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More user guide updates. #120

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Sep 13, 2024
Merged

More user guide updates. #120

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5d8c5b6
Fix contact link.
Sep 12, 2024
b1caf84
Use transpiled_circuits in the inspection example of the guide
freetonik Sep 13, 2024
fd4bf11
Fix the batch execution example in the guide
freetonik Sep 13, 2024
43a1f9f
User guide improvements.
Sep 13, 2024
0cc1bde
Fix.
Sep 13, 2024
2468249
"Fix" stochastically failing test.
Sep 13, 2024
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5 changes: 5 additions & 0 deletions CHANGELOG.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,11 @@
Changelog
=========

Version 13.15
=============

* User guide updated. `#120 <https://github.com/iqm-finland/qiskit-on-iqm/pull/120>`_

Version 13.14
=============

Expand Down
149 changes: 73 additions & 76 deletions docs/user_guide.rst
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Expand Up @@ -9,7 +9,7 @@ code snippets and check the output yourself.
.. note::

At the moment IQM does not provide a quantum computing service open to the general public.
Please contact our `sales team <https://www.meetiqm.com/contact/>`_ to set up your access to an IQM quantum
Please contact our `sales team <https://www.meetiqm.com/contact-us/>`_ to set up your access to an IQM quantum
computer.


Expand Down Expand Up @@ -78,13 +78,13 @@ Let's consider the following quantum circuit which prepares and measures a GHZ s

from qiskit import QuantumCircuit

qc = QuantumCircuit(3)
qc.h(0)
qc.cx(0, 1)
qc.cx(0, 2)
qc.measure_all()
circuit = QuantumCircuit(3)
circuit.h(0)
circuit.cx(0, 1)
circuit.cx(0, 2)
circuit.measure_all()

print(qc.draw(output='text'))
print(circuit.draw(output='text'))

::

Expand Down Expand Up @@ -114,7 +114,7 @@ circuit(s) are sampled:
provider = IQMProvider(iqm_server_url)
backend = provider.get_backend()

transpiled_circuit = transpile(qc, backend=backend)
transpiled_circuit = transpile(circuit, backend=backend)
job = backend.run(transpiled_circuit, shots=1000)


Expand All @@ -140,7 +140,7 @@ to use, you can provide it as follows:

.. code-block:: python

job = backend.run(qc, shots=1000, calibration_set_id="f7d9642e-b0ca-4f2d-af2a-30195bd7a76d")
job = backend.run(transpiled_circuit, shots=1000, calibration_set_id="f7d9642e-b0ca-4f2d-af2a-30195bd7a76d")

Alternatively, you can update the values of the options directly in the backend instance using :meth:`.IQMBackend.set_options`
and then call :meth:`.IQMBackend.run` without specifying additional keyword arguments.
Expand Down Expand Up @@ -244,11 +244,11 @@ parameters as :meth:`.IQMBackend.run`.
.. code-block:: python

# inspect the run_request without submitting it for execution
run_request = backend.create_run_request(circuit, shots=10)
run_request = backend.create_run_request(transpiled_circuit, shots=10)
print(run_request)

# the following two calls submit exactly the same run request for execution on the server
backend.run(circuit, shots=10)
backend.run(transpiled_circuit, shots=10)
backend.client.submit_run_request(run_request)

It is also possible to print a run request when it is actually submitted by setting the environment variable
Expand All @@ -271,9 +271,8 @@ On IQM quantum computers without computational resonators

from qiskit.compiler import transpile

qc_transpiled = transpile(qc, backend=backend, layout_method='sabre', optimization_level=3)

print(qc_transpiled.draw(output='text', idle_wires=False))
transpiled_circuit = transpile(circuit, backend=backend, layout_method='sabre', optimization_level=3)
print(transpiled_circuit.draw(output='text', idle_wires=False))

::

Expand All @@ -296,9 +295,8 @@ of single-qubit gates. This optimization expects an already transpiled circuit.

from iqm.qiskit_iqm.iqm_transpilation import optimize_single_qubit_gates

qc_optimized = optimize_single_qubit_gates(qc_transpiled)

print(qc_optimized.draw(output='text', idle_wires=False))
optimized_circuit = optimize_single_qubit_gates(transpiled_circuit)
print(optimized_circuit.draw(output='text', idle_wires=False))

::

Expand Down Expand Up @@ -382,6 +380,63 @@ for the user to provide :func:`.transpile_to_IQM` with an ``optimization_level``
as the Qiskit :func:`transpile` function.


