 11] +\n",
        "0.85 [3^ 13] +\n",
        "-0.85 [3^ 15] +\n",
-       "-0.85 [4^ 0] +\n",
+       "1.0 [4^ 0] +\n",
        "-1.0 [4^ 4] +\n",
        "-0.85 [4^ 8] +\n",
        "-0.85 [4^ 10] +\n",
-       "-0.85 [4^ 12] +\n",
-       "-0.85 [5^ 1] +\n",
+       "-1.3 [4^ 12] +\n",
+       "1.0 [5^ 1] +\n",
        "-1.0 [5^ 5] +\n",
        "-0.85 [5^ 9] +\n",
        "-0.85 [5^ 11] +\n",
-       "-0.85 [5^ 13] +\n",
-       "-0.85 [6^ 2] +\n",
+       "-1.3 [5^ 13] +\n",
+       "-1.3 [6^ 2] +\n",
        "-1.0 [6^ 6] +\n",
        "-0.85 [6^ 8] +\n",
        "-0.85 [6^ 10] +\n",
-       "-0.85 [6^ 14] +\n",
-       "-0.85 [7^ 3] +\n",
+       "1.0 [6^ 14] +\n",
+       "-1.3 [7^ 3] +\n",
        "-1.0 [7^ 7] +\n",
        "-0.85 [7^ 9] +\n",
        "-0.85 [7^ 11] +\n",
-       "-0.85 [7^ 15] +\n",
-       "-0.85 [8^ 0] +\n",
+       "1.0 [7^ 15] +\n",
+       "-1.3 [8^ 0] +\n",
        "-0.85 [8^ 4] +\n",
        "-0.85 [8^ 6] +\n",
        "-1.0 [8^ 8] +\n",
-       "-0.85 [8^ 12] +\n",
-       "-0.85 [9^ 1] +\n",
+       "1.0 [8^ 12] +\n",
+       "-1.3 [9^ 1] +\n",
        "-0.85 [9^ 5] +\n",
        "-0.85 [9^ 7] +\n",
        "-1.0 [9^ 9] +\n",
-       "-0.85 [9^ 13] +\n",
-       "-0.85 [10^ 2] +\n",
+       "1.0 [9^ 13] +\n",
+       "1.0 [10^ 2] +\n",
        "-0.85 [10^ 4] +\n",
        "-0.85 [10^ 6] +\n",
        "-1.0 [10^ 10] +\n",
-       "-0.85 [10^ 14] +\n",
-       "-0.85 [11^ 3] +\n",
+       "-1.3 [10^ 14] +\n",
+       "1.0 [11^ 3] +\n",
        "-0.85 [11^ 5] +\n",
        "-0.85 [11^ 7] +\n",
        "-1.0 [11^ 11] +\n",
-       "-0.85 [11^ 15] +\n",
+       "-1.3 [11^ 15] +\n",
        "-0.85 [12^ 0] +\n",
        "0.85 [12^ 2] +\n",
-       "-0.85 [12^ 4] +\n",
-       "-0.85 [12^ 8] +\n",
+       "-1.3 [12^ 4] +\n",
+       "1.0 [12^ 8] +\n",
        "-1.0 [12^ 12] +\n",
        "-0.85 [13^ 1] +\n",
        "0.85 [13^ 3] +\n",
-       "-0.85 [13^ 5] +\n",
-       "-0.85 [13^ 9] +\n",
+       "-1.3 [13^ 5] +\n",
+       "1.0 [13^ 9] +\n",
        "-1.0 [13^ 13] +\n",
        "0.85 [14^ 0] +\n",
        "-0.85 [14^ 2] +\n",
-       "-0.85 [14^ 6] +\n",
-       "-0.85 [14^ 10] +\n",
+       "1.0 [14^ 6] +\n",
+       "-1.3 [14^ 10] +\n",
        "-1.0 [14^ 14] +\n",
        "0.85 [15^ 1] +\n",
        "-0.85 [15^ 3] +\n",
-       "-0.85 [15^ 7] +\n",
-       "-0.85 [15^ 11] +\n",
+       "1.0 [15^ 7] +\n",
+       "-1.3 [15^ 11] +\n",
        "-1.0 [15^ 15]"
       ]
      },
@@ -394,9 +399,9 @@
     "g_ideal = generate_two_orbital_nx(20,20)\n",
     "\n",
     "##### START UNCOMMENT FOR TESTING\n",
-    "#n_test = 2\n",
-    "#g_current_limit = generate_two_orbital_nx(n_test,n_test)  \n",
-    "#g_ideal = generate_two_orbital_nx(n_test,n_test)\n",
+    "n_test = 2\n",
+    "g_current_limit = generate_two_orbital_nx(n_test,n_test)  \n",
+    "g_ideal = generate_two_orbital_nx(n_test,n_test)\n",
     "##### END UNCOMMENT FOR TESTING\n",
     "n_qubits_current_limit = len(g_current_limit)\n",
     "n_qubits_ideal = len(g_ideal)"
@@ -493,7 +498,7 @@
     "    category='scientific',\n",
     "    size=f'{6}x{7}',\n",
     "    task='Ground State Energy Estimation',\n",
-    "    implementations=f'GSEE, evolution_time={t_one_band}, bits_precision={bits_precision_one_band}, trotter_order={trotter_order_one_band}',\n",
+    "    implementations=f'GSEE, evolution_time={t_current_limit}, bits_precision={bits_precision_current_limit}, trotter_order={trotter_order_current_limit}',\n",
     ")\n",
     "\n",
     "metadata_ideal = EstimateMetaData(\n",
@@ -502,7 +507,7 @@
     "    category='scientific',\n",
     "    size=f'{20}x{20}',\n",
     "    task='Ground State Energy Estimation',\n",
-    "    implementations=f'GSEE, evolution_time={t_one_band}, bits_precision={bits_precision_one_band}, trotter_order={trotter_order_one_band}',\n",
+    "    implementations=f'GSEE, evolution_time={t_ideal}, bits_precision={bits_precision_ideal}, trotter_order={trotter_order_ideal}',\n",
     ")"
    ]
   },
@@ -516,31 +521,31 @@
      "output_type": "stream",
      "text": [
       "Estimating Current Limit\n",
-      "Time to generate circuit for GSEE: 0.00016554200010432396 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.common_gates.HPowGate circuit: 9.36659998842515e-05 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.common_gates.HPowGate circuit to Clifford+T: 0.00018891700005951861 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.identity.IdentityGate circuit: 1.137499998549174e-05 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.identity.IdentityGate circuit to Clifford+T: 3.707999894686509e-06 seconds\n",
-      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit: 7.216700009848864e-05 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit to Clifford+T: 7.266600005095825e-05 seconds\n",
-      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit: 23.03034737500002 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit to Clifford+T: 28.72352683400004 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.measurement_gate.MeasurementGate circuit: 0.03737262499998906 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.measurement_gate.MeasurementGate circuit to Clifford+T: 7.870899980844115e-05 seconds\n",
-      "Time to estimate Current Limit: 83.94716041700008\n",
+      "Time to generate circuit for GSEE: 7.179100066423416e-05 seconds\n",
+      "   Time to decompose high level <class 'cirq.ops.common_gates.HPowGate circuit: 0.00017420900985598564 seconds \n",
+      "   Time to transform decomposed <class 'cirq.ops.common_gates.HPowGate circuit to Clifford+T: 0.00020945898722857237 seconds\n",
+      "   Time to decompose high level <class 'cirq.ops.identity.IdentityGate circuit: 2.4416978703811765e-05 seconds \n",
+      "   Time to transform decomposed <class 'cirq.ops.identity.IdentityGate circuit to Clifford+T: 5.54099096916616e-06 seconds\n",
+      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit: 0.00010366700007580221 seconds \n",
+      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit to Clifford+T: 0.00010866599041037261 seconds\n",
+      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit: 0.24183270902722143 seconds \n",
+      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit to Clifford+T: 1.0117513749864884 seconds\n",
+      "   Time to decompose high level <class 'cirq.ops.measurement_gate.MeasurementGate circuit: 0.0007868749962653965 seconds \n",
+      "   Time to transform decomposed <class 'cirq.ops.measurement_gate.MeasurementGate circuit to Clifford+T: 7.495898171328008e-05 seconds\n",
+      "Time to estimate Current Limit: 2.188670125004137\n",
       "Estimating Ideal\n",
-      "Time to generate circuit for GSEE: 0.0014647080001850554 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.common_gates.HPowGate circuit: 9.979199990084453e-05 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.common_gates.HPowGate circuit to Clifford+T: 0.00020383399987622397 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.identity.IdentityGate circuit: 1.0458000133439782e-05 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.identity.IdentityGate circuit to Clifford+T: 3.2499999633728294e-06 seconds\n",
-      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit: 6.733300006089848e-05 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit to Clifford+T: 7.354099989242968e-05 seconds\n",
-      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit: 4516.392732292 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit to Clifford+T: 892.0450802919995 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.measurement_gate.MeasurementGate circuit: 2.3361285830005727 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.measurement_gate.MeasurementGate circuit to Clifford+T: 0.00018487499983166344 seconds\n",
-      "Time to estimate Ideal: 6145.624935334\n"
+      "Time to generate circuit for GSEE: 4.7291978262364864e-05 seconds\n",
+      "   Time to decompose high level <class 'cirq.ops.common_gates.HPowGate circuit: 7.916599861346185e-05 seconds \n",
+      "   Time to transform decomposed <class 'cirq.ops.common_gates.HPowGate circuit to Clifford+T: 9.008299093693495e-05 seconds\n",
