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fix iqp link (#1096)
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Co-authored-by: Kevin Tian <kevin.tian@ibm.com>
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jyu00 and kt474 authored Sep 21, 2023
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**Qiskit** is an open-source SDK for working with quantum computers at the level of extended quantum circuits, operators, and primitives.

**Qiskit IBM Runtime** is a new environment offered by IBM Quantum that streamlines quantum computations and provides optimal
implementations of the Qiskit primitives `sampler` and `estimator` for IBM Quantum hardware. It is designed to use additional classical compute resources to execute quantum circuits with more efficiency on quantum processors, by including near-time computations such as error suppression and error mitigation. Examples of error suppression include dynamical decoupling, noise-aware compilation, error mitigation including readout mitigation, zero-noise extrapolation (ZNE), and probabilistic error cancellation (PEC).
**Qiskit IBM Runtime** is a new environment offered by IBM Quantum that streamlines quantum computations and provides optimal
implementations of the Qiskit primitives `sampler` and `estimator` for IBM Quantum hardware. It is designed to use additional classical compute resources to execute quantum circuits with more efficiency on quantum processors, by including near-time computations such as error suppression and error mitigation. Examples of error suppression include dynamical decoupling, noise-aware compilation, error mitigation including readout mitigation, zero-noise extrapolation (ZNE), and probabilistic error cancellation (PEC).

Using the runtime service, a research team at IBM Quantum was able to achieve a 120x speedup
in their lithium hydride simulation. For more information, see the
Expand All @@ -30,8 +30,6 @@ pip install qiskit-ibm-runtime

### Qiskit Runtime service on IBM Quantum Platform

The default method for using the runtime service is IBM Quantum Platform.

You will need your IBM Quantum API token to authenticate with the runtime service:

1. Create an IBM Quantum account or log in to your existing account by visiting the [IBM Quantum login page].
Expand Down Expand Up @@ -138,7 +136,7 @@ bell.cx(0, 1)
# 2. Map the qubits to a classical register in ascending order
bell.measure_all()

# 3. Execute using the Sampler primitive
# 3. Execute using the Sampler primitive
backend = service.get_backend('ibmq_qasm_simulator')
sampler = Sampler(backend=backend, options=options)
job = sampler.run(circuits=bell)
Expand Down Expand Up @@ -174,14 +172,14 @@ qc_example.cx(0, 2) # condition 2nd qubit on 0th qubit
# 2. the observable to be measured
M1 = SparsePauliOp.from_list([("XXY", 1), ("XYX", 1), ("YXX", 1), ("YYY", -1)])

# batch of theta parameters to be executed
# batch of theta parameters to be executed
points = 50
theta1 = []
for x in range(points):
theta = [x*2.0*np.pi/50]
theta1.append(theta)

# 3. Execute using the Estimator primitive
# 3. Execute using the Estimator primitive
backend = service.get_backend('ibmq_qasm_simulator')
estimator = Estimator(backend, options=options)
job = estimator.run(circuits=[qc_example]*points, observables=[M1]*points, parameter_values=theta1)
Expand All @@ -197,7 +195,7 @@ This code batches together 50 parameters to be executed in a single job. If a us
In many algorithms and applications, an Estimator needs to be called iteratively without incurring queuing delays on each iteration. To solve this, the IBM Runtime service provides a **Session**. A session starts when the first job within the session is started, and subsequent jobs within the session are prioritized by the scheduler.

You can use the [`qiskit_ibm_runtime.Session`](https://github.com/Qiskit/qiskit-ibm-runtime/blob/main/qiskit_ibm_runtime/session.py) class to start a
session. Consider the same example above and try to find the optimal `theta`. The following example uses the [golden search method](https://en.wikipedia.org/wiki/Golden-section_search) to iteratively find the optimal theta that maximizes the observable.
session. Consider the same example above and try to find the optimal `theta`. The following example uses the [golden search method](https://en.wikipedia.org/wiki/Golden-section_search) to iteratively find the optimal theta that maximizes the observable.

