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Add output for code examples (#1146)
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closes #1143
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@beckykd
beckykd authored May 9, 2024
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167 changes: 138 additions & 29 deletions docs/run/primitives-examples.mdx
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Expand Up @@ -39,8 +39,9 @@ import numpy as np
from qiskit.circuit.library import IQP
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit.quantum_info import SparsePauliOp, random_hermitian
from qiskit_ibm_runtime import EstimatorV2 as Estimator, QiskitRuntimeService
from qiskit_ibm_runtime import EstimatorV2 as Estimator

from qiskit_ibm_runtime import QiskitRuntimeService
service = QiskitRuntimeService()
backend = service.least_busy(operational=True, simulator=False, min_num_qubits=127)
estimator = Estimator(backend)
Expand All @@ -60,6 +61,11 @@ result = job.result()

print(f" > Expectation value: {result[0].data.evs}")
print(f" > Metadata: {result[0].metadata}")
```
Output:
```text
> Expectation value: 0.123046875
> Metadata: {'target_precision': 0.015625}
```
</TabItem>

Expand Down Expand Up @@ -104,16 +110,15 @@ Use Estimator to determine the expectation values of multiple circuit-observable
<TabItem value="EstimatorV2" label="Estimator V2">
```python
import numpy as np

from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit.circuit.library import IQP
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit.quantum_info import SparsePauliOp, random_hermitian
from qiskit_ibm_runtime import QiskitRuntimeService, EstimatorV2 as Estimator
from qiskit_ibm_runtime import EstimatorV2 as Estimator
from qiskit_ibm_runtime import QiskitRuntimeService

service = QiskitRuntimeService()
backend = service.least_busy(operational=True, simulator=False, min_num_qubits=127)
pm = generate_preset_pass_manager(backend=backend, optimization_level=1)
estimator = Estimator(backend)

n_qubits = 127
rng = np.random.default_rng()
Expand All @@ -128,6 +133,7 @@ observables = [
]