+      "   Time to decompose high level <class 'cirq.ops.identity.IdentityGate circuit: 1.0749994544312358e-05 seconds \n",
+      "   Time to transform decomposed <class 'cirq.ops.identity.IdentityGate circuit to Clifford+T: 3.374996595084667e-06 seconds\n",
+      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit: 7.287500193342566e-05 seconds \n",
+      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit to Clifford+T: 4.095799522474408e-05 seconds\n",
+      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit: 0.27323129199794494 seconds \n",
+      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit to Clifford+T: 0.7680455000081565 seconds\n",
+      "   Time to decompose high level <class 'cirq.ops.measurement_gate.MeasurementGate circuit: 0.0010527079866733402 seconds \n",
+      "   Time to transform decomposed <class 'cirq.ops.measurement_gate.MeasurementGate circuit to Clifford+T: 5.0291011575609446e-05 seconds\n",
+      "Time to estimate Ideal: 2.1309333750104997\n"
      ]
     }
    ],
@@ -555,6 +560,7 @@
     "    init_state=init_state_current_limit,\n",
     "    precision_order=1,\n",
     "    bits_precision=bits_precision_current_limit,\n",
+    "    phase_offset=phase_offset_current_limit,\n",
     "    circuit_name='two_band_current_limit',\n",
     "    metadata=metadata_current_limit,\n",
     "    write_circuits=True\n",
@@ -571,6 +577,7 @@
     "    init_state=init_state_ideal,\n",
     "    precision_order=1,\n",
     "    bits_precision=bits_precision_ideal,\n",
+    "    phase_offset=phase_offset_ideal,\n",
     "    circuit_name='two_band_ideal',\n",
     "    metadata=metadata_ideal,\n",
     "    write_circuits=True\n",
@@ -588,6 +595,519 @@
     "A similar operation, though acting on 4 qubits rather than 2 qubits, can be achieved in a similarly low constant circuit depth for the case where $i \\neq j$."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Three Band"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lastly, we will demonstrate the same procedure for the three-band Fermi-Hubbard model, seen [here](http://sces.phys.utk.edu/publications/Pub2011/7-FOP-11107-ED.pdf). The Hamiltonian on a 2-d lattice is given by the sum of the following three Hamiltonians.  The first Hamiltonian, representing the hopping terms for the xz and yz orbitals, is the same as the two band Hamiltonian above:\n",
+    "\n",
+    "\\begin{equation}\n",
+    "\\begin{split}\n",
+    "    H_{xz,yz} & = -t_1 \\sum_{i,\\sigma} \\left( d^{\\dagger}_{i,xz,\\sigma}d_{i+\\hat{y},xz,\\sigma} +  d^{\\dagger}_{i,yz,\\sigma}d_{i+\\hat{x}, yz, \\sigma} + \\text{h.c.} \\right) \\\\\n",
+    "           & -t_2 \\sum_{i,\\sigma} \\left( d^{\\dagger}_{i,xz,\\sigma}d_{i+\\hat{x},xz,\\sigma} +  d^{\\dagger}_{i,yz,\\sigma}d_{i+\\hat{y}, yz, \\sigma} + \\text{h.c.} \\right) \\\\\n",
+    "           & -t_3 \\sum_{i,\\hat{\\mu}, \\hat{\\nu},\\sigma} \\left( d^{\\dagger}_{i,xz,\\sigma}d_{i+\\hat{\\mu} + \\hat{\\nu},xz,\\sigma} +  d^{\\dagger}_{i,yz,\\sigma}d_{i+\\hat{\\mu} + \\hat{\\nu}, yz, \\sigma} + \\text{h.c.} \\right) \\\\\n",
+    "           & +t_4 \\sum_{i,\\sigma} \\left( d^{\\dagger}_{i,xz,\\sigma}d_{i+\\hat{x}+\\hat{y},yz,\\sigma} +  d^{\\dagger}_{i,yz,\\sigma}d_{i+\\hat{x}+\\hat{y},xz,\\sigma} + \\text{h.c.} \\right) \\\\\n",
+    "           & -t_4 \\sum_{i,\\sigma} \\left( d^{\\dagger}_{i,xz,\\sigma}d_{i+\\hat{x}-\\hat{y},yz,\\sigma} +  d^{\\dagger}_{i,yz,\\sigma}d_{i+\\hat{x}-\\hat{y},xz,\\sigma} + \\text{h.c.} \\right) \\\\\n",
+    "           & -\\mu \\sum_{i} \\left( n_{i}^{xz} + n_{i}^{yz} \\right)\n",
+    "\\end{split}\n",
+    "\\end{equation}\n",
+    "\n",
+    "The second Hamiltonian reflects the intra-orbital hopping terms for the $xy$ orbital is given by\n",
+    "\\begin{equation}\n",
+    "\\begin{split} H_{xy} &= t_5 \\sum_{i,\\hat{\\mu}, \\sigma} \\left( d^{\\dagger}_{i,xy,\\sigma} d_{i+\\hat{\\mu},xy,\\sigma} + \\text{h.c.} \\right) \\\\\n",
+    "           & -t_6 \\sum_{i,\\hat{\\mu},\\hat{\\nu},\\sigma} \\left( d^{\\dagger}_{i,xy,\\sigma} d_{i+\\hat{\\mu}+\\hat{\\nu},xy,\\sigma}  + \\text{h.c.}\\right) \\\\\n",
+    "           & + \\Delta_{xy} \\sum_{i} n_{i,xy} - \\mu \\sum_{i} n_{i,xy}\n",
+    "\\end{split}\n",
+    "\\end{equation}\n",
+    "The term $\\Delta_{xy}$ represents the difference between the energy of the $xy$ and $xz$ or $yz$ orbitals.  Recall from the two band example above that the term $\\hat{\\mu}$ and $\\hat{\\nu}$ represent either the $\\hat{x}$ or $\\hat{y}$ unit vectors.  Therefore the summations over $\\hat{\\mu}$ represent nearest neighbor couplings.  \n",
+    "\n",
+    "Finally, the third represents the hybridization terms between the $xy$ terms and the $xz$ or $yz$ terms.\n",
+    "\\begin{equation}\n",
+    "\\begin{split}\n",
+    "    H_{\\text{xz,yz;xy}} &= -t_7 \\sum_{i,\\sigma} \\left[ \\left(-1\\right)^{|{\\bf{i}}|}d^{\\dagger}_{{i},xz,\\sigma} d_{{i} + \\hat{x}, xy, \\sigma} + \\text{h.c.} \\right]\n",
+    "                           -t_7 \\sum_{i,\\sigma} \\left[ \\left(-1\\right)^{|{\\bf{i}}|}d^{\\dagger}_{{i},xy,\\sigma} d_{{i} + \\hat{x}, xz, \\sigma} + \\text{h.c.} \\right] \\\\\n",
+    "                        &  -t_7 \\sum_{i,\\sigma} \\left[ \\left(-1\\right)^{|{\\bf{i}}|}d^{\\dagger}_{{i},yz,\\sigma} d_{{i} + \\hat{y}, xy, \\sigma} + \\text{h.c.} \\right]\n",
+    "                           -t_7 \\sum_{i,\\sigma} \\left[ \\left(-1\\right)^{|{\\bf{i}}|}d^{\\dagger}_{{i},xy,\\sigma} d_{{i} + \\hat{y}, yz, \\sigma} + \\text{h.c.} \\right] \\\\\n",
+    "                        &  -t_8 \\sum_{i,\\sigma} \\left[ \\left(-1\\right)^{|{\\bf{i}}|}d^{\\dagger}_{{i},xz,\\sigma} d_{{i} + \\hat{x} + \\hat{y}, xy, \\sigma} + \\text{h.c.} \\right] \n",
+    "                           +t_8 \\sum_{i,\\sigma} \\left[ \\left(-1\\right)^{|{\\bf{i}}|}d^{\\dagger}_{{i},xy,\\sigma} d_{{i} + \\hat{x} + \\hat{y}, xz, \\sigma} + \\text{h.c.} \\right] \\\\\n",
+    "                        &  -t_8 \\sum_{i,\\sigma} \\left[ \\left(-1\\right)^{|{\\bf{i}}|}d^{\\dagger}_{{i},xz,\\sigma} d_{{i} + \\hat{x} - \\hat{y}, xy, \\sigma} + \\text{h.c.} \\right] \n",
+    "                           +t_8 \\sum_{i,\\sigma} \\left[ \\left(-1\\right)^{|{\\bf{i}}|}d^{\\dagger}_{{i},xy,\\sigma} d_{{i} + \\hat{x} - \\hat{y}, xz, \\sigma} + \\text{h.c.} \\right] \\\\\n",
+    "                        &  -t_8 \\sum_{i,\\sigma} \\left[ \\left(-1\\right)^{|{\\bf{i}}|}d^{\\dagger}_{{i},yz,\\sigma} d_{{i} + \\hat{x} + \\hat{y}, xy, \\sigma} + \\text{h.c.} \\right] \n",
+    "                           +t_8 \\sum_{i,\\sigma} \\left[ \\left(-1\\right)^{|{\\bf{i}}|}d^{\\dagger}_{{i},xy,\\sigma} d_{{i} + \\hat{x} + \\hat{y}, yz, \\sigma} + \\text{h.c.} \\right] \\\\\n",
+    "                        &  +t_8 \\sum_{i,\\sigma} \\left[ \\left(-1\\right)^{|{\\bf{i}}|}d^{\\dagger}_{{i},yz,\\sigma} d_{{i} + \\hat{x} - \\hat{y}, xy, \\sigma} + \\text{h.c.} \\right] \n",
+    "                           -t_8 \\sum_{i,\\sigma} \\left[ \\left(-1\\right)^{|{\\bf{i}}|}d^{\\dagger}_{{i},xy,\\sigma} d_{{i} + \\hat{x} - \\hat{y}, yz, \\sigma} + \\text{h.c.} \\right] \\\\\n",
+    "\\end{split}\n",
+    "\\end{equation}\n",
+    "\n",
+    "The terms $(-1)^{|\\text{i}|}$ represent are site dependent parity terms that cause the sign of the interaction to depend on the site.  This comes from the two-iron unit cell of the FeAs planes in the material that these Hamiltonians are derived for.\n",
+    "\n",
+    "The coefficients of the various terms are as follows:\n",
+    "$t_1 = 0.02$\n",
+    ", $t_2 = 0.06$\n",
+    ", $t_3 = 0.03$\n",
+    ", $t_4 = -0.01$\n",
+    ", $t_5 = 0.2$\n",
+    ", $t_6 = 0.3$\n",
+    ", $t_7 = -0.2$\n",
+    ", $t_8 = -t_7/2$\n",
+    ", $\\Delta_{xy} = 0.4$\n",
+    "\n",
+    "The value $\\mu$ is swept and will be taken to be a representative value of 1 in this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g_example = generate_three_orbital_nx(2,2)\n",
+    "pos = get_node_attributes(g_example, 'pos')\n",
+    "edge_labels = dict([((n1, n2), d['label']) for n1, n2, d in g_example.edges(data=True)]);\n",
+    "draw(g_example, pos)\n",
