9 papers
Quantum computer-based simulation of Stark many-body localization in a 1D Fermi-Hubbard model
Abdul Kalam, Prasenjit Deb, Akitada Sakurai +3
Many-body localization (MBL) is a dynamical phenomenon that describes the non-ergodicity of isolated quantum many-body systems. In contrast to thermalization, this phenomenon leads…
Beyond Optimization: Harnessing Quantum Annealer Dynamics for Machine Learning
Akitada Sakurai, Aoi Hayashi, Tadayoshi Matsumori +3
Quantum annealing is typically regarded as a tool for combinatorial optimization, but its coherent dynamics also offer potential for machine learning. We present a model that encod…
Quantum Random Features: A Spectral Framework for Quantum Machine Learning
Akitada Sakurai, Aoi Hayashi, William John Munro +1
Quantum machine learning (QML) models often require deep, parameterized circuits to capture complex frequency components, limiting their scalability and near-term implementation. W…
Quantum Extreme Reservoir Computing for Phase Classification of Polymer Alloy Microstructures
Arisa Ikeda, Akitada Sakurai, Kae Nemoto +1
Quantum machine learning (QML) is expected to offer new opportunities to process high-dimensional data efficiently by exploiting the exponentially large state space of quantum syst…
Photonic Quantum-Accelerated Machine Learning
Markus Rambach, Abhishek Roy, Alexei Gilchrist +4
Machine learning is widely applied in modern society, but has yet to capitalise on the unique benefits offered by quantum resources. Boson sampling -- a quantum-interference based…
Modular quantum extreme reservoir computing
Hon Wai Lau, Aoi Hayashi, Akitada Sakurai +2
Quantum reservoir computing employs fixed quantum dynamics as a feature map for machine learning. Integrating multiple quantum reservoirs, however, raises a key question: how few i…