To invoke the `Estimator` primitive within a session:

Expand All @@ -224,15 +222,15 @@ qc_example.cx(0, 2) # condition 2nd qubit on 0th qubit
M1 = SparsePauliOp.from_list([("XXY", 1), ("XYX", 1), ("YXX", 1), ("YYY", -1)])


gr = (np.sqrt(5) + 1) / 2 # golden ratio
gr = (np.sqrt(5) + 1) / 2 # golden ratio
thetaa = 0 # lower range of theta
thetab = 2*np.pi # upper range of theta
tol = 1e-1 # tol
tol = 1e-1 # tol

# 3. Execute iteratively using the Estimator primitive
# 3. Execute iteratively using the Estimator primitive
with Session(service=service, backend="ibmq_qasm_simulator") as session:
estimator = Estimator(session=session, options=options)
#next test range
#next test range
thetac = thetab - (thetab - thetaa) / gr
thetad = thetaa + (thetab - thetaa) / gr
while abs(thetab - thetaa) > tol:
Expand All @@ -245,8 +243,8 @@ with Session(service=service, backend="ibmq_qasm_simulator") as session:
thetaa = thetac
thetac = thetab - (thetab - thetaa) / gr
thetad = thetaa + (thetab - thetaa) / gr
# Final job to evaluate Estimator at midpoint found using golden search method

# Final job to evaluate Estimator at midpoint found using golden search method
theta_mid = (thetab + thetaa) / 2
job = estimator.run(circuits=qc_example, observables=M1, parameter_values=theta_mid)
print(f"Session ID is {session.session_id}")
Expand All @@ -258,23 +256,26 @@ This code returns `Job result is [4.] at theta = 1.575674623307102` using only n

## Instances

Access to IBM Quantum Platform services is controlled by the instances (previously called providers) to which you are assigned. An instance is defined by a hierarchical organization of hub, group, and project. A hub is the top level of a given hierarchy (organization) and contains within it one or more groups. These groups are in turn populated with projects. The combination of hub/group/project is called an instance. Users can belong to more than one instance at any time.
Access to IBM Quantum Platform channel is controlled by the instances (previously called providers) to which you are assigned. An instance is defined by a hierarchical organization of hub, group, and project. A hub is the top level of a given hierarchy (organization) and contains within it one or more groups. These groups are in turn populated with projects. The combination of hub/group/project is called an instance. Users can belong to more than one instance at any time.

> **_NOTE:_** IBM Cloud instances are different from IBM Quantum Platform instances. IBM Cloud does not use the hub/group/project structure for user management. To view and create IBM Cloud instances, visit the [IBM Cloud Quantum Instances page](https://cloud.ibm.com/quantum/instances).
To view a list of your instances, visit your [account settings page](https://www-dev.quantum-computing.ibm.com/account) or use the `instances()` method.
To view a list of your instances, visit your [account settings page](https://www.quantum-computing.ibm.com/account) or use the `instances()` method.

You can specify an instance when initializing the service or provider, or when picking a backend:
You can specify an instance when initializing the service or provider, or when picking a backend:

```python

# Optional: List all the instances you can access.
service = QiskitRuntimeService(channel='ibm_quantum')
print(service.instances())

# Optional: Specify the instance at service level. This becomes the default unless overwritten.
service = QiskitRuntimeService(channel='ibm_quantum', instance="hub1/group1/project1")
backend1 = service.backend("ibmq_manila")
# Optional: Specify the instance at the backend level, which overwrites the service-level specification when this backend is used.

# Optional: Specify the instance at the backend level, which overwrites the service-level specification when this backend is used.
backend2 = service.backend("ibmq_manila", instance="hub2/group2/project2")

sampler1 = Sampler(backend=backend1) # this will use hub1/group1/project1
sampler2 = Sampler(backend=backend2) # this will use hub2/group2/project2
```
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