+    "draw_networkx_edge_labels(g_example,pos, edge_labels = edge_labels);\n",
+    "\n",
+    "print(g_example.edges(data=True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1.0 [0^ 0] +\n",
+       "-0.02 [0^ 4] +\n",
+       "-0.06 [0^ 8] +\n",
+       "-0.03 [0^ 12] +\n",
+       "-0.01 [0^ 14] +\n",
+       "0.2 [0^ 20] +\n",
+       "-0.1 [0^ 22] +\n",
+       "-1.0 [1^ 1] +\n",
+       "-0.02 [1^ 5] +\n",
+       "-0.06 [1^ 9] +\n",
+       "-0.03 [1^ 13] +\n",
+       "-0.01 [1^ 15] +\n",
+       "0.2 [1^ 21] +\n",
+       "-0.1 [1^ 23] +\n",
+       "-1.0 [2^ 2] +\n",
+       "-0.06 [2^ 6] +\n",
+       "-0.02 [2^ 10] +\n",
+       "-0.01 [2^ 12] +\n",
+       "-0.03 [2^ 14] +\n",
+       "0.2 [2^ 18] +\n",
+       "-0.1 [2^ 22] +\n",
+       "-1.0 [3^ 3] +\n",
+       "-0.06 [3^ 7] +\n",
+       "-0.02 [3^ 11] +\n",
+       "-0.01 [3^ 13] +\n",
+       "-0.03 [3^ 15] +\n",
+       "0.2 [3^ 19] +\n",
+       "-0.1 [3^ 23] +\n",
+       "-0.02 [4^ 0] +\n",
+       "-1.0 [4^ 4] +\n",
+       "-0.03 [4^ 8] +\n",
+       "0.01 [4^ 10] +\n",
+       "-0.06 [4^ 12] +\n",
+       "0.1 [4^ 20] +\n",
+       "-0.2 [4^ 22] +\n",
+       "-0.02 [5^ 1] +\n",
+       "-1.0 [5^ 5] +\n",
+       "-0.03 [5^ 9] +\n",
+       "0.01 [5^ 11] +\n",
+       "-0.06 [5^ 13] +\n",
+       "0.1 [5^ 21] +\n",
+       "-0.2 [5^ 23] +\n",
+       "-0.06 [6^ 2] +\n",
+       "-1.0 [6^ 6] +\n",
+       "0.01 [6^ 8] +\n",
+       "-0.03 [6^ 10] +\n",
+       "-0.02 [6^ 14] +\n",
+       "0.2 [6^ 16] +\n",
+       "-0.1 [6^ 20] +\n",
+       "-0.06 [7^ 3] +\n",
+       "-1.0 [7^ 7] +\n",
+       "0.01 [7^ 9] +\n",
+       "-0.03 [7^ 11] +\n",
+       "-0.02 [7^ 15] +\n",
+       "0.2 [7^ 17] +\n",
+       "-0.1 [7^ 21] +\n",
+       "-0.06 [8^ 0] +\n",
+       "-0.03 [8^ 4] +\n",
+       "0.01 [8^ 6] +\n",
+       "-1.0 [8^ 8] +\n",
+       "-0.02 [8^ 12] +\n",
+       "0.2 [8^ 16] +\n",
+       "-0.1 [8^ 18] +\n",
+       "-0.06 [9^ 1] +\n",
+       "-0.03 [9^ 5] +\n",
+       "0.01 [9^ 7] +\n",
+       "-1.0 [9^ 9] +\n",
+       "-0.02 [9^ 13] +\n",
+       "0.2 [9^ 17] +\n",
+       "-0.1 [9^ 19] +\n",
+       "-0.02 [10^ 2] +\n",
+       "0.01 [10^ 4] +\n",
+       "-0.03 [10^ 6] +\n",
+       "-1.0 [10^ 10] +\n",
+       "-0.06 [10^ 14] +\n",
+       "0.1 [10^ 18] +\n",
+       "-0.2 [10^ 22] +\n",
+       "-0.02 [11^ 3] +\n",
+       "0.01 [11^ 5] +\n",
+       "-0.03 [11^ 7] +\n",
+       "-1.0 [11^ 11] +\n",
+       "-0.06 [11^ 15] +\n",
+       "0.1 [11^ 19] +\n",
+       "-0.2 [11^ 23] +\n",
+       "-0.03 [12^ 0] +\n",
+       "-0.01 [12^ 2] +\n",
+       "-0.06 [12^ 4] +\n",
+       "-0.02 [12^ 8] +\n",
+       "-1.0 [12^ 12] +\n",
+       "0.1 [12^ 16] +\n",
+       "-0.2 [12^ 18] +\n",
+       "-0.03 [13^ 1] +\n",
+       "-0.01 [13^ 3] +\n",
+       "-0.06 [13^ 5] +\n",
+       "-0.02 [13^ 9] +\n",
+       "-1.0 [13^ 13] +\n",
+       "0.1 [13^ 17] +\n",
+       "-0.2 [13^ 19] +\n",
+       "-0.01 [14^ 0] +\n",
+       "-0.03 [14^ 2] +\n",
+       "-0.02 [14^ 6] +\n",
+       "-0.06 [14^ 10] +\n",
+       "-1.0 [14^ 14] +\n",
+       "0.1 [14^ 16] +\n",
+       "-0.2 [14^ 20] +\n",
+       "-0.01 [15^ 1] +\n",
+       "-0.03 [15^ 3] +\n",
+       "-0.02 [15^ 7] +\n",
+       "-0.06 [15^ 11] +\n",
+       "-1.0 [15^ 15] +\n",
+       "0.1 [15^ 17] +\n",
+       "-0.2 [15^ 21] +\n",
+       "0.2 [16^ 6] +\n",
+       "0.2 [16^ 8] +\n",
+       "0.1 [16^ 12] +\n",
+       "0.1 [16^ 14] +\n",
+       "-0.6 [16^ 16] +\n",
+       "0.2 [16^ 18] +\n",
+       "0.2 [16^ 20] +\n",
+       "-0.3 [16^ 22] +\n",
+       "0.2 [17^ 7] +\n",
+       "0.2 [17^ 9] +\n",
+       "0.1 [17^ 13] +\n",
+       "0.1 [17^ 15] +\n",
+       "-0.6 [17^ 17] +\n",
+       "0.2 [17^ 19] +\n",
+       "0.2 [17^ 21] +\n",
+       "-0.3 [17^ 23] +\n",
+       "0.2 [18^ 2] +\n",
+       "-0.1 [18^ 8] +\n",
+       "0.1 [18^ 10] +\n",
+       "-0.2 [18^ 12] +\n",
+       "0.2 [18^ 16] +\n",
+       "-0.6 [18^ 18] +\n",
+       "-0.3 [18^ 20] +\n",
+       "0.2 [18^ 22] +\n",
+       "0.2 [19^ 3] +\n",
+       "-0.1 [19^ 9] +\n",
+       "0.1 [19^ 11] +\n",
+       "-0.2 [19^ 13] +\n",
+       "0.2 [19^ 17] +\n",
+       "-0.6 [19^ 19] +\n",
+       "-0.3 [19^ 21] +\n",
+       "0.2 [19^ 23] +\n",
+       "0.2 [20^ 0] +\n",
+       "0.1 [20^ 4] +\n",
+       "-0.1 [20^ 6] +\n",
+       "-0.2 [20^ 14] +\n",
+       "0.2 [20^ 16] +\n",
+       "-0.3 [20^ 18] +\n",
+       "-0.6 [20^ 20] +\n",
+       "0.2 [20^ 22] +\n",
+       "0.2 [21^ 1] +\n",
+       "0.1 [21^ 5] +\n",
+       "-0.1 [21^ 7] +\n",
+       "-0.2 [21^ 15] +\n",
+       "0.2 [21^ 17] +\n",
+       "-0.3 [21^ 19] +\n",
+       "-0.6 [21^ 21] +\n",
+       "0.2 [21^ 23] +\n",
+       "-0.1 [22^ 0] +\n",
+       "-0.1 [22^ 2] +\n",
+       "-0.2 [22^ 4] +\n",
+       "-0.2 [22^ 10] +\n",
+       "-0.3 [22^ 16] +\n",
+       "0.2 [22^ 18] +\n",
+       "0.2 [22^ 20] +\n",
+       "-0.6 [22^ 22] +\n",
+       "-0.1 [23^ 1] +\n",
+       "-0.1 [23^ 3] +\n",
+       "-0.2 [23^ 5] +\n",
+       "-0.2 [23^ 11] +\n",
+       "-0.3 [23^ 17] +\n",
+       "0.2 [23^ 19] +\n",
+       "0.2 [23^ 21] +\n",
+       "-0.6 [23^ 23]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t1 = 0.02\n",
+    "t2 = 0.06\n",
+    "t3 = 0.03\n",
+    "t4 = -0.01\n",
+    "t5 = 0.2\n",
+    "t6 = 0.3\n",
+    "t7 = -0.2\n",
+    "t8 = -t7/2\n",
+    "mu = 1\n",
+    "delta = 0.4\n",
+    "nx_to_three_orbital_hamiltonian(g_example, t1, t2, t3, t4, t5, t6, t7, t8, mu, delta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "g_current_limit = generate_three_orbital_nx(6,7)\n",
+    "g_ideal = generate_three_orbital_nx(20,20)\n",
+    "\n",
+    "##### START UNCOMMENT FOR TESTING\n",
+    "n_test = 2\n",
+    "g_current_limit = generate_three_orbital_nx(n_test,n_test)  \n",
+    "g_ideal = generate_three_orbital_nx(n_test,n_test)\n",
+    "##### END UNCOMMENT FOR TESTING\n",
+    "n_qubits_current_limit = len(g_current_limit)\n",
+    "n_qubits_ideal = len(g_ideal)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have the Hamiltonians for both the model which constitutes the current limit of classical solvers, and the ideal capability of a solver, we can perform resource estimation for the Hamiltonians.  As with the single qubit model, we need to get a decent state initialization using Hartree-Fock.  As above, this has not been implemented at this time, but should have low depth in the quantum circuit since Hartree-Fock outputs a product state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#TODO: Incorporate Hartree-Fock into this section to prepare the initial state for QPE for GSEE.\n",
+    "#This should provide a low depth initialization circuit relative to the depth of the QPE, while giving access to a low-energy subspace"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Assuming that we have the output from the Hartree-Fock simulation, we may now perform QPE as above.  Currently we are using a short evolution time and a second order trotterization with a single step.  We will use scaling arguments to determine the final resources since generating the full circuit for a large number of trotter steps with many bits of precision is quite costly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ham_current_limit = nx_to_three_orbital_hamiltonian(g_current_limit,t1,t2,t3,t4,t5,t6,t7,t8,mu,delta)\n",
+    "ham_ideal = nx_to_three_orbital_hamiltonian(g_ideal,t1,t2,t3,t4,t5,t6,t7,t8,mu,delta)\n",
+    "trotter_order_current_limit = 2\n",
+    "trotter_steps_current_limit = 1 #Using one trotter step for a strict lower bound with this method\n",
+    "\n",
+    "trotter_order_ideal = 2\n",
+    "trotter_steps_ideal = 1 #Using one trotter step for a strict lower bound with this method\n",
+    "\n",
+    "bits_precision_ideal = 16\n",
+    "bits_precision_current_limit = 16\n",
+    "\n",
+    "E_min_ideal = -len(ham_ideal.terms)\n",
+    "E_max_ideal = 0\n",
+    "omega_ideal = E_max_ideal-E_min_ideal\n",
+    "t_ideal = 2*np.pi/omega_ideal\n",
+    "phase_offset_ideal = E_max_ideal*t_ideal\n",
+    "\n",
+    "E_min_current_limit = -len(ham_current_limit.terms)\n",
+    "E_max_current_limit = 0\n",
+    "omega_current_limit = E_max_current_limit-E_min_current_limit\n",
