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Quantum Computers Won’t Need Quantum Advantage to Compete Economically With Classical Systems

gold and silver round frame magnifying glass
gold and silver round frame magnifying glass
Quantum computers may still offer economic value over classical systems even before achieving quantum advantage. (Photo by Unsplash/Elena Mozhvilo)

In a working paper, a team of researchers report that because of the economic value that arises from asymmetries in cost structure between classical and quantum systems, quantum computers may still offer economic value over classical systems even before achieving quantum advantage.

The researchers write: “The results show that it is possible for quantum computers to be worth deploying even if quantum advantage is never achieved. The relative usefulness of quantum computers depends on how the benefit of faster calculation compares to the higher cost of scaling.”

A larger implication may be that quantum advantage may be irrelevant benchmark for quantum computing.

“That does not imply that quantum computers will be immediately useful for a wide range of applications. Instead, the results suggest that quantum advantage is not the appropriate benchmark for a commercially viable quantum computer.”

“That does not imply that quantum computers will be immediately useful for a wide range of applications,” the team writes. “Instead, the results suggest that quantum advantage is not the appropriate benchmark for a commercially viable quantum computer.”

In the study, the research team used a duopoly model — with a quantum computing company competes against a classical computing company — featuring “an asymmetric variable cost structure between the two companies and the potential for an asymmetric fixed cost structure, where each firm can invest in scaling its hardware to expand its respective market.”

Cost structure is influenced by two different factors, based on differences between quantum and classical computers, according to the researchers. The first factor is the ability of quantum algorithms to speed up certain processes relative to classical algorithms. The expected efficiency of quantum algorithms leads to a variable cost advantage for quantum computers. The second factor is the cost to scale each hardware, where more qubits a quantum computer — or the more bits a classical computer has — means the device can solve larger problems and address larger markets. Currently, classical computers are easier to scale.

“The results suggest that quantum computers may not need to display a quantum advantage to be able to generate a quantum economic advantage for the companies that develop them.”

According to the results, the quantum computing company can not only be more profitable but also invest more in market creation even if the companies are involved in identical calculations without quantum advantage and the quantum computer is more expensive to scale.

They conclude, “The results suggest that quantum computers may not need to display a quantum advantage to be able to generate a quantum economic advantage for the companies that develop them.”

The researchers define quantum advantage as a point when a quantum computer can perform a calculation that a classical computer is unable to complete.

The research team includes Francesco Bova and Avi Goldfarb, both of the University of Toronto, and Roger G. Melko, of the University of Waterloo and the Perimeter Institute.

The Future of Materials Discovery: Reducing R&D Costs significantly with GenMat’s AI and Machine Learning Tools

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Matt Swayne

With a several-decades long background in journalism and communications, Matt Swayne has worked as a science communicator for an R1 university for more than 12 years, specializing in translating high tech and deep tech for the general audience. He has served as a writer, editor and analyst at The Quantum Insider since its inception. In addition to his service as a science communicator, Matt also develops courses to improve the media and communications skills of scientists and has taught courses. [email protected]

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The Future of Materials Discovery: Reducing R&D Costs significantly with GenMat’s AI and Machine Learning Tools

When: July 13, 2023 at 11:30am

What: GenMat Webinar

Jake Vikoren

Jake Vikoren

Company Speaker

Deep Prasad

Deep Prasad

Company Speaker

Araceli Venegas

Araceli Venegas

Company Speaker

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