Co-authors Unveil Interdisciplinary Research Agenda in Applied Physics Letters
Palo Alto, Calif. – October 19, 2020 – NTT Research, Inc., a division of NTT (TYO:9432), today announced that Dr. Yoshihisa Yamamoto, the Director of its Physics and Informatics (PHI) Lab, along with colleagues at several academic institutions, has proposed an interdisciplinary research agenda that amounts to a new field of academic study. Their proposal, which arises in the course of addressing a fundamental research problem, appears in an article titled “Coherent Ising Machines: Quantum optics and neural network perspectives,” published as a “Perspectives” cover article in Applied Physics Letters (APL) – (117 (16) (2020)). The collaborating authors from Stanford University are Drs. Surya Ganguli and Hideo Mabuchi, associate professor and professor, respectively, of applied physics in the School of Humanities and Sciences at Stanford University.
A Coherent Ising Machine (CIM) is a special-purpose processor designed to address particularly difficult types of problems that can be mapped to an Ising model, such as combinatorial optimization problems. The Ising model, named after the physicist Ernst Ising, consists of variables that represent interacting spins, i.e. forms of a fundamental particle’s angular momentum. A CIM is actually a network of optical parametric oscillators (OPOs) and solves problems by finding the spin configuration that minimizes a problem’s Ising energy function. (Here is a visualization from MIT’s Lincoln Laboratory of how a CIM resolves the textbook combinatorial optimization problem of the traveling salesperson; potential current applications range from logistics to medicine to machine learning and beyond.) One condition for the optimal spin state is that it occur well above the lasing threshold, the point at which optical gain of the laser is balanced against its losses. A basic problem of the CIM, however, is that when the laser pump rate is increased from below to above threshold, the machine may be prevented from relaxing to true ground state, for reasons related to the behavior of eigenvectors with minimum values. This article explores two approaches to that problem. The first involves coherent spreading over local minima via quantum noise correlation; the second, implementing real-time error correction feedback. In their discussion of these approaches, the authors offer various perspectives based on a range of interdisciplinary viewpoints that span quantum optics, neural networks and message passing.
“Along the way,” write the co-authors in the article, “we will touch upon connections between the CIM and foundational concepts spanning the fields of statistical physics, mathematics and computer science, including dynamical systems theory, bifurcation theory, chaos, spin glasses, belief propagation and survey propagation.”
One reason for engaging in a cross-pollination of ideas across classical, quantum and neural approaches to combinatorial optimization is that, to date, CIM studies could be characterized as primarily experimentally-driven. “Large-scale measurement feedback coupling coherent Ising machine (MFB-CIM) prototypes constructed by NTT Basic Research Laboratories are reaching levels of computational performance that, in a fundamental sense, we do not really understand,” write the authors. That situation stands in marked contrast to that of mainstream quantum computing, in which laboratory efforts have lagged behind theoretical analyses.
“We look forward to accelerated advancement of learning in both the theoretical and experimental studies of CIMs,” said Dr. Yoshihisa Yamamoto, director of the PHI Lab at NTT Research, and one of the article’s co-authors. “Although there is no well-defined method for launching a new academic field of study, we see many rich possibilities for future interdisciplinary research, focused around a multifaceted theoretical and experimental approach to combinatorial optimization that unites perspectives from statistics, computer science, statistical physics and quantum optics, and we are grateful to the editors of APL for providing a forum from which to launch this proposal.”
A publication of AIP Publishing, a wholly owned, not-for-profit subsidiary of the American Institute of Physics (AIP), APL features concise, up-to-date reports on significant new findings in applied physics. “Perspectives are a new invitation-only article type for the journal, seeking personal views and scientific directions from experts in the field,” said APL Editor-in-Chief Lesley F. Cohen. “We are absolutely delighted that Dr. Yamamoto and his colleagues accepted our invitation to produce their fascinating and timely Perspective article on this emerging and important topic.”
The NTT Research PHI Lab has itself already cast a wide net, as part of its long-range goal to radically redesign artificial computers, both classical and quantum. It has established joint research agreements with seven universities, one government agency and quantum computing software company, covering a wide range of topics. Those universities are California Institute of Technology (CalTech), Cornell University, Massachusetts Institute of Technology (MIT), Notre Dame University, Stanford University, Swinburne University of Technology and the University of Michigan. The government entity is NASA Ames Research Center in Silicon Valley, and the private company is 1QBit.
About NTT Research
NTT Research opened its Palo Alto offices in July 2019 as a new Silicon Valley startup to conduct basic research and advance technologies that promote positive change for humankind. Currently, three labs are housed at NTT Research: the Physics and Informatics (PHI) Lab, the Cryptography and Information Security (CIS) Lab, and the Medical and Health Informatics (MEI) Lab. The organization aims to upgrade reality in three areas: 1) quantum information, neuro-science and photonics; 2) cryptographic and information security; and 3) medical and health informatics. NTT Research is part of NTT, a global technology and business solutions provider with an annual R&D budget of $3.6 billion.
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