25th Anniversary of QIP

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In 1981, at a conference in Boston, physicist Richard Feynman proposed that computers that exploit quantum mechanical phenomena could easily perform calculations that classical computers would struggle to do—even extremely difficult calculations.

In 1994, Bell Labs mathematician Peter Shor showed that a quantum computer (still a completely hypothetical device) could factor numbers exponentially faster than a classical computer. "Shor's algorithm constitutes a killer application that interests everyone," MIT quantum computing researcher Seth Lloyd once said .

Thomas Vidick (left), professor of computational and mathematical sciences at Caltech and chair of the 25th Annual Conference on Quantum Information Processing, and Simone Severini (right), director of quantum computing at Amazon Web Services.

Three years later, in 1998, the first conference on quantum information processing (QIP) was held in Aarhus, Denmark. Since then, quantum computing has become a major research initiative for leading technology companies, and QIP has become the premier conference in quantum information processing.

In honor of QIP's 25th anniversary, Amazon Science asked two distinguished quantum information scientists (Caltech professor of computational and mathematical sciences, Thomas Vidick, this year's QIP chair, and Simone Severini, director of quantum computing at Amazon Web Services), about how the field has worked in the past The question of how much progress has been made in 25 years and how far it still has to go.

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What has surprised you the most about what we have learned about quantum information science over the past 25 years?

Thomas Vidick: Well, honestly, we can run a 20-qubit quantum algorithm and it actually seems to be going according to plan. While my entire research is based on the assumption that quantum mechanics is a sufficiently accurate description of nature that it makes sense to study its impact on computation, it is a revelation to actually "see" such computations happening. (I need to use quotes because of course we can't see quantum computing happening without affecting it. But for small computations we can plot the resulting statistics in great detail. A few years ago, Monroe's group implemented Simon using an ion trap Algorithm . I can't believe it: it samples the correct string exactly.

Going back not even 25 years ago, but 15 years ago, this was the first time I learned that quantum computing was a thing while doing my masters, and the fact that it could become a reality was definitely not on my mind, nor did I Don't believe most theorists, let alone experimentalists. I think understanding that quantum computing works, rather than believing it does, has a major impact on the way we approach quantum information science.

Ernesto F. Galvão, Head of the Quantum and Linear Optical Computation Group at the International Iberian Laboratory for Nanotechnology; Iordanis Kerenidis, Head of Quantum Algorithms at QC Ware, Senior Researcher, CNRS, Director of the Centre for Quantum Computing in Paris; and Severini in 2001 At the 4th QIP in Amsterdam.

(Provided by SIMONE SEVERINI)

Simone Severini: Quantum information science facilitates the rich interplay between physics, mathematics and computing. This interaction produces new technologies that cross the boundaries of these fields.

A good example is the application of quantum complexity theory, in 2020 by Ji, Natarajan, Vidick, Wright, and Yuen to solve the Connes embedding problem in negation. Connes's embedding problem is a problem in abstract algebra, where an "algebra" is a combination of a set, a set of operators, and axioms that describe how the operators are applied. Real numbers are an example of a set, and arithmetic operators are an example of a set of operators, but in abstract algebra these can be anything.

Connes's question is to ask whether one class of algebra is contained in another. Alain Connes addressed this problem in a paper in 1976 that won the Fields Medal in 1982. Since then, the problem has been reformulated in several different branches of mathematics. Several conferences have been devoted to this issue.

The results of Ji et al. are a surprising case in which concepts and techniques that are part of the quantum information science toolbox have implications in other areas of mathematics and the natural sciences. This is just one of many exciting examples.

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What do you think are the biggest challenges in this field?

Thomas Vidick: The obvious challenges facing the field are, on the experimental side, to realize quantum computers, especially reducing the error rate while scaling up the system, and on the theoretical side, to find applications for such computers. While as a theorist I tend to think of the first as a difficult but definitely solvable engineering challenge, I am less confident in the end result of the second: other than niche applications in quantum simulation and post-quantum Beyond the widespread deployment of cryptography, will quantum computers enter everyday consumer life?

This is a multi-billion dollar question. But to be honest, it's not my primary concern. Closer to my heart, and perhaps less obvious, is the importance of maintaining the coherence, dynamism, and impact that quantum information science has had over the past quarter century, and the next (and more!) challenge. When I reviewed the first QIP programs, few were concerned about the near-term applicability of the theoretical results. By contrast, I probably didn't overestimate too much, asserting that nearly half of QIP's science programs over the past few years had some "near-term" motivation.

“In the complex and fast-paced world of today, we should not forget that fundamental science is still the root of future innovation.”

--Simone Severin

This evolution reflects a genuine and legitimate enthusiasm for the potential practical implications of our work as researchers, a prospect so distant 25 years ago that it wasn't even on our minds. It remains to be seen what impact this evolution will have on the health and diversity of our field. Will the QIP be split into "applied" and "theoretical" QIP, and if so, will this split be done in a way that maintains a strong interaction between the two components? Will theoretical work on quantum information retain its strength and status in the computer science community, regardless of the success or failure of experimental approaches?

Researchers in our field have been fighting, with great success, to demonstrate the importance of the idea of quantum information, far exceeding its possible practical relevance. Now that the latter is becoming a reality, we should not forget the former.

Simone Severini: It's fascinating to see how quantum information science spills over from academia to industry. The broader interest we're seeing in this space today is a great opportunity, but there are risks. I think the biggest non-technical challenge in the field is to grow organically and steadily in an environment that tries to balance scientific research and engineering, while coming up with a business route that has future impact. In today's complex and fast-paced world, we should not forget that basic science remains the source of future innovation. To realize the long-term promise of quantum technologies, such as processors and communication devices that can surpass classical engineering, it is important to set the right expectations today. In this context, supporting educational and scientific discovery and emphasizing the need for a long-term vision is critical.

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Article Author: Larry Hardesty

Larry Hardesty is the editor of the Amazon Science blog. Previously, he was a senior editor for the MIT Technology Review and a computer science writer for the MIT News Office.