6 papers · 1 filter
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…
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…
Equilibration of Non-interacting Photons and Quantum Signatures of Chaos
V. M. Bastidas, H. L. Nourse, A. Sakurai +4
Equilibration plays a fundamental role in our understanding of statistical mechanics and the long-time dynamics of many-body systems. In quantum systems, the route to equilibration…
Simple Hamiltonian dynamics is a powerful quantum processing resource
Akitada Sakurai, Aoi Hayashi, William John Munro +1
A quadrillion dimensional Hilbert space hosted by a quantum processor with over 50 physical qubits has been expected to be powerful enough to perform computational tasks ranging fr…
Impact of the form of weighted networks on the quantum extreme reservoir computation
Aoi Hayashi, Akitada Sakurai, Shin Nishio +2
The quantum extreme reservoir computation (QERC) is a versatile quantum neural network model that combines the concepts of extreme machine learning with quantum reservoir computati…