# Get ISA circuits
pm = generate_preset_pass_manager(optimization_level=1, backend=backend)

for qc, obs in zip(circuits, observables):
isa_circuit = pm.run(qc)
Expand All @@ -142,6 +148,15 @@ for idx in range(len(pubs)):
pub_result = job_result[idx]
print(f">>> Expectation values for PUB {idx}: {pub_result.data.evs}")
print(f">>> Standard errors for PUB {idx}: {pub_result.data.stds}")
```
Output:
```text
>>> Expectation values for PUB 0: -0.0263671875
>>> Standard errors for PUB 0: 0.015619567582387688
>>> Expectation values for PUB 1: -0.017578125
>>> Standard errors for PUB 1: 0.015622585825382946
>>> Expectation values for PUB 2: 0.33349609375
>>> Standard errors for PUB 2: 0.014730491894982241
```
</TabItem>

Expand Down Expand Up @@ -194,7 +209,8 @@ import numpy as np
from qiskit.circuit import QuantumCircuit, Parameter
from qiskit.quantum_info import SparsePauliOp
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit_ibm_runtime import QiskitRuntimeService
from qiskit_ibm_runtime import EstimatorV2 as Estimator


# Step 1: Map classical inputs to a quantum problem

Expand All @@ -217,21 +233,25 @@ ops = [ZZ, ZX, XZ, XX]

# Step 2: Optimize problem for quantum execution.

from qiskit_ibm_runtime import QiskitRuntimeService
service = QiskitRuntimeService()
backend = service.least_busy(operational=True, simulator=False)
estimator = Estimator(backend)

estimator = Estimator(backend)

pm = generate_preset_pass_manager(backend=backend, optimization_level=1)
chsh_isa_circuit = pm.run(chsh_circuit)
isa_observables = [operator.apply_layout(chsh_isa_circuit.layout) for operator in ops]

from qiskit_ibm_runtime import EstimatorV2 as Estimator

# Step 3: Execute using Qiskit primitives.

# Reshape observable array for broadcasting
reshaped_ops = np.fromiter(isa_observables, dtype=object)
reshaped_ops = reshaped_ops.reshape((4, 1))

from qiskit_ibm_runtime import EstimatorV2 as Estimator

estimator = Estimator(backend, options={"default_shots": int(1e4)})
job = estimator.run([(chsh_isa_circuit, reshaped_ops, individual_phases)])
# Get results for the first (and only) PUB
Expand All @@ -240,6 +260,47 @@ print(f">>> Expectation values: {pub_result.data.evs}")
print(f">>> Standard errors: {pub_result.data.stds}")
print(f">>> Metadta: {pub_result.metadata}")
```
<details>
<summary>
Output
</summary>
```text
>>> Expectation values: [[ 0.88525391 0.83837891 0.70458984 0.52880859 0.29150391 -0.00146484
-0.28271484 -0.52587891 -0.71777344 -0.83300781 -0.88916016 -0.83935547
-0.71826172 -0.53613281 -0.24560547 -0.00830078 0.28320312 0.515625
0.72460938 0.83935547 0.89013672]
[-0.00390625 0.25683594 0.515625 0.71337891 0.83691406 0.87548828
0.83105469 0.70605469 0.54150391 0.28222656 0.01269531 -0.27636719
-0.52539062 -0.73388672 -0.83203125 -0.88134766 -0.83984375 -0.71386719
-0.50390625 -0.25292969 0.02001953]
[-0.01464844 -0.26660156 -0.51757812 -0.72216797 -0.83398438 -0.89013672
-0.84472656 -0.72070312 -0.52001953 -0.26660156 0.00244141 0.26074219
0.51123047 0.7109375 0.83886719 0.86865234 0.86669922 0.71435547
0.52587891 0.27636719 -0.01025391]
[ 0.88525391 0.85986328 0.72509766 0.54101562 0.28125 0.00878906
-0.27539062 -0.52636719 -0.71533203 -0.84130859 -0.8828125 -0.83740234
-0.72998047 -0.51513672 -0.26171875 0.00537109 0.26660156 0.52636719
0.69677734 0.84521484 0.87353516]]
>>> Standard errors: [[0.00726731 0.008517 0.01108773 0.01326158 0.0149464 0.01562498
0.01498756 0.01328999 0.01087932 0.00864471 0.00714994 0.00849348
0.01087145 0.01318959 0.0151464 0.01562446 0.01498531 0.01338772
0.01076812 0.00849348 0.00712021]
[0.01562488 0.01510086 0.01338772 0.01094966 0.0085521 0.00755061
0.00869048 0.01106496 0.01313591 0.01498981 0.01562374 0.01501644
0.01329471 0.01061362 0.00866764 0.00738232 0.00848169 0.01094189
0.01349622 0.01511695 0.01562187]
[0.01562332 0.01505948 0.01336931 0.01080809 0.00862169 0.00712021
0.00836247 0.01083193 0.01334616 0.01505948 0.01562495 0.01508451
0.01342881 0.01098836 0.00850525 0.00774097 0.00779424 0.01093411
0.01328999 0.01501644 0.01562418]
[0.00726731 0.00797694 0.01076009 0.01314082 0.01499429 0.0156244
0.01502082 0.01328527 0.01091851 0.00844617 0.00733946 0.00854042
0.01067919 0.01339231 0.01508038 0.01562477 0.01505948 0.01328527
0.01120762 0.00835042 0.00760564]]
>>> Metadta: {'target_precision': 0.015625}
```
</details>

</TabItem>

<TabItem value="EstimatorV1" label="Estimator (V1)">
Expand Down Expand Up @@ -286,7 +347,12 @@ import numpy as np
from qiskit.circuit.library import IQP
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit.quantum_info import SparsePauliOp, random_hermitian
from qiskit_ibm_runtime import QiskitRuntimeService, Session, EstimatorV2 as Estimator
from qiskit_ibm_runtime import Session, EstimatorV2 as Estimator
from qiskit_ibm_runtime import QiskitRuntimeService

service = QiskitRuntimeService()
backend = service.least_busy(operational=True, simulator=False, min_num_qubits=127)
estimator = Estimator(backend)

n_qubits = 127

Expand All @@ -304,10 +370,6 @@ another_isa_circuit = pm.run(another_circuit)
isa_observable = observable.apply_layout(isa_circuit.layout)
another_isa_observable = another_observable.apply_layout(another_isa_circuit.layout)

service = QiskitRuntimeService()

backend = service.least_busy(operational=True, simulator=False, min_num_qubits=127)

with Session(service=service, backend=backend) as session:
estimator = Estimator(session=session)

