Kaito Kobayashi

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Over the past decade, information-based approaches have delivered fresh insights into quantum
phenomena. In this talk, we push this direction further by examining many-body physics through
the lens of information processing.
The starting point is quantum reservoir computing (QRC) [1, 2], a quantum machine learning
paradigm in which the natural dynamics of a quantum system (“quantum reservoir”) act as a
feature map for information processing. Training is limited to the classical post-processing stage;
the quantum reservoir itself remains fixed. Consequently, the computational performance of QRC
serves as a direct fingerprint of the underlying quantum many-body system.
By reversing this perspective, we introduce quantum reservoir probing (QRP) [3, 4], a framework
that uses computational performance as a probe of many-body physics. We illustrate QRP with
two case studies: (i) information propagation, where QRP discerns distinct propagation dynamics
reflecting the system’s inherent nature [3], and (ii) quantum phase transitions, where enhanced
critical fluctuations leave clear signatures in the computational performance [4]. These examples
show that QRP is a versatile tool for probing a wide range of exotic quantum many-body
phenomena.
[1] K. Fujii and K. Nakajima, Phys. Rev. Applied 8, 024030 (2017).
[2] K. Kobayashi, K. Fujii, N. Yamamoto, PRX Quantum 5, 040325 (2024).
[3] K. Kobayashi and Y. Motome, SciPost Phys. 18, 198 (2025).
[4] K. Kobayashi and Y. Motome, Nat. Commun. 16, 3871 (2025).

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University of Tokyo
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Contact: Yoshito Watanabe / Simon Trebst