Get started with Tensor-network error mitigation (TEM)

Note

Qiskit Functions are an experimental feature available only to IBM Quantum® Premium Plan, Flex Plan, and On-Prem (via IBM Quantum Platform API) Plan users. They are in preview release status and subject to change.

For an overview of this function, see the Tensor-network error mitigation (TEM) documentation home.

The code on this page was developed using the following requirements. We recommend using these versions or newer.
```
qiskit[all]~=2.5.1
```

Set up

Authenticate using your IBM Quantum Platform API key, and select the TEM function as follows. (This snippet assumes you've already saved your account to your local environment.)

from qiskit_ibm_catalog import QiskitFunctionsCatalog

tem_function_name = "algorithmiq/tem"
catalog = QiskitFunctionsCatalog(channel="ibm_quantum_platform")
# verify that you have access to the function
catalog.list()

Output:

[QiskitFunction(qunova/hivqe-chemistry),
 QiskitFunction(global-data-quantum/quantum-portfolio-optimizer),
 QiskitFunction(algorithmiq/tem),
 QiskitFunction(qedma/qesem),
 QiskitFunction(multiverse/singularity),
 QiskitFunction(ibm/circuit-function),
 QiskitFunction(q-ctrl/optimization-solver),
 QiskitFunction(colibritd/quick-pde),
 QiskitFunction(q-ctrl/performance-management),
 QiskitFunction(kipu-quantum/iskay-quantum-optimizer)]

# Load your function
tem = catalog.load(tem_function_name)

Example

The following snippet shows an example where TEM is used to compute the expectation values of an observable given a simple quantum circuit.

from qiskit import QuantumCircuit
from qiskit.quantum_info import SparsePauliOp

# Create a quantum circuit
qc = QuantumCircuit(3)
qc.u(0.4, 0.9, -0.3, 0)
qc.u(-0.4, 0.2, 1.3, 1)
qc.u(-1.2, -1.2, 0.3, 2)
for _ in range(2):
    qc.barrier()
    qc.cx(0, 1)
    qc.cx(2, 1)
    qc.barrier()
    qc.u(0.4, 0.9, -0.3, 0)
    qc.u(-0.4, 0.2, 1.3, 1)
    qc.u(-1.2, -1.2, 0.3, 2)

# Define the observables
observable = SparsePauliOp("IYX", 1.0)

# Define the execution options
pub = (qc, [observable])
options = {"default_precision": 0.02}

# Define backend to use. TEM will choose the least-busy device
# reported by IBM if not specified
backend_name = "ibm_marrakesh"

# Run the TEM function (uses around three minutes of QPU time)
job = tem.run(pubs=[pub], backend_name=backend_name, options=options)

Use the Qiskit Serverless APIs to check your Qiskit Function workload's status:

# Print the ID so you can use it later, if necessary
print(job.job_id)
print(job.status())

Output:

ea1d7802-b067-4cc9-af39-3c2402d22413

QUEUED

You can return the results as:

result = job.result()
evs = result[0].data.evs
print(evs[0])

Output:

-0.10628832892063486

Info

The expected value for the noiseless circuit for the given operator should be around 0.18409094298943401.