+    "t_current_limit = 2*np.pi/omega_current_limit\n",
+    "phase_offset_current_limit = E_max_current_limit*t_current_limit\n",
+    "\n",
+    "init_state_ideal = [0] * n_qubits_ideal\n",
+    "init_state_current_limit = [0] * n_qubits_current_limit\n",
+    "\n",
+    "current_limit_args = {\n",
+    "    'trotterize' : True,\n",
+    "    'mol_ham'    : ham_current_limit,\n",
+    "    'ev_time'    : t_current_limit,\n",
+    "    'trot_ord'   : trotter_order_current_limit,\n",
+    "    'trot_num'   : 1\n",
+    "}\n",
+    "\n",
+    "ideal_args = {\n",
+    "    'trotterize' : True,\n",
+    "    'mol_ham'    : ham_ideal,\n",
+    "    'ev_time'    : t_ideal,\n",
+    "    'trot_ord'   : trotter_order_ideal,\n",
+    "    'trot_num'   : 1\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "metadata_current_limit = EstimateMetaData(\n",
+    "    id=3000,\n",
+    "    name='FermiHubbard_ideal_Current_Limit`',\n",
+    "    category='scientific',\n",
+    "    size=f'{6}x{7}',\n",
+    "    task='Ground State Energy Estimation',\n",
+    "    implementations=f'GSEE, evolution_time={t_current_limit}, bits_precision={bits_precision_current_limit}, trotter_order={trotter_order_current_limit}',\n",
+    ")\n",
+    "\n",
+    "metadata_ideal = EstimateMetaData(\n",
+    "    id=4000,\n",
+    "    name='FermiHubbard_ideal_Ideal',\n",
+    "    category='scientific',\n",
+    "    size=f'{20}x{20}',\n",
+    "    task='Ground State Energy Estimation',\n",
+    "    implementations=f'GSEE, evolution_time={t_ideal}, bits_precision={bits_precision_ideal}, trotter_order={trotter_order_ideal}',\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Estimating Current Limit\n",
+      "Time to generate circuit for GSEE: 4.704200546257198e-05 seconds\n",
+      "   Time to decompose high level <class 'cirq.ops.common_gates.HPowGate circuit: 0.00011325001833029091 seconds \n",
+      "   Time to transform decomposed <class 'cirq.ops.common_gates.HPowGate circuit to Clifford+T: 0.00022524999803863466 seconds\n",
+      "   Time to decompose high level <class 'cirq.ops.identity.IdentityGate circuit: 1.3165990822017193e-05 seconds \n",
+      "   Time to transform decomposed <class 'cirq.ops.identity.IdentityGate circuit to Clifford+T: 3.4999975468963385e-06 seconds\n",
+      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit: 8.550001075491309e-05 seconds \n",
+      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit to Clifford+T: 9.033299284055829e-05 seconds\n",
+      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit: 0.7486400830093771 seconds \n",
+      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit to Clifford+T: 2.5677436669939198 seconds\n",
+      "   Time to decompose high level <class 'cirq.ops.measurement_gate.MeasurementGate circuit: 0.00246395799331367 seconds \n",
+      "   Time to transform decomposed <class 'cirq.ops.measurement_gate.MeasurementGate circuit to Clifford+T: 8.699999307282269e-05 seconds\n",
+      "Time to estimate Current Limit: 5.534946375002619\n",
+      "Estimating Ideal\n",
+      "Time to generate circuit for GSEE: 5.1207985961809754e-05 seconds\n",
+      "   Time to decompose high level <class 'cirq.ops.common_gates.HPowGate circuit: 9.524999768473208e-05 seconds \n",
+      "   Time to transform decomposed <class 'cirq.ops.common_gates.HPowGate circuit to Clifford+T: 0.00019350001821294427 seconds\n",
+      "   Time to decompose high level <class 'cirq.ops.identity.IdentityGate circuit: 1.2125005014240742e-05 seconds \n",
+      "   Time to transform decomposed <class 'cirq.ops.identity.IdentityGate circuit to Clifford+T: 3.583991201594472e-06 seconds\n",
+      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit: 6.925000343471766e-05 seconds \n",
+      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit to Clifford+T: 7.974999607540667e-05 seconds\n",
+      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit: 0.6040110420144629 seconds \n",
+      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit to Clifford+T: 2.368579125002725 seconds\n",
+      "   Time to decompose high level <class 'cirq.ops.measurement_gate.MeasurementGate circuit: 0.002168875013012439 seconds \n",
+      "   Time to transform decomposed <class 'cirq.ops.measurement_gate.MeasurementGate circuit to Clifford+T: 9.10409726202488e-05 seconds\n",
+      "Time to estimate Ideal: 5.23599783401005\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Estimating Current Limit')\n",
+    "t0 = time.perf_counter()\n",
+    "\n",
+    "estimate_current_limit = gsee_resource_estimation(\n",
+    "    outdir='GSE/FermiHubbard/',\n",
+    "    numsteps=trotter_steps_current_limit,\n",
+    "    gsee_args=current_limit_args,\n",
+    "    init_state=init_state_current_limit,\n",
+    "    precision_order=1,\n",
+    "    bits_precision=bits_precision_current_limit,\n",
+    "    phase_offset=phase_offset_current_limit,\n",
+    "    circuit_name='three_band_current_limit',\n",
+    "    metadata=metadata_current_limit,\n",
+    "    write_circuits=True\n",
+    ")\n",
+    "t1 = time.perf_counter()\n",
+    "print(f'Time to estimate Current Limit: {t1-t0}')\n",
+    "\n",
+    "print('Estimating Ideal')\n",
+    "t0 = time.perf_counter()\n",
+    "estimate_ideal = gsee_resource_estimation(\n",
+    "    outdir='GSE/FermiHubbard/',\n",
+    "    numsteps=trotter_steps_ideal,\n",
+    "    gsee_args=ideal_args,\n",
+    "    init_state=init_state_ideal,\n",
+    "    precision_order=1,\n",
+    "    bits_precision=bits_precision_ideal,\n",
+    "    phase_offset=phase_offset_ideal,\n",
+    "    circuit_name='three_band_ideal',\n",
+    "    metadata=metadata_ideal,\n",
+    "    write_circuits=True\n",
+    ")\n",
+    "t1 = time.perf_counter()\n",
+    "print(f'Time to estimate Ideal: {t1-t0}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After Ground State Energy Estimation has been performed, we need to measure the autocorrelation, $\\frac{1}{2}\\langle \\Delta_{i,j} + \\Delta_{i,j}^{\\dagger}\\rangle$ where $\\Delta_{i,j} = \\langle c_{i+}c_{j-} - c_{i-}c_{j+} \\rangle$.  As above, for the case where $i = j = 0$, this can be performed in the following way.  One can rotate into the Pauli basis using the following transformation, where for simplicity we assume that $i = j = 0$ is in the middle of the lattice.  We will label the qubit representing site $i+$ as qubit 1 and the qubit representing site $i-$ as qubit 2.  Given $U = CNOT_{i+,i-} H_{i+} CNOT_{i+,i-}$, it can be shown that $U^\\dagger (c_{i+}c_{i-} - c_{i-}c_{i+}) U = -2 |\\uparrow \\uparrow \\rangle \\langle \\uparrow \\uparrow | + 2 |\\downarrow \\downarrow \\rangle \\langle \\downarrow \\downarrow|$.  This means that by simply applying the circuit $U$, then measuring, we can observe the autocorrelation.  This introduces 3 Clifford operations and no T gates.\n",
+    "\n",
+    "A similar operation, though acting on 4 qubits rather than 2 qubits, can be achieved in a similarly low constant circuit depth for the case where $i \\neq j$."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -598,7 +1118,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "qca",
    "language": "python",
    "name": "python3"
   },
diff --git a/notebooks/pdfs/HighTemperatureSuperConductorExample.pdf b/notebooks/pdfs/HighTemperatureSuperConductorExample.pdf
index 0b57bfe..cb0f9a3 100644
Binary files a/notebooks/pdfs/HighTemperatureSuperConductorExample.pdf and b/notebooks/pdfs/HighTemperatureSuperConductorExample.pdf differ
diff --git a/scripts/HTSC-one-band-RE.py b/scripts/HTSC-one-band-RE.py
index 57065d7..982ba02 100644
--- a/scripts/HTSC-one-band-RE.py
+++ b/scripts/HTSC-one-band-RE.py
@@ -1,4 +1,5 @@
 #!/usr/bin/env python
+
 import os
 import argparse
 import time
@@ -7,6 +8,7 @@
 import math
 from pyLIQTR.PhaseEstimation.pe import PhaseEstimation
 from networkx import get_node_attributes, draw, draw_networkx_edge_labels
+from qca.utils.algo_utils import gsee_resource_estimation
 from qca.utils.utils import circuit_estimate, EstimateMetaData
 from qca.utils.hamiltonian_utils import generate_two_orbital_nx, nx_to_two_orbital_hamiltonian
 