Expand All @@ -327,6 +389,13 @@ with Session(service=service, backend=backend) as session:
print(f" > Another Expectation value: {another_result[0].data.evs}")
print(f" > More Metadata: {another_result[0].metadata}")
```
Output:
```text
> Expectation value: 0.0048828125
> Metadata: {'target_precision': 0.015625}
> Another Expectation value: -0.03857421875
> More Metadata: {'target_precision': 0.015625}
```
</TabItem>

<TabItem value="EstimatorV1" label="Estimator (V1)">
Expand Down Expand Up @@ -377,6 +446,7 @@ print(f" > Expectation values job 2: {another_result.values}")
</TabItem>
</Tabs>

<span id="sampler-examples"></span>
## Sampler examples

Generate entire error-mitigated quasi-probability distributions sampled from quantum circuit outputs. Leverage Sampler’s capabilities for search and classification algorithms like Grover’s and QVSM.
Expand All @@ -393,26 +463,38 @@ import numpy as np
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit.circuit.library import IQP
from qiskit.quantum_info import random_hermitian
from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler
from qiskit_ibm_runtime import SamplerV2 as Sampler
from qiskit_ibm_runtime import QiskitRuntimeService

service = QiskitRuntimeService()

backend = service.least_busy(operational=True, simulator=False, min_num_qubits=127)
estimator = Estimator(backend)

n_qubits = 127

mat = np.real(random_hermitian(n_qubits, seed=1234))
circuit = IQP(mat)
circuit.measure_all()

from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager

pm = generate_preset_pass_manager(backend=backend, optimization_level=1)
isa_circuit = pm.run(circuit)

sampler = Sampler(backend)
job = sampler.run([isa_circuit])
result = job.result()

# Get results for the first (and only) PUB
pub_result = result[0]

print(f" > Counts: {pub_result.data.meas.get_counts()}")
```
</TabItem>
Output
```text
> Counts: {'0101': 103, '0100': 195, '0011': 142, '0000': 237, '1010': 26, '0001': 92, '0110': 18, '1111': 19, '0010': 36, '1100': 5, '0111': 42, '1110': 31, '1011': 27, '1101': 18, '1001': 13, '1000': 20}
```
</TabItem>

<TabItem value="SamplerV1" label="Sampler (V1)">
```python
Expand Down Expand Up @@ -457,11 +539,12 @@ import numpy as np
from qiskit.circuit.library import IQP
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit.quantum_info import random_hermitian
from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler
from qiskit_ibm_runtime import SamplerV2 as Sampler
from qiskit_ibm_runtime import QiskitRuntimeService

service = QiskitRuntimeService()

backend = service.least_busy(operational=True, simulator=False, min_num_qubits=127)
estimator = Estimator(backend)

n_qubits = 127

Expand All @@ -480,6 +563,12 @@ result = job.result()

for idx, pub_result in enumerate(result):
print(f" > Counts for pub {idx}: {pub_result.data.meas.get_counts()}")
```
Output
```text
> Counts for pub 0: {'0001': 120, '0000': 671, '0101': 21, '0011': 18, '0010': 91, '1001': 7, '1000': 23, '0100': 29, '1110': 2, '0110': 28, '1010': 3, '1111': 2, '1100': 4, '1011': 3, '0111': 2}
> Counts for pub 1: {'1001': 31, '1100': 122, '0100': 263, '0101': 86, '1101': 69, '1000': 96, '0001': 51, '1011': 7, '0110': 21, '0000': 163, '0011': 17, '1010': 26, '0010': 48, '1110': 13, '0111': 10, '1111': 1}
> Counts for pub 2: {'0000': 694, '0010': 78, '0100': 61, '0011': 21, '0001': 58, '0111': 6, '1000': 26, '0110': 50, '1001': 9, '1010': 3, '1100': 10, '1011': 2, '0101': 4, '1110': 1, '1111': 1}
```
</TabItem>

