fix: small modifications to layers

src/qiboml/models/abstract.py

@@ -5,6 +5,7 @@
 
 from qibo import Circuit
 from qibo.config import raise_error
+from qibo.gates import abstract
 
 from qiboml.backends import TensorflowBackend as JaxBackend
 
@@ -29,13 +30,8 @@ def __post_init__(self):
         self.circuit = Circuit(self.nqubits)
 
     @abstractmethod
-    def _feed_input(self, x):
-        pass
-
     def forward(self, x):
-        """Performs the forward pass: prepares the input and execute the circuit."""
-        self._feed_input(x)
-        return self.backend.execute_circuit(self.circuit)
+        pass
 
     @abstractmethod
     def backward(self, input_grad: "ndarray"):

src/qiboml/models/ansatze.py

@@ -5,12 +5,6 @@
 
 class ReuploadingLayer(QuantumCircuitLayer):
 
-    def _feed_input(self, x):
-        if isinstance(x, Circuit):
-            self.circuit = self.circuit + x
-        else:
-            self.initial_state = x
-
     def __post_init__(self):
         super().__post_init__()
         for q in self.qubits:
@@ -20,5 +14,8 @@ def __post_init__(self):
             self.circuit.add(gates.CNOT(q0=q, q1=self.qubits[i + 1]))
         self.circuit.add(gates.CNOT(q0=self.qubits[-1], q1=self.qubits[0]))
 
+    def forward(self, x: Circuit) -> Circuit:
+        return x + self.circuit
+
     def backward(self):
         raise_error(NotImplementedError, "TO DO")

src/qiboml/models/encoding_decoding.py

@@ -17,19 +17,22 @@ class QuantumEncodingLayer(QuantumCircuitLayer):
 @dataclass
 class BinaryEncodingLayer(QuantumEncodingLayer):
 
-    def _feed_input(self, x: "ndarray"):
+    def forward(self, x: "ndarray") -> Circuit:
+        if isinstance(x, Circuit):
+            raise_error(RuntimeError, "Passed a `Circuit` as input data.")
         if x.shape[-1] != self.nqubits:
             raise_error(
                 RuntimeError,
                 f"Invalid input dimension {x.shape[-1]}, but nqubits is {self.nqubits}.",
             )
-        self.circuit = Circuit(self.nqubits)
-        for i, bit in enumerate(x):
+        circuit = self.circuit.copy()
+        for q, bit in zip(self.qubits, x):
             if bit:
-                self.circuit.add(gates.X(i))
+                circuit.add(gates.X(q))
+        return circuit
 
-    def backward(self):
-        raise_error(NotImplementedError, "TO DO")
+    def backward(self, input_grad: "ndarray") -> "ndarray":
+        return input_grad
 
 
 class PhaseEncodingLayer(QuantumEncodingLayer):
@@ -48,31 +51,33 @@ class AmplitudeEncodingLayer(QuantumEncodingLayer):
 """
 
 
-class QuantumDecodingLayer(QuantumCircuitLayer):
-    pass
-
-
 @dataclass
-class ExpectationLayer(QuantumDecodingLayer):
+class QuantumDecodingLayer(QuantumCircuitLayer):
 
-    observable: Union["ndarray", "qibo.models.Hamiltonian"]
+    nshots: int = 1000
 
-    def _feed_input(self, x):
-        if isinstance(x, Circuit):
-            self.circuit = x
+    def __post_init__(self):
+        super().__post_init__()
+        self.circuit.add(gates.M(*self.qubits))
 
-    def forward(self, x):
-        if isinstance(x, Circuit):
-            return self.observable.expectation_from_samples(
-                super().forward(x).frequencies()
-            )
-        else:
-            return self.observable.expectation(x)
+    def forward(self, x: Circuit) -> Circuit:
+        return self.backend.execute_circuit(x + self.circuit, nshots=self.nshots)
 
     def backward(self):
         raise_error(NotImplementedError, "TO DO")
 
 
+@dataclass
+class ExpectationLayer(QuantumDecodingLayer):
+
+    observable: Union["ndarray", "qibo.models.Hamiltonian"] = None
+
+    def forward(self, x: Circuit) -> "ndarray":
+        return self.observable.expectation_from_samples(
+            super().forward(x).frequencies()
+        )
+
+
 """
    .
    .

tutorials/model_example.ipynb

@@ -0,0 +1,86 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f929e9a9-d6c3-4857-b9b5-bc4f267955f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BinaryEncodingLayer(nqubits=3, qubits=range(0, 3), circuit=<qibo.models.circuit.Circuit object at 0x795ab0567f40>, initial_state=None, backend=tensorflow)\n",
+      "ReuploadingLayer(nqubits=3, qubits=range(0, 3), circuit=<qibo.models.circuit.Circuit object at 0x795a052328f0>, initial_state=None, backend=tensorflow)\n",
+      "QuantumDecodingLayer(nqubits=3, qubits=range(0, 3), circuit=<qibo.models.circuit.Circuit object at 0x795a052335e0>, initial_state=None, backend=tensorflow, nshots=1000)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiboml.models.encoding_decoding import *\n",
+    "from qiboml.models.ansatze import *\n",
+    "\n",
+    "model = [BinaryEncodingLayer(nqubits=3), ReuploadingLayer(nqubits=3), QuantumDecodingLayer(nqubits=3)]\n",
+    "for m in model:\n",
+    "    print(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "09cccd24-dece-4279-bcd2-7006582458c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[<tf.Tensor: shape=(8,), dtype=float64, numpy=array([1., 0., 0., 0., 0., 0., 0., 0.])>, <tf.Tensor: shape=(8,), dtype=float64, numpy=array([0., 0., 1., 0., 0., 0., 0., 0.])>, <tf.Tensor: shape=(8,), dtype=float64, numpy=array([0., 0., 0., 0., 1., 0., 0., 0.])>]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "data = np.array([[0,0,0], [0,1,0], [1,1,0]])\n",
+    "result = []\n",
+    "\n",
+    "for d in data:\n",
+    "    x = model[0].forward(d)\n",
+    "    x = model[1].forward(x)\n",
+    "    result.append(model[2].forward(x).probabilities())\n",
+    "\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "005b4456-baab-486d-901a-0834ee042f54",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}