What will it take to solve the quantum computing challenges of the future? Microsoft has an app for that – and now developers around the world can have it too.
The application is called Azure Quantum Resource Estimator. It is a software tool that was originally developed for internal Microsoft use. The tool is already guiding the company’s efforts to develop full-stack quantum computers, and now it can also help outside developers determine how much computing power they’ll need to run a given quantum algorithm in a reasonable amount of time.
This is a key question, because the guidelines used for classical computing do not necessarily apply at the quantum frontier. Unlike classical computers, quantum computers take advantage of an environment in which a quantum bit – better known as a qubit – can represent a one and a zero at the same time.
Quantum approaches can be much more effective than the standard binary computing approach in solving particular types of problems: optimizing a network, for example, or figuring out how to design a synthetic molecule to perform a specific chemical task.
“We will be able to study, for example, how to help remove harmful gases from the atmosphere,” Krysta Svore, a distinguished engineer and vice president of quantum software at Microsoft, told GeekWire.
“Ten years ago we thought it would take a billion years of execution on a quantum computer,” Svore said. “It’s a very long wait. But over the past decade, we’ve been able to get that down to a month of running on a quantum computer…using exactly the resource estimator, that tool, to figure out the cost of the algorithm. And we were also able to redesign our hardware accordingly.
There is a small catch: the type of quantum computer that the resource estimator uses as a baseline does not yet exist. “What we’ve found is that these quantum machines to run problems that we identify as having practical quantum advantage will need at least a million qubits,” Svore said.
Just last week, IBM unveiled its largest quantum processor, which packs in just 433 qubits. IBM aims to scale its systems to more than 4,000 qubits by 2025, and D-Wave Systems plans to deploy a 7,000-qubit annealing quantum computer by 2023-2024. Even those machines will be way below the power that Svore and his colleagues at Microsoft have in mind.
“Achieving a quantum machine that has more than a million physical qubits is measured in years,” Svore acknowledged. But she stressed that it will also take years to gain a deep understanding of the applications of quantum computing. “So we have to prepare,” she said.
This is where the Resource Estimator will come in handy, especially in cases where developers mix classical and quantum approaches to come up with hybrid methods for solving problems.
“It’s a great tool to understand the hybrid,” Svore said. “I have a classical calculation and a quantum calculation which come together. What is the cost of each? Quantum computing should be used when it provides a speedup over classical usage. So you want to compare this classical-plus-classical with classical-plus-quantum. It is a tool to allow this type of study.
The estimator tells you approximately how much processing time it takes to run a given algorithm under different computational scenarios, depending on the number of qubits, type of error correction scheme, and other parameters.
Svore said the estimator could show software developers how making changes to their quantum algorithms could lead to faster run times. “We at Microsoft have also used the tool to develop the underlying architecture of the machine, to understand what type of machine will even activate these algorithms,” she said. The tool supports Microsoft’s view that a topology-based quantum machine “will provide the necessary scaling,” Svore said.
The first steps in the process of using the estimator involve setting up an Azure account and creating an Azure Quantum workspace. Then you can follow the procedure described in this introduction to quantum resource estimation.
Michal Stechly, a quantum software engineer at Zapata Computing, said in a Microsoft blog post that the estimator is “easy to use.”
“The onboarding process was straightforward, and the results provide both a high-level overview useful for people new to error fixing, as well as a detailed breakdown for experts,” Stechly said. . “Resource estimation should be part of the pipeline for anyone working on fault-tolerant quantum algorithms.”
In 1964, Nobel Prize-winning physicist Richard Feynman said, “I think I can safely say that nobody understands quantum mechanics. The fact that there is now an “easy to use” tool for quantum computing may well be an indication of how far we have come since then.
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