NTT Research, Inc., a division of NTT, announced that a scientist from its Cryptography and Information Security (CIS) Lab and a colleague from the NTT Social Informatics Laboratories (SIL) have written a pathbreaking paper on quantum advantage. The paper was selected to be presented at the annual IEEE Symposium on Foundations of Computer Science (FOCS), which is taking place Oct. 31–Nov. The co-authors of the paper, titled "Verifiable Quantum Advantage without Structure," are Dr. Takashi Yamakawa, distinguished researcher at NTT SIL and Dr. Mark Zhandry, senior scientist in the NTT Research CIS Lab. The work was done in part at Princeton University, where Dr. Yamakawa was a visiting research scholar and Dr. Zhandry also serves as an assistant professor of computer science. The topic of quantum advantage (or quantum speedup) relates to the kinds of problems that quantum computers can solve faster than classical, or non-quantum, computers and how much faster they are.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Could analog artificial intelligence (AI) hardware – rather than digital – tap fast, low-energy processing to solve machine learning's rising costs and carbon footprint? Researchers say yes: Logan Wright and Tatsuhiro Onodera, research scientists at NTT Research and Cornell University, envision a future where machine learning (ML) will be performed with novel physical hardware, such as those based on photonics or nanomechanics. These unconventional devices, they say, could be applied in both edge and server settings.

"There is no better place to explore the overlap between machine learning and computational neuroscience than the annual NeurIPS event," said Yoshihisa Yamamoto, PHI Lab Director. "We are excited to see the latest paper by Dr. Tanaka and his Stanford colleagues, as well as those by our colleagues at the NTT Software Innovation Center and NTT Communication Science Laboratories and expect the fields of neural networking and machine learning will benefit from the efficiencies and expanded capabilities that they are proposing." This year's seven-day virtual NeurIPS event includes an expo, conference sessions, tutorials and workshops. The authors of these papers will participate in the event through poster and short recorded presentations. A follow-up to the "Pruning Neural Networks" paper, as noted above, will be presented at one of the event's workshops.

WIRE)--NTT Research, Inc., a division of NTT (TYO:9432), today announced that a research scientist in its Physics & Informatics (PHI) Lab, Dr. Hidenori Tanaka, was the lead author on a technical paper that advances basic understanding of biological neural networks in the brain through artificial neural networks. Titled "From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction," the paper was presented at NeurIPS 2019, a leading machine-learning, artificial intelligence (AI) and computational neuroscience conference, and published in Advances in Neural Information Processing Systems 32 (NIPS 2019). Work on the paper originated at Stanford University, academic home of the paper's six authors when the research was performed. At the time, a post-doctoral fellow and visiting scholar at Stanford University, Dr. Tanaka joined NTT Research in December 2019. The underlying research aligns with the PHI Lab's mission to rethink the computer by drawing inspirations from computational principles of neural networks in the brain.