@@ -38,7 +40,7 @@ def main(args):
     t = 2*np.pi/omega
     phase_offset = E_max*t
 
-    args = {
+    gsee_args = {
         'trotterize' : True,
         'mol_ham'    : ham,
         'ev_time'    : t,
@@ -56,33 +58,23 @@ def main(args):
         category='scientific',
         size=f'{lattice_size}x{lattice_size}',
         task='Ground State Energy Estimation',
+        value_per_circuit=value,
+        repetitions_per_application=repetitions,
         implementations=f'GSEE, evolution_time={t}, bits_precision={bits_precision}, trotter_order={trotter_order}',
     )
 
-    t0 = time.perf_counter()
-    gse_inst = PhaseEstimation(
-        precision_order=1, #actual precision bits accounted as scaling factors in the resource estimate
-        init_state=init_state,
-        phase_offset=phase_offset,
-        include_classical_bits=False, # Do this so print to openqasm works
-        kwargs=args)
-    gse_inst.generate_circuit()
-    t1 = time.perf_counter()
-    print(f'One band GSEE time to generate high level Circuit: {t1 - t0}')
-
-    gse_circuit = gse_inst.pe_circuit
-
     print('Estimating one_band')
     t0 = time.perf_counter()
-    estimate = circuit_estimate(
-        circuit=gse_circuit,
-        metadata = metadata,
-        outdir=directory,
-        circuit_name=name,
-        write_circuits=circuit_write,
-        bits_precision=bits_precision,
-        numsteps=trotter_steps
-    )
+    estimate = gsee_resource_estimation(
+            outdir=directory,
+            numsteps=trotter_steps,
+            gsee_args=gsee_args,
+            init_state=init_state,
+            precision_order=1,
+            bits_precision=bits_precision,
+            circuit_name=name,
+            metadata=metadata,
+            write_circuits=args.circuit_write)
     t1 = time.perf_counter()
     print(f'Time to estimate one_band: {t1-t0}')
     return estimate
diff --git a/scripts/HTSC-three-band-RE.py b/scripts/HTSC-three-band-RE.py
new file mode 100644
index 0000000..8b47c11
--- /dev/null
+++ b/scripts/HTSC-three-band-RE.py
@@ -0,0 +1,121 @@
+#!/usr/bin/env python
+
+import os
+import argparse
+import time
+import openfermion as of
+import numpy as np
+import math
+from pyLIQTR.PhaseEstimation.pe import PhaseEstimation
+from networkx import get_node_attributes, draw, draw_networkx_edge_labels
+from qca.utils.algo_utils import gsee_resource_estimation
+from qca.utils.utils import circuit_estimate, EstimateMetaData
+from qca.utils.hamiltonian_utils import generate_three_orbital_nx, nx_to_three_orbital_hamiltonian
+
+## Three band
+
+def main(args):
+    t1 = args.param_t1
+    t2 = args.param_t2
+    t3 = args.param_t3
+    t4 = args.param_t4
+    t5 = args.param_t5
+    t6 = args.param_t6
+    t7 = args.param_t7
+    t8 = args.param_t8
+    mu = args.param_mu
+    delta = args.param_delta
+
+    trotter_steps = args.trotter_steps
+    trotter_order = args.trotter_order
+
+    lattice_size = args.lattice_size
+
+    value = args.value
+    repetitions = args.repetitions
+    directory = args.directory
+    name = args.name
+
+    bits_precision = estimate_bits_precision(args.error_precision)
+    g = generate_three_orbital_nx(lattice_size,lattice_size)
+    n_qubits = len(g)
+
+    ham = nx_to_three_orbital_hamiltonian(g,t1,t2,t3,t4,t5,t6,t7,t8,mu,delta)
+
+    E_min = -len(ham.terms) * max(abs(t1), abs(t2), abs(t3), abs(t4), abs(mu))
+    E_max = 0
+    omega = E_max-E_min
+    t = 2*np.pi/omega
+    phase_offset = E_max*t
+
+    init_state = [0] * n_qubits
+
+    gsee_args = {
+        'trotterize' : True,
+        'mol_ham'    : ham,
+        'ev_time'    : t,
+        'trot_ord'   : trotter_order,
+        'trot_num'   : 1 #Accounted for in a scaling argument later
+    }
+
+
+    print('starting')
+
+    metadata = EstimateMetaData(
+        id=time.time_ns(),
+        name=name,
+        category='scientific',
+        size=f'{lattice_size}x{lattice_size}',
+        task='Ground State Energy Estimation',
+        value_per_circuit=value,
+        repetitions_per_application=repetitions,
+        implementations=f'GSEE, evolution_time={t}, bits_precision={bits_precision}, trotter_order={trotter_order}',
+    )
+
+    print('Estimating Circuit Resources')
+    t0 = time.perf_counter()
+    estimate = gsee_resource_estimation(
+            outdir=directory,
+            numsteps=trotter_steps,
+            gsee_args=gsee_args,
+            init_state=init_state,
+            precision_order=1,
+            phase_offset=phase_offset,
+            bits_precision=bits_precision,
+            circuit_name=name,
+            metadata=metadata,
+            write_circuits=args.circuit_write)
+    t1 = time.perf_counter()
+    return estimate
+
+def estimate_bits_precision(epsilon):
+    return math.ceil(math.log2(1.0/epsilon))
+
+def parse_arguments():
+    parser = argparse.ArgumentParser(prog='HTSC-three-band-RE')
+    parser.add_argument('-l', '--lattice-size', type=int, default=10)
+    parser.add_argument('-e', '--error-precision', type=float, default=1e-5)
+    parser.add_argument('-t', '--trotter-steps', type=int, default=1)
+    parser.add_argument('-to', '--trotter-order', type=int, default=2)
+
+    parser.add_argument('-t1', '--param-t1', type=float, default=0.02)
+    parser.add_argument('-t2', '--param-t2', type=float, default=0.06)
+    parser.add_argument('-t3', '--param-t3', type=float, default=0.03)
+    parser.add_argument('-t4', '--param-t4', type=float, default=-0.01)
+    parser.add_argument('-t5', '--param-t5', type=float, default=0.2)
+    parser.add_argument('-t6', '--param-t6', type=float, default=0.3)
+    parser.add_argument('-t7', '--param-t7', type=float, default=-0.2)
+    parser.add_argument('-t8', '--param-t8', type=float, default=0.1)
+    parser.add_argument('-mu', '--param-mu', type=float, default=1)
+    parser.add_argument('-delta', '--param-delta', type=float, default=0.4)
+
+    parser.add_argument('-n', '--name', type=str, default=f'FermiHubbard-ThreeBand', help='name of this circuit instance, becomes prefix for output file')
+    parser.add_argument('-d', '--directory', type=str, default='./', help='output file directory')
+    parser.add_argument('-v', '--value', type=float, default=0, help='value of the total application')
+    parser.add_argument('-r', '--repetitions', type=int, default=1, help='repetitions needed to achieve value of computatation (not runs of this script)')
+    parser.add_argument('-c', '--circuit_write', default=False, action='store_true')
+    return parser
+
+if __name__ == "__main__":
+    parser = parse_arguments()
+    main(parser.parse_args())
diff --git a/scripts/HTSC-two-band-RE.py b/scripts/HTSC-two-band-RE.py
index f4999fe..728bfc9 100644
--- a/scripts/HTSC-two-band-RE.py
+++ b/scripts/HTSC-two-band-RE.py
@@ -8,6 +8,7 @@
 import math
 from pyLIQTR.PhaseEstimation.pe import PhaseEstimation
 from networkx import get_node_attributes, draw, draw_networkx_edge_labels
+from qca.utils.algo_utils import gsee_resource_estimation
 from qca.utils.utils import circuit_estimate, EstimateMetaData
 from qca.utils.hamiltonian_utils import generate_two_orbital_nx, nx_to_two_orbital_hamiltonian
 
