Explore quantum
Resource Estimation
Resource Estimation
Although quantum computers promise to solve some of the most intractable problems our society faces, such as climate change and food security, commercially viable solutions will require large-scale, fault-tolerant quantum computers. Solving these problems will also require algorithms and quantum applications capable of executing solutions at a scale, and in a timeframe, that is practical. But how do you know how long a given quantum application will take to run, or how many physical qubits it will require? In quantum computing, Resource Estimation is the process used to answer these questions. Resource Estimation can help you determine the number of qubits, quantum gates, processing time, and other resources needed to run a quantum program assuming specific hardware characteristics.
In November 2022, Microsoft Quantum announced the release of the Azure Quantum Resource Estimator, a tool to help quantum innovators prepare their solutions for future, scaled quantum systems. The Resource Estimator does this by helping to ascertain how many resources it will take to run a quantum application on a fault tolerant quantum machine. Using the Resource Estimator, quantum developers can understand the number of logical and physical qubits and runtime required to execute a given quantum program. The developer or researcher can then iterate, progressively bringing down the cost required to run the solution. Knowing how long it will take to run a specific algorithm, and which qubit technologies are better suited to solving a specific problem, can guide the developer towards beneficial implementation changes that will improve resource consumption.
The Azure Quantum Resource Estimator is designed specifically for future, fault-tolerant quantum systems. Starting from well-known pre-defined qubit parameter settings and Quantum Error Correction (QEC) schemes, a developer can estimate resources for an application or algorithm using the Resource Estimator to understand how choices such as qubit technology, or assumed error correction scheme, will impact overall runtime. More advanced users can review the formulas and values used to derive each estimate and can configure settings across a wide range of machine characteristics such as operation error rates, operation speeds, and error correction schemes. Running comparisons across configurations can help quantum developers refine their solutions to run efficiently on future, scaled quantum machines.
To learn more about Resource Estimation, including how to get started with the Azure Quantum Resource Estimator, check out the Microsoft Quantum release announcement.
The Azure Quantum Resource Estimator has been designed with capabilities to help you analyze and optimize your quantum applications.
Capabilities to facilitate application analysis:
Resource Estimator space diagram
Capabilities to help you quickly run and retrieve Resource Estimation jobs:
Capabilities to improve your estimate: