Photonic processors are promising candidates for solving tough mathematical problems. Nature Photonics asked Yoshihisa Yamamoto, director of the Physics and Informatics Laboratories at NTT Research in USA, about the progress that is being made in realizing coherent Ising machines (CIMs). Computing with light | Nature Photonics

Progress in quantum computing is ongoing and various organizations, including technology companies, startups and research institutions, are working on quantum hardware and software. Quantum computers could one day begin to solve problems that are traditionally very difficult for conventional computation platforms. However, it’s important to note that building a stable and scalable quantum computer is an extremely challenging task, and significant technical hurdles remain. Having said that, there are also many ongoing efforts of “quantum-inspired” computers. In these approaches, a simulation of a quantum algorithm is run on classical digital technologies.
These approaches are tuned to particular problems and may become available before real quantum machines hit the market. October 2023 Special Focus: Quantum Computing: Is the Future Already Here? | Disruptive Tech News

I lead NTT Research, Inc. as president and chief executive officer (CEO). In this role, I oversee each of our three Labs – the Physics & Informatics (PHI) Lab, the Cryptography & Information Security (CIS) Lab, and the Medical & Health Informatics (MEI) Lab – and work directly with each director to ensure we are continuing to develop groundbreaking research and innovation. Prior to joining NTT Research, I held several roles within NTT. CIO Influence Interview with Kazuhiro Gomi – NTT Research

The security of today’s public key encryption is based on the fact that huge computational resources are required to solve factoring problems, especially for large integers, says NTT Research CEO, Kazuhiro (Kazu) Gomi. This may no longer be true in the years ahead, he warns. “Shor’s algorithm, running on a scalable quantum computer, will change this environment entirely,” Gomi predicts. With scalable quantum computers, factoring problems will no longer be difficult to achieve, and attackers will be able to determine secret-keys from public-keys. “Once the secret-key is known, the bad actors can complete many different attacks, including pretending to be the legitimate party in exchanging sensitive information,” he notes. Will Quantum Computers Become the Next Cyber-Attack Platform? (informationweek.com)

Autonomous therapeutic systems are one of the most significant future medical technologies. These systems take over patient care from (human) providers by analyzing, determining and autonomously controlling conditions and treatments. Thus, we need to have a precise simulation of a patient’s medical condition—the patient’s bio digital twin. This should reduce human error and medical care costs. – Kazuhiro Gomi, NTT Research 18 New And Emerging Biotech Developments Everyone Should Know About (forbes.com)

Researchers at MIT in the US and the Technische Universitat Berlin have built a optical neural network processor with embedded lasers that slashes the power consumption of large language model AI such as GPT4.0. Chen, Hamerly, and Englund have filed for a patent on the work, which was sponsored by the US Army Research Office and NTT Research in Japan as well as the Volkswagen Foundation in Germany. Optical AI processor to cut data centre power consumption … (eenewseurope.com)

MIT system demonstrates greater than 100-fold improvement in energy efficiency and a 25-fold improvement in compute density compared with current systems. The current work is the latest achievement in a drumbeat of progress over the last few years by Englund and many of the same colleagues. For example, in 2019 an Englund team reported the theoretical work that led to the current demonstration. The first author of that paper, Ryan Hamerly, now of RLE and NTT Research Inc., is also an author of the current paper. Machine-learning system based on light could yield more powerful, efficient large language models | MIT News | Massachusetts Institute of Technology