Biography

I work as a Senior Quantum Applied Research Scientist in NVIDIA Inc. Previously I was a postdoctoral researcher at the University of Toronto’s Matter Lab (Alan Aspuru-Guzik group), specializing in quantum computing and its applications. I began my researcher career at Keio University and the Keio Quantum Computing Center in 2020, subsequently earning my Ph.D. in 2022. I possess strong expertise in machine learning engineering as well, backed by over five years of experience in the industry prior to my academic career.

My recent researches

I have strong interests in near-term realization of practical quantum computing applications. Quantum computing devices have been developed rapidly. In particular, the recent achievement of 40+ logical qubits and the introduction of new QLDPC error-correcting codes have significantly impacted the quantum computing community. However, given the challenge of reducing the logical error rate, the realization of deterministic algorithms, such as phase estimation and quantum simulation, remains a distant goal.

In light of this, I am convinced that contributions from the software side remain crucial for the early realization of quantum utility. My current interests in this domain include (i) AI for quantum and (ii) adapting long-term algorithms for more near-term use.

(i) AI for quantum

The variational quantum algorithm (VQA), executable in near-term devices, has significantly attracted attention when proposed a decade ago, but its scalability issues, such as the barren plateau issue, hinder its practical applications. I strongly believe that utilizing the power of classical machine learning is the key to circumvent this situation. To show the significance of this possibility, we have recently posted the paper: https://arxiv.org/abs/2401.09253. In the paper, we propose the Generative Quantum Eigensolver (GQE) utilizing the generative model of quantum circuits and its pre-trainable Transformer implementation (GPT-QE). Please check our paper for the details.

AI can also help optimizing the physical process in quantum devices for a given quantum computation. I am also tackling on the projects combining physics and AI.

(ii) Adapting long-term algorithms for more near-term use

Reducing the number of gates required for long-term quantum algorithms is crucial not only for improving time complexity but also for enhancing their near-term applicability. In line with this, we have recently posted papers for Hamiltonian simulation PRX Quantum 5, 020330 and for solving differential equations https://arxiv.org/abs/2306.11802.

For the other activities, please see blog.