@@ -44,7 +45,7 @@ def main(args):
 
     init_state = [0] * n_qubits
 
-    pe_args = {
+    gsee_args = {
         'trotterize' : True,
         'mol_ham'    : ham,
         'ev_time'    : t,
@@ -61,32 +62,23 @@ def main(args):
         category='scientific',
         size=f'{lattice_size}x{lattice_size}',
         task='Ground State Energy Estimation',
+        value_per_circuit=value,
+        repetitions_per_application=repetitions,
         implementations=f'GSEE, evolution_time={t}, bits_precision={bits_precision}, trotter_order={trotter_order}',
     )
 
-    t0 = time.perf_counter()
-    gse_inst = PhaseEstimation(
-        precision_order=1,
-        init_state=init_state,
-        phase_offset=phase_offset,
-        include_classical_bits=False, # Do this so print to openqasm works
-        kwargs=pe_args)
-    gse_inst.generate_circuit()
-    t1 = time.perf_counter()
-    print(f'time to generate high level: {t1 - t0}')
-
-    gse_circuit = gse_inst.pe_circuit
-
     print('Estimating Circuit Resources')
     t0 = time.perf_counter()
-    estimate = circuit_estimate(
-                     circuit=gse_circuit,
-                     metadata = metadata,
-                     outdir=directory,
-                     circuit_name=name,
-                     write_circuits=args.circuit_write,
-                     bits_precision=bits_precision,
-                     numsteps=trotter_steps)
+    estimate = gsee_resource_estimation(
+            outdir=directory,
+            numsteps=trotter_steps,
+            gsee_args=gsee_args,
+            init_state=init_state,
+            precision_order=1,
+            bits_precision=bits_precision,
+            circuit_name=name,
+            metadata=metadata,
+            write_circuits=args.circuit_write)
     t1 = time.perf_counter()
     return estimate
 
@@ -96,14 +88,16 @@ def estimate_bits_precision(epsilon):
 def parse_arguments():
     parser = argparse.ArgumentParser(prog='HTSC-two-band-RE')
     parser.add_argument('-l', '--lattice-size', type=int, default=10)
-    parser.add_argument('-e', '--error-precision', type=float, default=1e-3)
+    parser.add_argument('-e', '--error-precision', type=float, default=1e-5)
     parser.add_argument('-t', '--trotter-steps', type=int, default=1)
     parser.add_argument('-to', '--trotter-order', type=int, default=2)
+
     parser.add_argument('-t1', '--param-t1', type=float, default=-1)
     parser.add_argument('-t2', '--param-t2', type=float, default=1.3)
     parser.add_argument('-t3', '--param-t3', type=float, default=0.85)
     parser.add_argument('-t4', '--param-t4', type=float, default=0.85)
     parser.add_argument('-mu', '--param-mu', type=float, default=1)
+
     parser.add_argument('-n', '--name', type=str, default=f'FermiHubbard-TwoBand', help='name of this circuit instance, becomes prefix for output file')
     parser.add_argument('-d', '--directory', type=str, default='./', help='output file directory')
     parser.add_argument('-v', '--value', type=float, default=0, help='value of the total application')
diff --git a/src/qca/utils/algo_utils.py b/src/qca/utils/algo_utils.py
index e9715d4..8011993 100644
--- a/src/qca/utils/algo_utils.py
+++ b/src/qca/utils/algo_utils.py
@@ -173,6 +173,7 @@ def gsee_resource_estimation(
         init_state:list,
         precision_order:int,
         bits_precision:int,
+        phase_offset:float,
         circuit_name:str='Hamiltonian',
         metadata:EstimateMetaData=None,
         include_classical_bits:bool=False,
@@ -183,6 +184,7 @@ def gsee_resource_estimation(
         precision_order=precision_order,
         init_state=init_state,
         include_classical_bits=include_classical_bits,
+        phase_offset=phase_offset,
         kwargs=gsee_args
     )
     t1 = time.perf_counter()
diff --git a/src/qca/utils/hamiltonian_utils.py b/src/qca/utils/hamiltonian_utils.py
index 9fa5e0b..ee2796c 100644
--- a/src/qca/utils/hamiltonian_utils.py
+++ b/src/qca/utils/hamiltonian_utils.py
@@ -1,6 +1,6 @@
 import random
 from pyLIQTR.utils import Hamiltonian
-from networkx import relabel_nodes, Graph, grid_graph
+from networkx import relabel_nodes, Graph, MultiGraph, grid_graph, compose
 from networkx.generators.lattice import grid_2d_graph
 from openfermion import FermionOperator, QubitOperator
 