Expand Down Expand Up @@ -527,11 +616,12 @@ Run several experiments in a single job, leveraging parameter values to increase
import numpy as np
from qiskit.circuit.library import RealAmplitudes
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit_ibm_runtime import QiskitRuntimeService

# Step 1: Map classical inputs to a quantum problem
num_qubits = 127
circuit = RealAmplitudes(num_qubits=num_qubits, reps=2)

n_qubits = 127

circuit = RealAmplitudes(num_qubits=n_qubits, reps=2)
circuit.measure_all()

# Define three sets of parameters for the circuit
Expand All @@ -542,8 +632,12 @@ parameter_values = [

# Step 2: Optimize problem for quantum execution.

from qiskit_ibm_runtime import SamplerV2 as Sampler
from qiskit_ibm_runtime import QiskitRuntimeService

service = QiskitRuntimeService()
backend = service.least_busy(operational=True, simulator=False, min_num_qubits=num_qubits)
backend = service.least_busy(operational=True, simulator=False, min_num_qubits=127)
estimator = Estimator(backend)

pm = generate_preset_pass_manager(backend=backend, optimization_level=1)
isa_circuit = pm.run(circuit)
Expand All @@ -559,6 +653,10 @@ result = job.result()
pub_result = result[0]
# Get counts from the classical register "meas".
print(f" >> Counts for the meas output register: {pub_result.data.meas.get_counts()}")
```
Output
```text
>> Counts for the meas output register: {'1000': 449, '0100': 183, '0110': 475, '1110': 249, '0101': 167, '0111': 116, '1100': 227, '0011': 111, '1101': 123, '1001': 252, '1010': 229, '0001': 37, '0010': 123, '1011': 120, '1111': 156, '0000': 55}
```
</TabItem>

Expand Down Expand Up @@ -606,7 +704,12 @@ import numpy as np
from qiskit.circuit.library import IQP
from qiskit.quantum_info import random_hermitian
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler, Session
from qiskit_ibm_runtime import Session, SamplerV2 as Sampler
from qiskit_ibm_runtime import QiskitRuntimeService

service = QiskitRuntimeService()
backend = service.least_busy(operational=True, simulator=False, min_num_qubits=127)
estimator = Estimator(backend)

n_qubits = 127

Expand All @@ -618,15 +721,12 @@ mat = np.real(random_hermitian(n_qubits, seed=rng))
another_circuit = IQP(mat)
another_circuit.measure_all()

service = QiskitRuntimeService()

backend = service.least_busy(operational=True, simulator=False, min_num_qubits=n_qubits)

pm = generate_preset_pass_manager(backend=backend, optimization_level=1)
isa_circuit = pm.run(circuit)
another_isa_circuit = pm.run(another_circuit)

with Session(service=service, backend=backend) as session:
#with Session(service=service, backend=backend) as session:
with Session(backend=backend) as session:
sampler = Sampler(session=session)
job = sampler.run([isa_circuit])
another_job = sampler.run([another_isa_circuit])
Expand All @@ -635,9 +735,18 @@ with Session(service=service, backend=backend) as session:

# first job
print(f" > Counts for job 1: {result[0].data.meas.get_counts()}")

```
Output
```text
> Counts for job 1: {'1110': 39, '0100': 164, '0000': 274, '0010': 40, '0001': 101, '0011': 138, '1101': 20, '1010': 26, '1100': 7, '0101': 83, '0111': 43, '1011': 15, '1001': 14, '1000': 34, '0110': 12, '1111': 14}
```
```python
# second job
print(f" > Counts for job 2: {another_result[0].data.meas.get_counts()}")
```
Output
```text
> Counts for job 2: {'0000': 285, '0100': 128, '0111': 29, '0110': 147, '0011': 15, '0010': 277, '1110': 10, '1010': 25, '1011': 15, '1000': 32, '0001': 21, '1111': 6, '1100': 10, '1101': 5, '1001': 15, '0101': 4}
```
</TabItem>

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