Microsoft introduced its Azure Quantum Resource Estimator to help developers and researchers check if their algorithm will be practical to run on future-scaled quantum computers, and enable comparison across different hardware implementations while estimating the number of qubits and runtime required to execute quantum applications on those systems.

Quantum software developers have already started to create algorithms to run on future fault-tolerant scaled quantum computers as they await hardware advances, Fabrice Frachon, principal project manager of Azure Quantum at Microsoft, noted in a blog post.

“These innovators are faced with questions such as: What hardware resources are required? How many physical and logical qubits are needed and what type? What’s the runtime? Azure Quantum Resource Estimator was designed specifically to answer these questions,” Frachon wrote.

Starting as an internal tool, Microsoft has been using the estimator to shape its quantum machine design including the machine’s architecture and its decision to use topological qubits.

“The Azure Quantum Resource Estimator performs one of the most challenging problems for researchers developing quantum algorithms. It breaks down the resources required for a quantum algorithm, including the total number of physical qubits, the computational resources required including wall clock time, and the details of the formulas and values used for each estimate,” Frachon touted.

Azure Quantum Resource Estimator for Quantum Algorithm R&D Acceleration

This resource estimation allows quantum software innovators to compare algorithms at scale across various hardware profiles, and define custom machine characteristics and quantum error correction (QEC) models, Microsoft claims.

This also paves the way for software-hardware co-design, Frachon claims, “enabling hardware designers to improve their architectures based on how large-scale algorithms might run on their specific implementation, and in turn, allowing algorithm and software developers to iterate on bringing down the cost of algorithms at scale.”

Additionally, today’s noisy intermediate scale quantum (NISQ) systems will transition to tomorrow’s fault-tolerant quantum computers, Frachon noted. The estimator can help with the transition by estimating the number of logical and physical qubits and runtime required to run applications on those post-NISQ quantum computers.

It can “estimate the overheads in time and space required to enable implementation of your scaled quantum algorithms on a variety of hardware designs, and use the information to improve your algorithms and applications well before scaled fault-tolerant hardware is available,” he wrote.

Those estimations can help accelerate the current research and development on quantum algorithms for solving problems in chemistry, materials science, or finance, Microsoft claims.

Microsoft Releases Jupyter Notebooks on Azure Quantum

The tech giant this week also plans to publish the raw data and analysis in Jupyter notebooks on Azure Quantum. It allows access to the same data as Microsoft’s scientists to see the company’s progress in engineering a scalable quantum computer with topological qubits.

“These notebooks provide the exact steps needed to reproduce all the data in our paper. While engineering challenges remain, the physics discovery demonstrated in this data proves out a fundamental building block for our approach to a scaled quantum computer and puts Microsoft on the path to deliver a quantum machine in Azure that will help solve some of the world’s toughest problems,” Frachon explained.