@@ -15,9 +15,9 @@ def flatten_nx_graph(graph: Graph) -> Graph:
     return new_graph
 
 
-def generate_two_orbital_nx(Lx: int, Ly: int) -> Graph:
+def generate_two_orbital_nx(Lx: int, Ly: int) -> MultiGraph:
     # can combine logic between loops if this is slow
-    g = Graph()
+    g = MultiGraph()
     for m in range(Lx):
         for n in range(Ly):
             for a in range(2):
@@ -59,20 +59,107 @@ def generate_two_orbital_nx(Lx: int, Ly: int) -> Graph:
                 if n4 in g:
                     g.add_edge(n3, n4, label="-t3")
 
-                n1, n2 = (m, n, 0, s), (m + 1, n, 0, s)
+
+                # +t_4 terms
+                n1, n2 = (m, n, 0, s), (m + 1, n + 1, 1, s)
+                n3, n4 = (m, n, 1, s), (m + 1, n + 1, 0, s)
+                if n2 in g:
+                    g.add_edge(n1, n2, label="+t4")
+                if n4 in g:
+                    g.add_edge(n3, n4, label="+t4")
+
+                # -t4 terms
+                n1, n2 = (m, n, 0, s), (m + 1, n - 1, 1, s)
+                n3, n4 = (m, n, 1, s), (m + 1, n - 1, 0, s)
+                if n2 in g:
+                    g.add_edge(n1, n2, label="-t4")
+                if n4 in g:
+                    g.add_edge(n3, n4, label="-t4")
+    return g
+
+
+def nx_to_two_orbital_hamiltonian(
+        graph: MultiGraph,
+        t1: float,
+        t2: float,
+        t3: float,
+        t4: float,
+        mu: float) -> FermionOperator:
+    g_flat = flatten_nx_graph(graph)
+    H = FermionOperator()
+
+    # generating hopping terms on each edge
+    for i, j, d in g_flat.edges(data=True):
+        w = 0
+        label = d['label']
+        if label == "-t1":
+            w = -t1
+        elif label == "-t2":
+            w = -t2
+        elif label == "-t3":
+            w = -t3
+        elif label == "-t4":
+            w = -t4
+        elif label == "+t4":
+            w = t4
+        else:
+            raise ValueError("Graph improperly labeled")
+
+        H += FermionOperator(((i, 1), (j, 0)), w)
+        H += FermionOperator(((j, 1), (i, 0)), w)
+
+    # applying number operator to each qubit
+    for i in g_flat.nodes:
+        H += FermionOperator(((i, 1), (i, 0)), -mu)
+
+    return H
+
+def generate_xz_yz_hamiltonian_nx(Lx: int, Ly: int) -> Graph:
+    #xz is given orbital label 0, yz is given orbital label 1
+    g = MultiGraph()
+    for m in range(Lx):
+        for n in range(Ly):
+            for a in range(2):
+                for s in range(2):
+                    g.add_node((m, n, a, s), pos=(
+                        m + a * (Lx + 1), n + s * (Ly + 1)),
+                              delta=False)
+
+    for m in range(Lx):
+        for n in range(Ly):
+            for s in range(2):
+                # t_1 terms
+                n1, n2 = (m, n, 0, s), (m, n + 1, 0, s)
                 n3, n4 = (m, n, 1, s), (m + 1, n, 1, s)
+                if n2 in g:
+                    g.add_edge(n1, n2, label="-t1")
+                if n4 in g:
+                    g.add_edge(n3, n4, label="-t1")
+
+                # t_2 terms
+                n1, n2 = (m, n, 0, s), (m + 1, n, 0, s)
+                n3, n4 = (m, n, 1, s), (m, n + 1, 1, s)
+                if n2 in g:
+                    g.add_edge(n1, n2, label="-t2")
+                if n4 in g:
+                    g.add_edge(n3, n4, label="-t2")
+
+                # t_3 terms
+                n1, n2 = (m, n, 0, s), (m + 1, n + 1, 0, s)
+                n3, n4 = (m, n, 1, s), (m + 1, n + 1, 1, s)
                 if n2 in g:
                     g.add_edge(n1, n2, label="-t3")
                 if n4 in g:
                     g.add_edge(n3, n4, label="-t3")
 
-                n1, n2 = (m, n, 0, s), (m, n + 1, 0, s)
-                n3, n4 = (m, n, 1, s), (m, n + 1, 1, s)
+                n1, n2 = (m, n, 0, s), (m + 1, n - 1, 0, s)
+                n3, n4 = (m, n, 1, s), (m + 1, n - 1, 1, s)
                 if n2 in g:
                     g.add_edge(n1, n2, label="-t3")
                 if n4 in g:
                     g.add_edge(n3, n4, label="-t3")
 
+
                 # +t_4 terms
                 n1, n2 = (m, n, 0, s), (m + 1, n + 1, 1, s)
                 n3, n4 = (m, n, 1, s), (m + 1, n + 1, 0, s)
@@ -90,14 +177,168 @@ def generate_two_orbital_nx(Lx: int, Ly: int) -> Graph:
                     g.add_edge(n3, n4, label="-t4")
     return g
 
+def generate_xy_hamiltonian_nx(Lx: int, Ly: int) -> Graph:
+    #xy is given orbital label 2
+    g = MultiGraph()
+    for m in range(Lx):
+        for n in range(Ly):
+            for a in [2]:
+                for s in range(2):
+                    #Adding nodes to graph to make edge checking easier
+                    #Also adding node positions for drawing functionality
+                    g.add_node((m, n, a, s), pos=(
+                        m + a * (Lx + 1), n + s * (Ly + 1)),
+                              delta=True)
+
+    for m in range(Lx):
+        for n in range(Ly):
+            for s in range(2):
+                #t_5 terms
+                n1, n2 = (m, n, 2, s), (m, n + 1, 2, s)
+                n3, n4 = (m, n, 2, s), (m + 1, n, 2, s)
+                if n2 in g:
+                    g.add_edge(n1, n2, label="+t5")
+                if n4 in g:
+                    g.add_edge(n3, n4, label="+t5")
 
-def nx_to_two_orbital_hamiltonian(
-        graph: Graph,
+                #t_6 terms
+                n1, n2 = (m, n, 2, s), (m + 1, n + 1, 2, s)
+                if n2 in g:
+                    g.add_edge(n1, n2, label="-t6")
+
+                n1, n2 = (m, n, 2, s), (m + 1, n - 1, 2, s)
+                if n2 in g:
+                    g.add_edge(n1, n2, label="-t6")
+    return g
+
+def generate_xz_yz_xy_hamiltonian_nx(Lx: int, Ly: int) -> Graph:
+    #xz is orbital label 0
+    #yz is orbital label 1
+    #xy is orbital label 2
+    g = MultiGraph()
+    for m in range(Lx):
+        for n in range(Ly):
+            for a in range(3):
+                for s in range(2):
+                    #Adding nodes to graph to make edge checking easier
+                    #Also adding node positions for drawing functionality
+                    g.add_node((m, n, a, s), pos=(
+                        m + a * (Lx + 1), n + s * (Ly + 1)),
+                              delta=(a==2))
+
+    for m in range(Lx):
+        for n in range(Ly):
+            for s in range(2):
+                #t_7 terms
+                n1, n2 = (m, n, 0, s), (m + 1, n, 2, s)
+                if n2 in g:
+                    if (m + n) % 2 == 0:
+                        g.add_edge(n1, n2, label="-t7")
+                    else:
+                        g.add_edge(n1, n2, label="+t7")
+
+                n1, n2 = (m, n, 2, s), (m + 1, n, 0, s)
+                if n2 in g:
+                    if (m + n) % 2 == 0:
+                        g.add_edge(n1, n2, label="-t7")
+                    else:
+                        g.add_edge(n1, n2, label="+t7")
+
+                n1, n2 = (m, n, 1, s), (m, n + 1, 2, s)
+                if n2 in g:
+                    if (m + n) % 2 == 0:
+                        g.add_edge(n1, n2, label="-t7")
+                    else:
+                        g.add_edge(n1, n2, label="+t7")
+
+                n1, n2 = (m, n, 2, s), (m, n + 1, 1, s)
+                if n2 in g:
+                    if (m + n) % 2 == 0:
+                        g.add_edge(n1, n2, label="-t7")
+                    else:
+                        g.add_edge(n1, n2, label="+t7")
+
+                #t_8 terms
+                n1, n2 = (m, n, 0, s), (m + 1, n + 1, 2, s)
+                if n2 in g:
+                    if (m + n) % 2 == 0:
+                        g.add_edge(n1, n2, label="-t8")
+                    else:
+                        g.add_edge(n1, n2, label="+t8")
+
+                n1, n2 = (m, n, 2, s), (m + 1, n + 1, 0, s)
+                if n2 in g:
+                    if (m + n) % 2 == 0:
+                        g.add_edge(n1, n2, label="+t8")
+                    else:
+                        g.add_edge(n1, n2, label="-t8")
+
+                n1, n2 = (m, n, 0, s), (m + 1, n - 1, 2, s)
+                if n2 in g:
+                    if (m + n) % 2 == 0:
+                        g.add_edge(n1, n2, label="-t8")
+                    else:
+                        g.add_edge(n1, n2, label="+t8")
+
+                n1, n2 = (m, n, 2, s), (m + 1, n - 1, 0, s)
+                if n2 in g:
+                    if (m + n) % 2 == 0:
+                        g.add_edge(n1, n2, label="+t8")
+                    else:
+                        g.add_edge(n1, n2, label="-t8")
+
+                n1, n2 = (m, n, 1, s), (m + 1, n + 1, 2, s)
+                if n2 in g:
+                    if (m + n) % 2 == 0:
+                        g.add_edge(n1, n2, label="-t8")
+                    else:
+                        g.add_edge(n1, n2, label="+t8")
+
+                n1, n2 = (m, n, 2, s), (m + 1, n + 1, 1, s)
+                if n2 in g:
+                    if (m + n) % 2 == 0:
+                        g.add_edge(n1, n2, label="+t8")
+                    else:
+                        g.add_edge(n1, n2, label="-t8")
+
+                n1, n2 = (m, n, 1, s), (m + 1, n - 1, 2, s)
+                if n2 in g:
+                    if (m + n) % 2 == 0:
+                        g.add_edge(n1, n2, label="+t8")
+                    else:
+                        g.add_edge(n1, n2, label="-t8")
+
+                n1, n2 = (m, n, 2, s), (m + 1, n - 1, 1, s)
+                if n2 in g:
+                    if (m + n) % 2 == 0:
+                        g.add_edge(n1, n2, label="-t8")
+                    else:
+                        g.add_edge(n1, n2, label="+t8")
+    return g
+
+def generate_three_orbital_nx(Lx: int, Ly: int) -> MultiGraph:
+    # can combine logic between loops if this is slow
+    g_xz_yz = generate_xz_yz_hamiltonian_nx(Lx, Ly)
+    g_xy = generate_xy_hamiltonian_nx(Lx, Ly)
+    g_xz_yz_xy = generate_xz_yz_xy_hamiltonian_nx(Lx, Ly)
+
+    g_three_band = compose(g_xz_yz, g_xy)
+    g_three_band = compose(g_three_band, g_xz_yz_xy)
+    return g_three_band
+
+def nx_to_three_orbital_hamiltonian(
+        graph: MultiGraph,
         t1: float,
         t2: float,
         t3: float,
         t4: float,
-        mu: float) -> FermionOperator:
+        t5: float,
+        t6: float,
+        t7: float,
+        t8: float,
+        mu: float,
+        delta: float) -> FermionOperator:
+
     g_flat = flatten_nx_graph(graph)
     H = FermionOperator()
 
