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I have been studying the interdisciplinary field between quantum physics and information science, including representation and classification of physical states using neural networks and application of NISQ (noisy intermediate quantum) devices.
In a broad sense, my current interest resides in quantum many-body states using classical data processing.
Refer to Publications and Presentations for further references.
Quantum error countermeasures
Quantum error countermeasures
In quantum computing at a non-asymptotic scale, whether in NISQ or FTQC, it is necessary to efficiently address computational errors within algorithms. We are developing methods that are the easiest to implement, the most effective, and involve minimal overhead.
[Quantum error mitigation] PRL 2022, arXiv 2023, PRL 2024
[Limitation in quantum error mitigation] PRL 2023
[Random compilation] arXiv 2024
[Error countermeasures in early FTQC] PRA 2023, arXiv 2024, arXiv 2024 (AQIS poster award)
Schematics of Generalized Quantum Subspace Expansion proposed in Yoshioka et al., PRL (2022).
Quantum Algorithms
Quantum Algorithms
From a computational theoretical perspective, it is known that the dynamics of quantum systems can be efficiently simulated by quantum computers. A natural question that arises is how complex more general quantum simulations, including the extraction of information from dynamics, would be. With the aim of applying these simulations to fields such as condensed matter physics, statistical physics, and quantum chemistry, we are advancing the development of quantum algorithms.
[NISQ algorithms] PRR 2020, PRR 2022, New J Phys 2024, arXiv 2023
[Large scale many-body experiments] arXiv 2024
[FTQC algorithms] arXiv 2024 (AQIS2024 oral), arXiv 2024
Large-scale experiment of 2d Heisenberg model studied in collaboration with IBM in Yoshioka et al. (2024)
Fault-tolerant quantum computation
Fault-tolerant quantum computation
Quantum error correction is a method to protect quantum information from local noise by regarding a globally entangled quantum state as a single bit. A quantum computer equipped with error correction functionality is called a Fault-Tolerant Quantum Computer, or FTQC. Realizing FTQC is one of the holy grail in the field of quantum technology. Meanwhile, it remains imperfect to assess how we benefit from FTQC; our interest is to reveal what is the impact for physical science in a practical sense.
[Resourse estimation] npj Quantum Info 2024
[Assessment of magic resource] Quantum 2024, arXiv 2024
[FTQC algorithms] arXiv 2024 (AQIS2024), arXiv 2024
Required resource to achieve quantum advantage using FTQC, pointed out in Yoshioka et al., npj Quantum Info (2024).
Quantum Randomness
Quantum Randomness
Quantum randomness underpins a wide range of fields, from quantum information science, computational complexity theory, to many-body physical phenomena. Among these, the impact of symmetry on randomness and whether it deepens information processing or physical phenomena remains largely unknown. Our research aims to quantitatively understand randomness through quantum circuits and advance its application in quantum information processing and quantum computing algorithms.
[Interplay of randomness and symmetry] PRXQ 2023, arXiv 2024, arXiv 2024
[Application to quantum computing algorithm] arXiv 2024
Machine learning and Physics
Machine learning and Physics
One of the reasons for the success of deep learning is the high expressive power of artificial neural networks as nonlinear functions. The probability distributions corresponding to physical states are generally expected to differ significantly from the probability distributions inherent in datasets used in machine learning. However, surprisingly, it has recently become clear that neural networks are capable of capturing the characteristics in data produced from natural science experiments, simulations, and calculations. The goal of our research is to further expand the frontier of state representations and establishing efficient numerical analysis methods for many-body systems.
[Machine Learning Quantum Phases] PRB 2018
[Neural network representation of classical system] PRE 2019
[Neural network representation of quantum system] PRB 2019, PRL 2021, Commun. Phys. 2021
[Quantum control by machine learning] PRApp 2023
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