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Quantum Computing

Quantum computing is a new paradigm that aims to introduce the next era of computing speed and power; it does so by leveraging the phenomena of quantum physics to create new kinds of computing elements that will revolutionize how computers process information.

Over the past five decades, the integrated circuit computing paradigm has powered many technological breakthroughs. However, the computing power we’re able to fit into existing computer chips is reaching its limit, and the hardest problems in fields like software verification and validation (VV), materials science, or machine learning and artificial intelligence (ML and AI), can’t be solved by existing technology.

Quantum technology could represent the leap forward in computing necessary to solve these kinds of problems as well as many others, such as developing AI for advanced autonomous systems. It could also provide the means for simulating complex, chemical and biological systems for the production of advanced materials, such as metals, polymers, and hybrids, to support advanced aeronautics, or to advance the state of biotechnology to develop new vaccines and treatments for many diseases.

Currently, however, the development of quantum hardware is still in its infancy, and the field finds itself in what researchers call the era of the NISQ, or noisy intermediate scale quantum processing unit. The elements of quantum computers are still unstable, or “noisy,” because they flip to different states. In these early days of quantum computing research, it is still difficult to tell when quantum computing might deliver the capabilities that early research promises are possible.

Achieving Quantum Advantage

At the SEI, we are investigating whether quantum algorithms and computers can serve as the next paradigm that will produce new breakthroughs in computing speed and power, and we are investigating when and how we might arrive at such breakthroughs.

A major part of this research is to predict when and how quantum computers might demonstrate quantum advantage. Quantum advantage refers to a quantum computer obtaining a solution more quickly, or obtaining a better-quality solution, than a classical computer for a problem with practical relevance. The SEI is investigating several algorithms to predict the advent of quantum advantage and its hardware requirements.

To help achieve quantum advantage, the SEI is working with NISQ devices to benchmark optimization techniques, improve circuit generation for NISQ-era QPUs, and address the challenges of scaling up quantum computing hardware. We are also developing software tools to help data scientists and engineers use quantum computers.

As the SEI works through these problems, we are committed to leveraging our expertise and experience to help advance the field. We are beginning to work with universities across the U.S. to help build curricula to transfer the knowledge we’ve built to national and global software and engineering communities. Recently, Daniel Justice—a software developer working on quantum computing in the SEI’s Emerging Technology Center—collaborated with faculty at Carnegie Mellon University (CMU) to co-create and co-teach a course in quantum computing at CMU’s prestigious School of Computer Science.

In addition to contributing to the improvement of education in the field, the SEI is also working to promote greater communication among researchers and institutions. As part of our collaboration with CMU, we have established Quantum Hub (quantum.etchub.xyz/hub/login), a central location for researchers everywhere to collect and share information about leading work. We want to make Quantum Hub into a collaborative space where the SEI and CMU research communities can push quantum computing research forward to hasten the arrival of useful applications.

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Our Vision for the Future of Quantum Computing

As we continue to explore problems in the field of quantum computing, we plan to extend our work to new applications, including studying quantum machine learning involving quantum algorithms to perform machine learning and artificial intelligence tasks. In addition, we plan to work on quantum interactive proof systems, using QPUs to form interactive proof systems, verifying and validating quantum computation, and possibly extending our work to the study of quantum-classical networks for cryptographic uses.

Contact us to collaborate on these problems or to discuss whether quantum computing can benefit your organization in the near or long term.

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