@@ -115,6 +356,18 @@ def nx_to_two_orbital_hamiltonian(
             w = -t4
         elif label == "+t4":
             w = t4
+        elif label == "+t5":
+            w = t5
+        elif label == "-t6":
+            w = -t6
+        elif label == "+t7":
+            w = t7
+        elif label == "-t7":
+            w = -t7
+        elif label == "+t8":
+            w = t8
+        elif label == "-t8":
+            w = -t8
         else:
             raise ValueError("Graph improperly labeled")
 
@@ -122,8 +375,10 @@ def nx_to_two_orbital_hamiltonian(
         H += FermionOperator(((j, 1), (i, 0)), w)
 
     # applying number operator to each qubit
-    for i in g_flat.nodes:
+    for i,d in g_flat.nodes(data=True):
         H += FermionOperator(((i, 1), (i, 0)), -mu)
+        if d['delta']:
+            H += FermionOperator(((i, 1), (i, 0)), delta)
 
     return H
 
diff --git a/tests/hamiltonian_utils_test.py b/tests/hamiltonian_utils_test.py
index 3e46679..5e2c4b3 100644
--- a/tests/hamiltonian_utils_test.py
+++ b/tests/hamiltonian_utils_test.py
@@ -17,11 +17,13 @@ def test_triangle_hamiltonian_generation(self):
         graph_triangle = nx_triangle_lattice(lattice_size=2)
         assign_directional_triangular_labels(graph_triangle, 2)
         edge_labels = dict([((n1, n2), d['label']) for n1, n2, d in graph_triangle.edges(data=True)])
-        correct_labels = {((0,0), (1,0)): 'Z',
-                          ((0,0), (0,1)): 'X',
-                          ((0,0), (1, 1)): 'Y',
-                          ((0, 1), (1, 1)): 'Z',
-                          ((1, 0), (1, 1)): 'X'}
+        correct_labels = {
+            ((0,0), (1,0)): 'Z',
+            ((0,0), (0,1)): 'X',
+            ((0,0), (1, 1)): 'Y',
+            ((0, 1), (1, 1)): 'Z',
+            ((1, 0), (1, 1)): 'X'
+        }
         for edge, label in edge_labels.items():
             self.assertTrue(edge in correct_labels.keys())
             self.assertEqual(label, correct_labels[edge])
@@ -30,12 +32,14 @@ def test_hexagonal_labels(self):
         hexagon_graph = hexagonal_lattice_graph(1,1)
         assign_hexagon_labels(hexagon_graph, 'X', 'Y', 'Z')
         edge_labels = dict([((n1, n2), d['label']) for n1, n2, d in hexagon_graph.edges(data=True)])
-        correct_labels = {((0, 0), (0,1)): 'Y',
-                          ((0, 0), (1, 0)): 'Z',
-                          ((0, 1), (0, 2)): 'X',
-                          ((0, 2), (1, 2)): 'Z',
-                          ((1, 0), (1, 1)): 'X',
-                          ((1, 1), (1, 2)): 'Y'}
+        correct_labels = {
+            ((0, 0), (0,1)): 'Y',
+            ((0, 0), (1, 0)): 'Z',
+            ((0, 1), (0, 2)): 'X',
+            ((0, 2), (1, 2)): 'Z',
+            ((1, 0), (1, 1)): 'X',
+            ((1, 1), (1, 2)): 'Y'
+        }
         for edge, label in edge_labels.items():
             self.assertTrue(edge in correct_labels.keys())
             self.assertEqual(label, correct_labels[edge])
@@ -72,38 +76,40 @@ def test_two_orbital_gen_test(self):
         two_orbital = generate_two_orbital_nx(2, 2)
         edge_labels = dict([((n1, n2), d['label'])
                            for n1, n2, d in two_orbital.edges(data=True)])
-        correct_labels = {((0, 0, 0, 0), (0, 1, 0, 0)): '-t3',
-                          ((0, 0, 0, 0), (1, 0, 0, 0)): '-t3',
-                          ((0, 0, 0, 0), (1, 1, 0, 0)): '-t3',
-                          ((0, 0, 0, 0), (1, 1, 1, 0)): '+t4',
-                          ((0, 0, 0, 1), (0, 1, 0, 1)): '-t3',
-                          ((0, 0, 0, 1), (1, 0, 0, 1)): '-t3',
-                          ((0, 0, 0, 1), (1, 1, 0, 1)): '-t3',
-                          ((0, 0, 0, 1), (1, 1, 1, 1)): '+t4',
-                          ((0, 0, 1, 0), (1, 0, 1, 0)): '-t3',
-                          ((0, 0, 1, 0), (0, 1, 1, 0)): '-t3',
-                          ((0, 0, 1, 0), (1, 1, 1, 0)): '-t3',
-                          ((0, 0, 1, 0), (1, 1, 0, 0)): '+t4',
-                          ((0, 0, 1, 1), (1, 0, 1, 1)): '-t3',
-                          ((0, 0, 1, 1), (0, 1, 1, 1)): '-t3',
-                          ((0, 0, 1, 1), (1, 1, 1, 1)): '-t3',
-                          ((0, 0, 1, 1), (1, 1, 0, 1)): '+t4',
-                          ((0, 1, 0, 0), (1, 1, 0, 0)): '-t3',
-                          ((0, 1, 0, 0), (1, 0, 0, 0)): '-t3',
-                          ((0, 1, 0, 0), (1, 0, 1, 0)): '-t4',
-                          ((0, 1, 0, 1), (1, 1, 0, 1)): '-t3',
-                          ((0, 1, 0, 1), (1, 0, 0, 1)): '-t3',
-                          ((0, 1, 0, 1), (1, 0, 1, 1)): '-t4',
-                          ((0, 1, 1, 0), (1, 1, 1, 0)): '-t3',
-                          ((0, 1, 1, 0), (1, 0, 1, 0)): '-t3',
-                          ((0, 1, 1, 0), (1, 0, 0, 0)): '-t4',
-                          ((0, 1, 1, 1), (1, 1, 1, 1)): '-t3',
-                          ((0, 1, 1, 1), (1, 0, 1, 1)): '-t3',
-                          ((0, 1, 1, 1), (1, 0, 0, 1)): '-t4',
-                          ((1, 0, 0, 0), (1, 1, 0, 0)): '-t3',
-                          ((1, 0, 0, 1), (1, 1, 0, 1)): '-t3',
-                          ((1, 0, 1, 0), (1, 1, 1, 0)): '-t3',
-                          ((1, 0, 1, 1), (1, 1, 1, 1)): '-t3'}
+        correct_labels = {
+            ((0, 0, 0, 0), (0, 1, 0, 0)): '-t1',
+            ((0, 0, 0, 0), (1, 0, 0, 0)): '-t2',
+            ((0, 0, 0, 0), (1, 1, 0, 0)): '-t3',
+            ((0, 0, 0, 0), (1, 1, 1, 0)): '+t4',
+            ((0, 0, 0, 1), (0, 1, 0, 1)): '-t1',
+            ((0, 0, 0, 1), (1, 0, 0, 1)): '-t2',
+            ((0, 0, 0, 1), (1, 1, 0, 1)): '-t3',
+            ((0, 0, 0, 1), (1, 1, 1, 1)): '+t4',
+            ((0, 0, 1, 0), (1, 0, 1, 0)): '-t1',
+            ((0, 0, 1, 0), (0, 1, 1, 0)): '-t2',
+            ((0, 0, 1, 0), (1, 1, 1, 0)): '-t3',
+            ((0, 0, 1, 0), (1, 1, 0, 0)): '+t4',
+            ((0, 0, 1, 1), (1, 0, 1, 1)): '-t1',
+            ((0, 0, 1, 1), (0, 1, 1, 1)): '-t2',
+            ((0, 0, 1, 1), (1, 1, 1, 1)): '-t3',
+            ((0, 0, 1, 1), (1, 1, 0, 1)): '+t4',
+            ((0, 1, 0, 0), (1, 1, 0, 0)): '-t2',
+            ((0, 1, 0, 0), (1, 0, 0, 0)): '-t3',
+            ((0, 1, 0, 0), (1, 0, 1, 0)): '-t4',
+            ((0, 1, 0, 1), (1, 1, 0, 1)): '-t2',
+            ((0, 1, 0, 1), (1, 0, 0, 1)): '-t3',
+            ((0, 1, 0, 1), (1, 0, 1, 1)): '-t4',
+            ((0, 1, 1, 0), (1, 1, 1, 0)): '-t1',
+            ((0, 1, 1, 0), (1, 0, 1, 0)): '-t3',
+            ((0, 1, 1, 0), (1, 0, 0, 0)): '-t4',
+            ((0, 1, 1, 1), (1, 1, 1, 1)): '-t1',
+            ((0, 1, 1, 1), (1, 0, 1, 1)): '-t3',
+            ((0, 1, 1, 1), (1, 0, 0, 1)): '-t4',
+            ((1, 0, 0, 0), (1, 1, 0, 0)): '-t1',
+            ((1, 0, 0, 1), (1, 1, 0, 1)): '-t1',
+            ((1, 0, 1, 0), (1, 1, 1, 0)): '-t2',
+            ((1, 0, 1, 1), (1, 1, 1, 1)): '-t2'
+        }
         for edge, label in edge_labels.items():
             self.assertTrue(edge in correct_labels.keys())
             self.assertEqual(label, correct_labels[edge])
@@ -118,87 +124,88 @@ def test_nx_to_two_orbital_hamiltonian(self):
             0.85,
             1
         )
-        edge_labels = dict([((n1, n2), d['label']) for n1, n2, d in two_orbital.edges(data=True)]);
-        correct_fermionic_terms = {((0, 1), (4, 0)): -0.85,
-                                    ((4, 1), (0, 0)): -0.85,
-                                    ((0, 1), (8, 0)): -0.85,
-                                    ((8, 1), (0, 0)): -0.85,
-                                    ((0, 1), (12, 0)): -0.85,
-                                    ((12, 1), (0, 0)): -0.85,
-                                    ((0, 1), (14, 0)): 0.85,
-                                    ((14, 1), (0, 0)): 0.85,
-                                    ((1, 1), (5, 0)): -0.85,
-                                    ((5, 1), (1, 0)): -0.85,
-                                    ((1, 1), (9, 0)): -0.85,
-                                    ((9, 1), (1, 0)): -0.85,
-                                    ((1, 1), (13, 0)): -0.85,
-                                    ((13, 1), (1, 0)): -0.85,
-                                    ((1, 1), (15, 0)): 0.85,
-                                    ((15, 1), (1, 0)): 0.85, 
-                                    ((2, 1), (10, 0)): -0.85,
-                                    ((10, 1), (2, 0)): -0.85,
-                                    ((2, 1), (6, 0)): -0.85,
-                                    ((6, 1), (2, 0)): -0.85,
-                                    ((2, 1), (14, 0)): -0.85,
-                                    ((14, 1), (2, 0)): -0.85,
-                                    ((2, 1), (12, 0)): 0.85,
-                                    ((12, 1), (2, 0)): 0.85,
-                                    ((3, 1), (11, 0)): -0.85,
-                                    ((11, 1), (3, 0)): -0.85,
-                                    ((3, 1), (7, 0)): -0.85,
-                                    ((7, 1), (3, 0)): -0.85,
-                                    ((3, 1), (15, 0)): -0.85,
-                                    ((15, 1), (3, 0)): -0.85,
-                                    ((3, 1), (13, 0)): 0.85,
-                                    ((13, 1), (3, 0)): 0.85,
-                                    ((4, 1), (12, 0)): -0.85,
-                                    ((12, 1), (4, 0)): -0.85,
-                                    ((4, 1), (8, 0)): -0.85,
-                                    ((8, 1), (4, 0)): -0.85,
-                                    ((4, 1), (10, 0)): -0.85,
-                                    ((10, 1), (4, 0)): -0.85,
-                                    ((5, 1), (13, 0)): -0.85,
-                                    ((13, 1), (5, 0)): -0.85,
-                                    ((5, 1), (9, 0)): -0.85,
-                                    ((9, 1), (5, 0)): -0.85,
-                                    ((5, 1), (11, 0)): -0.85,
-                                    ((11, 1), (5, 0)): -0.85,
-                                    ((6, 1), (14, 0)): -0.85,
-                                    ((14, 1), (6, 0)): -0.85,
-                                    ((6, 1), (10, 0)): -0.85,
-                                    ((10, 1), (6, 0)): -0.85,
-                                    ((6, 1), (8, 0)): -0.85,
-                                    ((8, 1), (6, 0)): -0.85,
-                                    ((7, 1), (15, 0)): -0.85,
-                                    ((15, 1), (7, 0)): -0.85,
-                                    ((7, 1), (11, 0)): -0.85,
-                                    ((11, 1), (7, 0)): -0.85,
-                                    ((7, 1), (9, 0)): -0.85,
-                                    ((9, 1), (7, 0)): -0.85,
-                                    ((8, 1), (12, 0)): -0.85,
-                                    ((12, 1), (8, 0)): -0.85,
-                                    ((9, 1), (13, 0)): -0.85,
-                                    ((13, 1), (9, 0)): -0.85,
-                                    ((10, 1), (14, 0)): -0.85,
-                                    ((14, 1), (10, 0)): -0.85,
-                                    ((11, 1), (15, 0)): -0.85,
-                                    ((15, 1), (11, 0)): -0.85,
-                                    ((0, 1), (0, 0)): -1.0,
-                                    ((1, 1), (1, 0)): -1.0,
-                                    ((2, 1), (2, 0)): -1.0,
-                                    ((3, 1), (3, 0)): -1.0,
-                                    ((4, 1), (4, 0)): -1.0,
-                                    ((5, 1), (5, 0)): -1.0,
-                                    ((6, 1), (6, 0)): -1.0,
-                                    ((7, 1), (7, 0)): -1.0,
-                                    ((8, 1), (8, 0)): -1.0,
-                                    ((9, 1), (9, 0)): -1.0,
-                                    ((10, 1), (10, 0)): -1.0,
-                                    ((11, 1), (11, 0)): -1.0,
-                                    ((12, 1), (12, 0)): -1.0,
-                                    ((13, 1), (13, 0)): -1.0,
-                                    ((14, 1), (14, 0)): -1.0,
-                                    ((15, 1), (15, 0)): -1.0}
+        correct_fermionic_terms = {
+            ((0, 1), (4, 0)): 1.0,
+            ((4, 1), (0, 0)): 1.0,
+            ((0, 1), (8, 0)): -1.3,
+            ((8, 1), (0, 0)): -1.3,
+            ((0, 1), (12, 0)): -0.85,
+            ((12, 1), (0, 0)): -0.85,
+            ((0, 1), (14, 0)): 0.85,
+            ((14, 1), (0, 0)): 0.85,
+            ((1, 1), (5, 0)): 1.0,
+            ((5, 1), (1, 0)): 1.0,
+            ((1, 1), (9, 0)): -1.3,
+            ((9, 1), (1, 0)): -1.3,
+            ((1, 1), (13, 0)): -0.85,
+            ((13, 1), (1, 0)): -0.85,
+            ((1, 1), (15, 0)): 0.85,
+            ((15, 1), (1, 0)): 0.85,
+            ((2, 1), (10, 0)): 1.0,
+            ((10, 1), (2, 0)): 1.0,
+            ((2, 1), (6, 0)): -1.3,
+            ((6, 1), (2, 0)): -1.3,
+            ((2, 1), (14, 0)): -0.85,
+            ((14, 1), (2, 0)): -0.85,
+            ((2, 1), (12, 0)): 0.85,
+            ((12, 1), (2, 0)): 0.85,
+            ((3, 1), (11, 0)): 1.0,
+            ((11, 1), (3, 0)): 1.0,
+            ((3, 1), (7, 0)): -1.3,
+            ((7, 1), (3, 0)): -1.3,
+            ((3, 1), (15, 0)): -0.85,
+            ((15, 1), (3, 0)): -0.85,
+            ((3, 1), (13, 0)): 0.85,
+            ((13, 1), (3, 0)): 0.85,
+            ((4, 1), (12, 0)): -1.3,
+            ((12, 1), (4, 0)): -1.3,
+            ((4, 1), (8, 0)): -0.85,
+            ((8, 1), (4, 0)): -0.85,
+            ((4, 1), (10, 0)): -0.85,
+            ((10, 1), (4, 0)): -0.85,
+            ((5, 1), (13, 0)): -1.3,
+            ((13, 1), (5, 0)): -1.3,
+            ((5, 1), (9, 0)): -0.85,
+            ((9, 1), (5, 0)): -0.85,
+            ((5, 1), (11, 0)): -0.85,
+            ((11, 1), (5, 0)): -0.85,
+            ((6, 1), (14, 0)): 1.0,
+            ((14, 1), (6, 0)): 1.0,
+            ((6, 1), (10, 0)): -0.85,
+            ((10, 1), (6, 0)): -0.85,
+            ((6, 1), (8, 0)): -0.85,
+            ((8, 1), (6, 0)): -0.85,
+            ((7, 1), (15, 0)): 1.0,
+            ((15, 1), (7, 0)): 1.0,
+            ((7, 1), (11, 0)): -0.85,
+            ((11, 1), (7, 0)): -0.85,
+            ((7, 1), (9, 0)): -0.85,
+            ((9, 1), (7, 0)): -0.85,
+            ((8, 1), (12, 0)): 1.0,
+            ((12, 1), (8, 0)): 1.0,
+            ((9, 1), (13, 0)): 1.0,
+            ((13, 1), (9, 0)): 1.0,
+            ((10, 1), (14, 0)): -1.3,
+            ((14, 1), (10, 0)): -1.3,
+            ((11, 1), (15, 0)): -1.3,
+            ((15, 1), (11, 0)): -1.3,
+            ((0, 1), (0, 0)): -1.0,
+            ((1, 1), (1, 0)): -1.0,
+            ((2, 1), (2, 0)): -1.0,
+            ((3, 1), (3, 0)): -1.0,
+            ((4, 1), (4, 0)): -1.0,
+            ((5, 1), (5, 0)): -1.0,
+            ((6, 1), (6, 0)): -1.0,
+            ((7, 1), (7, 0)): -1.0,
+            ((8, 1), (8, 0)): -1.0,
+            ((9, 1), (9, 0)): -1.0,
+            ((10, 1), (10, 0)): -1.0,
+            ((11, 1), (11, 0)): -1.0,
+            ((12, 1), (12, 0)): -1.0,
+            ((13, 1), (13, 0)): -1.0,
+            ((14, 1), (14, 0)): -1.0,
+            ((15, 1), (15, 0)): -1.0
+        }
         for edge, label in correct_fermionic_terms.items():
             self.assertTrue(edge in fermionic_two_orbital.terms)
             self.assertEqual(label, fermionic_two_orbital.terms[edge])