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How to compare multiple configurations of target parameters with the resource estimator

In this article, you learn how to use the Microsoft Quantum resource estimator to run estimates for multiple configurations of target parameters at the same time and compare the results for each configuration.

To compare resource estimates for different configurations of target parameters, you can run one estimate at a time or you can run a batch job that estimates the resource requirements for all configurations. Batch estimates are useful because they display the results for all configurations together in tables and visual charts.

For information about how to run the resource estimator, see Different ways to use the resource estimator.

Prerequisites

Run multiple configurations with the resource estimator

You can run multiple configurations of target parameters as a single batch job in Q# with Jupyter Notebook in VS Code. To run a batch estimate, pass a list of target parameters dictionaries to the params parameter of the qsharp.estimate function.

The following example shows how to run two configurations of target parameters as a single batch job for a Q# algorithm that puts a qubit into an equal superposition state. The first configuration uses the default target parameters, and the second configuration uses the qubit_maj_ns_e6 qubit parameter and the floquet_code quantum error correction (QEC) scheme.

To compare estimates for the Q# program, follow these steps:

  1. In VS Code, open the View menu and choose Command Palette.

  2. Enter Create: New Jupyter Notebook.

  3. To import the qsharp Python module, run the following code in the first cell:

    from qdk import qsharp

  4. Write your Q# program in a new %%qsharp cell:

    %%qsharp

operation Main() : Result {
    use qubit = Qubit();
    H(qubit);
    MResetZ(qubit)
}

  5. Create a list of parameter set dictionaries and then pass the list as the params argument to the qsharp.estimate function. Run the following code in a new cell:

    defaultParams = {} # Use an empty dictionary for the default input parameter set
customParams = {"qubitParams": {"name": "qubit_maj_ns_e6"},
                "qecScheme": {"name": "floquet_code"}
                }

result_batch = qsharp.estimate("Main()", params=[defaultParams, customParams])

result_batch.summary_data_frame(labels=["Gate-based ns, 10⁻³", "Majorana ns, 10⁻⁶"])

A summary of resource estimation results appears in a table so that you can easily compare how the algorithm runs with different qubit types and error correction schemes.

Run a batch job with the EstimatorParams class

You can also construct a list of estimation target parameters as attributes of the EstimatorParams class. The following code shows how to batch six configurations of target parameters as a single job.

from qdk import qsharp
from qdk.estimator import EstimatorParams, QubitParams, QECScheme

labels = ["Gate-based µs, 10⁻³", "Gate-based µs, 10⁻⁴", "Gate-based ns, 10⁻³", "Gate-based ns, 10⁻⁴", "Majorana ns, 10⁻⁴", "Majorana ns, 10⁻⁶"]

params = EstimatorParams(num_items=6)
params.error_budget = 0.333
params.items[0].qubit_params.name = QubitParams.GATE_US_E3
params.items[1].qubit_params.name = QubitParams.GATE_US_E4
params.items[2].qubit_params.name = QubitParams.GATE_NS_E3
params.items[3].qubit_params.name = QubitParams.GATE_NS_E4
params.items[4].qubit_params.name = QubitParams.MAJ_NS_E4
params.items[4].qec_scheme.name = QECScheme.FLOQUET_CODE
params.items[5].qubit_params.name = QubitParams.MAJ_NS_E6
params.items[5].qec_scheme.name = QECScheme.FLOQUET_CODE

qsharp.estimate("Main()", params=params).summary_data_frame(labels=labels)

Note

If you experience issues when you work with the resource estimator, then see the Troubleshooting page or contact AzureQuantumInfo@microsoft.com.

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