Quickstart: Run your first resource estimate

Article
05/05/2023

Learn how to use the Azure Quantum service to submit quantum programs to the Azure Quantum Resource Estimator. This example uses a Jupyter Notebook in Azure Quantum and the built-in azure-quantum Python package - no installation or configuration is required. For more information about using Jupyter Notebooks with Azure Quantum, see Run Jupyter Notebooks in an Azure Quantum workspace.

In this example, you'll run a sample notebook that estimates the costs of a multiplier on a fault-tolerant quantum computer written in Q# and Python.

Prerequisites

An Azure account with an active subscription. If you don’t have an Azure account, register for free and sign up for a pay-as-you-go subscription.
An Azure Quantum workspace. For more information, see Create an Azure Quantum workspace.

Enable the Azure Quantum Resource Estimator target in your workspace

The Resource Estimator is a target of the Microsoft Quantum Computing provider. If you have created a workspace since the release of the Resource Estimator, the Microsoft Quantum Computing provider was added to your workspace automatically.

If you are using an existing Azure Quantum workspace:

Open your workspace in the Azure portal
On the left panel, under Operations, select Providers
Select + Add a provider
Select + Add for Microsoft Quantum Computing
Select Learn & Develop and select Add

Copy a sample notebook

Sign in to the Azure portal and select your Azure Quantum workspace.
Select Notebooks.
In the Sample gallery, navigate to the Resource estimation tab.
Locate the Estimates with Q# input notebook tile, and select Copy to my notebooks.
The sample notebook can be found under My notebooks and you can now run the notebook.

Run the notebook

In My notebooks, select the Estimates with Q# input notebook.
To run the full program from top to bottom, select Run all.
To walk through the example and run each cell individually from top to bottom, select the cell you want to run and select the run icon.

Stepping through the program

The Estimates with Q# input program runs a multiplier and analyzes the physical resource estimates targeted on a fault-tolerant quantum computer.

1st and 2nd cell: Preloads qsharp package and your subscription information to connect to the Azure Quantum service.
3rd cell: Selects the Resource Estimator as the target using the microsoft.estimator target ID and loads some required packages.
4th cell: The Q# code that defines the quantum algorithm. It creates a multiplier using the MultiplyI operation. You can configure the size of the multiplier by passing an operation argument bitwidth to the operation. The operation will have two input registers, each the size of the specified bitwidth, and one output register that is twice the size of the specified bitwidth.

Note

The %%qsharp magic command allows you to enter Q# code directly into the notebook when using the Python 3 (ipykernel). For more information, see %%qsharp magic command.
5th and 6th cells: Submits the quantum algorithm to the Resource Estimator using the qsharp.azure.execute function. The function calls the Q# operation EstimateMultiplication and passes the operation argument bitwidth=8.

8th cell: Retrieves the results of the resource estimation job and shows a table with overall physical resource counts. You can inspect cost details by collapsing the groups, which have more information. For example, if you collapse the Logical qubit parameters group, you can more easily see that the error correction code distance is 13.

Logical qubit parameter	Value
QEC scheme	surface_code
Code distance	13
Physical qubits	338
Logical cycle time	5us 200 ns
Logical qubit error rate	3.00E-9
Crossing prefactor	0.03
Error correction threshold	0.01
Logical cycle time formula	(4 * `twoQubitGateTime` + 2 * `oneQubitMeasurementTime`) * `codeDistance`
Physical qubits formula	2 * `codeDistance` * `codeDistance`

In the Physical qubit parameters group, you can see the physical qubit properties that were assumed for this estimation. For example, the time to perform a single-qubit measurement and a single-qubit gate are assumed to be 100 ns and 50 ns, respectively.

Physical qubit parameter	Value
Qubit name	qubit_gate_ns_e3
Instruction set	GateBased
Single-qubit measurement time	100 ns
T gate time	50 ns
T gate error rate	0.001
Single-qubit measurement error rate	0.001
Single-qubit gate time	50 ns
Single-qubit error rate	0.001
Two-qubit gate time	50 ns
Two-qubit error rate	0.001

For more information, see the full list of output data for the Resource Estimator.

