Azure-Quantum-Resource-Estimatation

Microsoft has today announced the Azure Quantum Resource Estimator, which provides tools to help customers create and refine algorithms for quantum computers.

Innovators in quantum computing face the challenge of creating algorithms on current hardware for scalable quantum computers of the future. There are restrictions in computations that hamper innovation. Building algorithms that will run on more powerful hardware in the future comes with acceleration issues.

Microsoft says Azure Quantum Resource Estimator helps innovators to address these challenges. It relies on the importance of data for building, testing, and refining algorithms for practical quantum computing solutions of the future.

It seems the system works as Microsoft says it has been using the Resource Estimator internally and it has been instrumental in the development of its quantum machine:

“The insights it has provided have informed our approach to engineering a machine capable of the scale required for impact including the machine’s architecture and our decision to use topological qubits. We’re making progress on our machine and recently had a physics breakthrough that was detailed in a preprint to the arXiv.”

That breakthrough came earlier this year. Microsoft’s Azure Quantum team has developed a new type of qubit that shows the physics for scalable quantum computing holds up.

How the Estimator Works

Microsoft says the Azure Quantum Resource Estimator is essential because it focuses on breaking through restrictions of scalable algorithms. It essentially provides data on what resources are needed for the quantum algorithm, such as the number of computational processes, and the number of physical qubits.

“For the first time, it is possible to compare resource estimates for quantum algorithms at scale across different hardware profiles. Start from well-known, pre-defined qubit parameter settings and quantum error correction (QEC) schemes or configure unique settings across a wide range of machine characteristics such as operation error rates, operation speeds, and error correction schemes and thresholds.”

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