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Toshiba Brings Quantum-Inspired Optimization Solution to AWS Marketplace

Toshiba

Insider Brief

  • Toshiba Digital Solutions Corporation (TDSL) announced the launch of “SQBM+ for AWS.”
  • The service includes SQBM+ software customized for the AWS Marketplace.
  • The company reports the module provides solutions to customers in fields as diverse as finance, drug development, genetic engineering, logistics and AI.

Toshiba Digital Solutions Corporation (TDSL), an industry leader in applying quantum-inspired solutions to complex real-world problems, announced the launch of “SQBM+ for AWS,” a version of its SQBM+ software customized for the AWS Marketplace operated by Amazon Web Services, Inc.

SQBM+ is optimization software1 that quickly finds good solutions among an immense number of options. It is derived from the Simulated Bifurcation Machine (“SBM”), a combinatorial optimization solver built on the quantum-inspired2 Simulated Bifurcation Algorithm (“SB Algorithm”)3 developed by Toshiba Corporation.

TDSL released a free, proof-of-concept (PoC) version of SBM on the AWS Marketplace in July 2019. Since then, working with universities, research institutions and other companies, TDSL has investigated real problems in combinatorial optimization, toward finding solutions to social issues in a range of fields. The PoC version has been used by hundreds of subscribers worldwide, and much of the knowledge obtained through its application has been brought into SQBM+. This has triggered requests for the incorporation of SQBM+ into customer systems for application in business.

Providing SQBM+ as a software module will allow customers who need to solve combinatorial optimization problems, in fields as diverse as finance, drug development, genetic engineering, logistics, and AI, to incorporate SQBM+ into their business applications and systems.

Combinatorial optimization is an essential tool for taking on many social and industrial challenges, and finding optimal answers from an enormous range of choices. Its many spheres of application include financial decision-making5, the movement of industrial robots, logistics and transmission routes, and drug discovery at the molecular level6. High speed combinatorial optimization on current computers is very difficult, because combination patterns increase exponentially as the scale of a problem grows. To overcome this, dedicated combinatorial optimization computers are being developed around the world.

SQBM+ runs on current computers and discovers high-precision, approximate solutions (good solutions) to complex, large-scale problems in a short time. Going forward, TDSL will apply SQBM+ in many fields and contribute to solving complicated social issues.

For more market insights, check out our latest quantum computing news here.

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

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Jake Vikoren

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Deep Prasad

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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

Picture of Jake Vikoren

Jake Vikoren

Company Speaker

Picture of Deep Prasad

Deep Prasad

Company Speaker

Picture of Araceli Venegas

Araceli Venegas

Company Speaker

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