From 9th cell to the end: Changes the target parameters of the program and estimates the same quantum algorithm.

Target parameters

When submitting a resource estimate request for your program, you can specify some optional parameters. There are three top-level target parameters that can be customized:

errorBudget - the overall allowed error budget
qecScheme - the quantum error correction (QEC) scheme
qubitParams - the physical qubit parameters

For more information, see Target parameters for the Resource Estimator.

In the 9th cell of the notebook, you can see the defaults and access all the values that can be passed to a job execution.

result['jobParams']

{'errorBudget': 0.001,
 'qecScheme': {'crossingPrefactor': 0.03,
  'errorCorrectionThreshold': 0.01,
  'logicalCycleTime': '(4 * twoQubitGateTime + 2 * oneQubitMeasurementTime) * codeDistance',
  'name': 'surface_code',
  'physicalQubitsPerLogicalQubit': '2 * codeDistance * codeDistance'},
 'qubitParams': {'instructionSet': 'GateBased',
  'name': 'qubit_gate_ns_e3',
  'oneQubitGateErrorRate': 0.001,
  'oneQubitGateTime': '50 ns',
  'oneQubitMeasurementErrorRate': 0.001,
  'oneQubitMeasurementTime': '100 ns',
  'tGateErrorRate': 0.001,
  'tGateTime': '50 ns',
  'twoQubitGateErrorRate': 0.001,
  'twoQubitGateTime': '50 ns'}}

The Resource Estimator offers six pre-defined qubit parameters, four of which have gate-based instruction sets and two that have a Majorana instruction set. In the 10th cell, you can estimate the cost for the same algorithm using the Majorana-based qubit parameter, qubitParams, "qubit_maj_ns_e6".

result = qsharp.azure.execute(EstimateMultiplication8,
            jobParams={
                "qubitParams": {
                    "name": "qubit_maj_ns_e6"
                }})
result

The pre-defined qubit parameters can also be customized by specifying the name and then updating the values of other fields. For more information, see Custom pre-defined qubit parameters.

Run the following cells of the notebook and learn how to customize the quantum error correction code (QED), qecScheme, and the error budget, errorBudget.

The full functionality of the Resource Estimator is beyond the scope of this quickstart. For more information, see Submitting jobs to the Resource Estimator with different SDKs and IDEs.

Load and run other sample notebooks

You'll find more sample notebooks in the Resource estimation tab of the sample gallery.

Advanced analysis of estimates: This sample uses the implementations from the Estimates with Q# notebook to compute advance analysis of the results such as computing multiple resource estimates, plot and compare their results.
Estimates with Qiskit input: This sample estimates the resources of a quantum circuit for a multiplier written in Qiskit that uses the Quantum Fourier Transform to implement arithmetic.
Estimates with tools producing QIR: This sample shows how to generate and submit a Quantum Intermediate Representation (QIR) program to the Resource Estimator using the QIR generator PyQIR. You can also find this sample in the Tutorial: Submit a QIR program to the Resource Estimator.

The following sample notebooks assess requirements for scaling quantum computers in real-world scenarios. You can learn about the context of these samples in the paper Accessing requirements for scaling quantum computers and their applications.

Quantum dynamics: This sample estimates the resources needed to simulate the quantum spin in a quantum magnet.
Quantum chemistry: This sample estimates the resources needed to calculate the energy of a user provided Hamiltonian to chemical accuracy of 1 mHa, to using the so-called double-factorized qubitization algorithm described in Phys. Rev. Research 3, 033055 (2021). The Hamiltonian is provided in terms of an FCIDUMP file that is accessible via an HTTPS URI.
Factoring: This sample estimates the resources needed to factorize a 2048-bit number, which could have implications in quantum cryptography.

Note

If you have questions or run into any issue using Azure Quantum, bookmark Azure Quantum office hours and join our open office hours every Thursday 8∶30 AM Pacific Time zone (PT).