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How to install and use the Microsoft Quantum resource estimator

The Microsoft Quantum resource estimator is part of the Microsoft Quantum Development Kit (QDK). In this article, you learn how to install the quantum resource estimator and run an estimation for a simple Q# program with a default architecture model.

Warning

The resource estimator in the QDK extension for VS Code will be deprecated soon. Use the qdk.qre Python module to perform resource estimation.

Prerequisites

To follow the steps in this article, you need to have the following installed:

Install the quantum resource estimator

The resource estimator is available through the qdk.qre Python module. To use the resource estimator, install the latest version of the qdk Python library with the qre extra:

pip install --upgrade "qdk[qre]"

Tip

You don't need to have an Azure account to use the resource estimator.

Run a simple resource estimation with default settings

To perform a basic resource estimate, pass an application model, a hardware architecture model, and an error correction model to the resource estimator. Then, run the resource estimator to get the results. The following example runs the resource estimator from Jupyter Notebook in VS Code.

Create an application model

For this example, use the 1D Ising model algorithm from the Q# samples in the QDK VS Code extension. Or, use one of your existing Q# programs.

Write a Q# program

  1. In VS Code, create a new text file and save the file as ising-1d-sample.qs.
  2. On the first line of the file, enter sample to bring up the Q# sample program list.
  3. Choose Ising Model (Simple 1D) sample, and then save the file.

Create an application

Use the Q# program to create an application model for the resource estimator.

  1. To create a new Jupyter Notebook file in VS Code, open the Command Palette and enter Create: New Jupyter Notebook.

  2. In the first notebook cell, evaluate the Q# program.

    from pathlib import Path
from qdk import qsharp

qsharp.eval(Path("ising-1d-sample.qs").read_text(encoding="utf-8"))

    This code creates a Main object in the qdk.code namespace that contains the Q# program.

  3. In a new cell, create a resource estimator application from the Q# program.

    import qdk
from qdk.qre.application import QSharpApplication

app = QSharpApplication(qdk.code.Main)

Create an architecture model

For the hardware architecture, use the default GateBased model that the resource estimator comes with. The GateBased model requires inputs for error rate, gate time, and measurement time.

In a new cell, create a gate-based architecture with an error rate of $10^{-4}$, a gate time of 100 ns, and a measurement time of 500 ns.

from qdk.qre.models import GateBased

# Times are in units of nanoseconds
arch = GateBased(error_rate=1e-4, gate_time=100, measurement_time=500)

Run an estimation

To run an estimate, use the estimate function and pass the following inputs:

  • Application
  • Architecture
  • Physical instruction set (ISA) query
  • Maximum error rate

The ISA query tells the resource estimator what combinations of error correction code and T factory distillation protocols to evaluate. The resource estimator finds the Pareto frontier optimized set of results from those combinations. For this example, use the default SurfaceCode QEC and RoundBasedFactory distillation protocol that the resource estimator comes with.

from qdk.qre import estimate
from qdk.qre.models import SurfaceCode, RoundBasedFactory

results = estimate(app, arch, isa_query=SurfaceCode.q() * RoundBasedFactory.q(), max_error=0.01)```

View the results

To view the optimal combinations of number of physical qubits and program run time, use the as_frame method to display the estimation results in a pandas DataFrame.

results.as_frame()

To plot the results, use the plot_estimates method and pass a unit for run time.

from qdk.qre import plot_estimates

plot_estimates(results, figsize=(6, 4), runtime_unit="ms")
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