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CQ, Nippon Steel Scientists Forging Path to Algorithms That Can Simulate Materials on Today’s Quantum Computers

Steel
Steel
Cambridge Quantum, Nippon Steel scientists say algorithms may offer today’s quantum computers a job in the steel industry — and that may just be the beginning.

Finding use cases that work on today’s quantum computers is an important step to prove the value of quantum computing. Now a team of researchers from Cambridge Quantum and Nippon Steel report an important advance in that direction.

In a paper published on the pre-print server ArXiv, the team said they were able to accurately simulate hydrogen and iron using algorithms and noise mitigation techniques that could be run on today’s quantum computers. Prior to this, most experiments for material discovery run on today’s machines focused on molecules. Simulating an infinite iron lattice in two phases (where the molecules adopt different physical structures), is so complex it is considered to be inaccessible using classical computational methods.

Today’s quantum computers — often referred to as NISQ, or noisy intermediate-scale quantum computers — are vulnerable to environmental noise, which make them less effective than some classical computers. Although, as these experiments show, that may be beginning to change.

According to the team, scientists — particularly materials scientists and steel makers — simulating periodic infinite iron crystals may prove valuable because carbon is differently soluble in the various phases of iron. This could then lead to to the discovery new types of steel through quantum computer modeling.

The work also establishes a pathway for future investigations. Scientists could use quantum computers to study ways to improve the efficiency of batteries, for example, or better understand superconductivity to help us create safer, more environmentally friendly materials.

They add that it’s not just an advance for industry, but an advance for science. Undiscovered characteristics of iron under high pressure may also hold secrets about the formation and internal nature of our own planet.

The researchers have also confirmed a chemistry-specific noise mitigation method that exploits the symmetries of the system and does not consume quantum resources.

This work also established a potential framework for quantum chemical calculations that can be developed and advanced as quantum hardware continues to improve.

The team was led by Dr. David Muñoz Ramo. They used IBM’s Quantum Falcon Processors for the computations.

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

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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

Jake Vikoren

Jake Vikoren

Company Speaker

Deep Prasad

Deep Prasad

Company Speaker

Araceli Venegas

Araceli Venegas

Company Speaker

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