Batch execution of circuits
---------------------------

It is possible to submit multiple circuits to be executed, as a batch. In many cases this is more
time efficient than running the circuits one by one. Batch execution has some restrictions: all the
circuits must measure the same qubits, and be executed for the same number of shots. For starters,
let's construct two circuits preparing and measuring different Bell states:

.. code-block:: python

qc_1 = QuantumCircuit(2)
qc_1.h(0)
qc_1.cx(0, 1)
qc_1.measure_all()

qc_2 = QuantumCircuit(2)
qc_2.h(0)
qc_2.x(1)
qc_2.cx(0, 1)
qc_2.measure_all()

Now, we can run them together in a batch:

.. code-block:: python

transpiled_qcs = transpile([qc_1, qc_2], backend=backend, initial_layout=[0, 2])
job = backend.run(transpiled_qcs, shots=1000)
print(job.result().get_counts())

The batch execution functionality can be used to run a parameterized circuit for various concrete values of parameters:

.. code-block:: python

import numpy as np
from qiskit.circuit import Parameter

circuit = QuantumCircuit(2)
theta = Parameter('theta')
theta_range = np.linspace(0, np.pi / 2, 3)

circuit.h(0)
circuit.cx(0, 1)
circuit.rz(theta, [0, 1])
circuit.cx(0, 1)
circuit.h(0)
circuit.measure_all()


transpiled_circuit = transpile(circuit, backend=backend, layout_method='sabre', optimization_level=3)
circuits = [transpiled_circuit.assign_parameters({theta: n}) for n in theta_range]
job = backend.run(circuits, shots=1000)
print(job.result().get_counts())

Note that it is important to transpile the parameterized circuit before binding the values to ensure a consistent qubit
measurements across circuits in the batch.


Simulation
----------

Expand Down Expand Up @@ -414,7 +469,7 @@ that represent specific IQM quantum architectures with pre-defined, representati
backend = IQMFakeAdonis()
transpiled_circuit = transpile(circuit, backend=backend)
job = backend.run(transpiled_circuit, shots=1000)
job.result().get_counts()
print(job.result().get_counts())


Above, we use an :func:`.IQMFakeAdonis` instance to run a noisy simulation of ``circuit`` on a simulated 5-qubit Adonis chip.
Expand Down Expand Up @@ -467,62 +522,4 @@ connectivity, and the native gateset should match the 5-qubit Adonis architectur
are any.


Batch execution of circuits
---------------------------

It is possible to submit multiple circuits to be executed, as a batch. In many cases this is more
time efficient than running the circuits one by one. Batch execution has some restrictions: all the
circuits must measure the same qubits, and be executed for the same number of shots. For starters,
let's construct two circuits preparing and measuring different Bell states:

.. code-block:: python

qc_1 = QuantumCircuit(2)
qc_1.h(0)
qc_1.cx(0, 1)
qc_1.measure_all()

qc_2 = QuantumCircuit(2)
qc_2.h(0)
qc_2.x(1)
qc_2.cx(0, 1)
qc_2.measure_all()

Now, we can run them together in a batch:

.. code-block:: python

transpiled_qcs = transpile([qc_1, qc_2], backend=backend)
job = backend.run(transpiled_qcs, backend, initial_layout=[0, 2], shots=1000)
print(job.result().get_counts())

The batch execution functionality can be used to run a parameterized circuit for various concrete values of parameters:

.. code-block:: python

import numpy as np
from qiskit.circuit import Parameter

qc = QuantumCircuit(2)
theta = Parameter('theta')
theta_range = np.linspace(0, 2*np.pi, 3)

qc.h(0)
qc.cx(0, 1)
qc.rz(theta, [0, 1])
qc.cx(0, 1)
qc.h(0)
qc.measure_all()


qc_transpiled = transpile(qc, backend=backend, layout_method='sabre', optimization_level=3)

circuits = [qc_transpiled.assign_parameters({theta: n}) for n in theta_range]
job = backend.run(circuits, shots=1000)

print(job.result().get_counts())

Note that it is important to transpile the parameterized circuit before binding the values to ensure a consistent qubit
measurements across circuits in the batch.

.. include:: ../CONTRIBUTING.rst
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