Execution of the Bell circuit
The “Hello World!” of Quantum Computing is the generation of the 2-qubit Bell state.
Creating the EPR/Bell state
Let us start with the circuit:
[1]:
from mpqp import QCircuit
from mpqp.gates import H, CNOT
[2]:
circuit = QCircuit([H(0), CNOT(0,1)], label="Bell state")
print(circuit)
┌───┐
q_0: ┤ H ├──■──
└───┘┌─┴─┐
q_1: ─────┤ X ├
└───┘
Run the circuit on a local device
We can execute a circuit on a local simulator by calling the function run and precising the device.
[3]:
from mpqp.execution import run, ATOSDevice
When no measure is added to the circuit, running the circuit will consist in extracting the state-vector at the output of the circuit.
⚠ This feature is not supported on all backends
[4]:
result = run(circuit, ATOSDevice.MYQLM_PYLINALG)
print(result)
print(result.amplitudes)
print(result.probabilities)
Result: ATOSDevice, MYQLM_PYLINALG
State vector: [0.7071068, 0, 0, 0.7071068]
Probabilities: [0.5, 0, 0, 0.5]
Number of qubits: 2
[0.70710677+0.j 0. +0.j 0. +0.j 0.70710677+0.j]
[0.5 0. 0. 0.5]
We can also add to the circuit a BasisMeasure, consisting in sample the state in a given basis. By default, the basis is the computational one.
[5]:
from mpqp.measures import BasisMeasure
We precise which qubits we can to measure by inputting a list of indices, and precising the number of shots. When shots=0, we end up in the same case as before, a statevector simulation.
[6]:
circuit.add(BasisMeasure([0,1], shots=0))
result = run(circuit, ATOSDevice.MYQLM_PYLINALG)
print(result)
Result: ATOSDevice, MYQLM_PYLINALG
State vector: [0.7071068, 0, 0, 0.7071068]
Probabilities: [0.5, 0, 0, 0.5]
Number of qubits: 2
When we precise a number of shots, the circuit will be sampled and the core of the Result will be a list of Sample. A precising the counts for each state of the basis.
[7]:
circuit = circuit.without_measurements()
circuit.add(BasisMeasure([0,1], shots=1024))
[8]:
result = run(circuit, ATOSDevice.MYQLM_PYLINALG)
print(result)
print(result.samples)
print(result.counts)
print(result.probabilities)
Result: ATOSDevice, MYQLM_PYLINALG
Counts: [505, 0, 0, 519]
Probabilities: [0.4931641, 0, 0, 0.5068359]
Samples:
State: 00, Index: 0, Count: 505, Probability: 0.4931640625
State: 11, Index: 3, Count: 519, Probability: 0.5068359375
Error: 0.015631173891374292
[Sample(2, index=0, count=505, probability=0.4931640625), Sample(2, index=3, count=519, probability=0.5068359375)]
[505, 0, 0, 519]
[0.49316406 0. 0. 0.50683594]
Run the circuit on multiple devices
By using the same function run we can execute the circuit on several simulators at the time. One just has to give a list of devices instead of a single device.
[9]:
from mpqp.execution import IBMDevice, AWSDevice, GOOGLEDevice
[10]:
results = run(circuit, [ATOSDevice.MYQLM_PYLINALG, IBMDevice.AER_SIMULATOR, AWSDevice.BRAKET_LOCAL_SIMULATOR, GOOGLEDevice.CIRQ_LOCAL_SIMULATOR])
print(results)
print('---------')
print(results[0])
c:\Users\JulienCalisto\Documents\MPQP_main_repo\mpqp\.venv\lib\site-packages\mpqp\qasm\qasm_to_braket.py:80: UnsupportedBraketFeaturesWarning:
This program uses OpenQASM language features that may not be supported on QPUs or on-demand simulators.
warnings.warn(
BatchResult: 4 results
Result: ATOSDevice, MYQLM_PYLINALG
Counts: [480, 0, 0, 544]
Probabilities: [0.46875, 0, 0, 0.53125]
Samples:
State: 00, Index: 0, Count: 480, Probability: 0.46875
State: 11, Index: 3, Count: 544, Probability: 0.53125
Error: 0.0156020726215454
Result: GOOGLEDevice, CIRQ_LOCAL_SIMULATOR
Counts: [506, 0, 0, 518]
Probabilities: [0.4941406, 0, 0, 0.5058594]
Samples:
State: 00, Index: 0, Count: 506, Probability: 0.494140625
State: 11, Index: 3, Count: 518, Probability: 0.505859375
Error: None
Result: AWSDevice, BRAKET_LOCAL_SIMULATOR
Counts: [513, 0, 0, 511]
Probabilities: [0.5009766, 0, 0, 0.4990234]
Samples:
State: 00, Index: 0, Count: 513, Probability: 0.5009765625
State: 11, Index: 3, Count: 511, Probability: 0.4990234375
Error: None
Result: IBMDevice, AER_SIMULATOR
Counts: [530, 0, 0, 494]
Probabilities: [0.5175781, 0, 0, 0.4824219]
Samples:
State: 00, Index: 0, Count: 530, Probability: 0.517578125
State: 11, Index: 3, Count: 494, Probability: 0.482421875
Error: None
---------
Result: ATOSDevice, MYQLM_PYLINALG
Counts: [480, 0, 0, 544]
Probabilities: [0.46875, 0, 0, 0.53125]
Samples:
State: 00, Index: 0, Count: 480, Probability: 0.46875
State: 11, Index: 3, Count: 544, Probability: 0.53125
Error: 0.0156020726215454
Run or submit the circuit on a remote device
To execute the circuit on remote device, one can use the exact same process as with local devices. A call of the function run on a remote device will launch the job and wait until it finished before returning the result. One or several devices can still be given in parameter.
[11]:
result = run(circuit, IBMDevice.AER_SIMULATOR)
print(result)
UserWarning: Cloud simulators have been deprecated and will be removed on 15 May 2024. Use the new local testing mode in qiskit-ibm-runtime version 0.22.0 or later to meet your debugging needs.
Result: IBMDevice, AER_SIMULATOR
Counts: [500, 0, 0, 524]
Probabilities: [0.48828125 0. 0. 0.51171875]
Samples:
State: 00, Index: 0, Count: 500, Probability: 0.48828125
State: 11, Index: 3, Count: 524, Probability: 0.51171875
Error: None
However, it is also possible to asynchronously submit the job way using the submit function.
[12]:
from mpqp.execution import submit
By submitting the circuit to a remote device, we retrieve the id of the job attributed by the provider, as well as the corresponding MPQP job.
The MPQP job object contains additional information, such as the status of the job.
[13]:
job_id, job = submit(circuit, IBMDevice.IBM_LEAST_BUSY)
print(job_id)
coogal6phvvei9s9j13g
Once the computation is done, we use the function get_remote_result for retrieving the result.
If the job is not completed, the function will wait (blocking) until it is done.
[14]:
from mpqp.execution import get_remote_result
[15]:
result = get_remote_result(job_id, IBMDevice.IBM_LEAST_BUSY)
print(result)
Result: IBMDevice, IBM_LEAST_BUSY
Counts: [531, 0, 0, 493]
Probabilities: [0.51855469 0. 0. 0.48144531]
Samples:
State: 00, Index: 0, Count: 531, Probability: 0.5185546875
State: 11, Index: 3, Count: 493, Probability: 0.4814453125
Error: None