38 citations · 148 across the 17 of their papers we have counts for
17 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…
Efficient Quantum Information-Inspired Ansatz for Variational Quantum Eigensolver Algorithm: Applications to Atomic Systems
Abdul Kalam, Prasenjit Deb, Akitada Sakurai +3
We present a quantum information-inspired ansatz for the variational quantum eigensolver (VQE) and demonstrate its efficacy in calculating ground-state energies of atomic systems.…
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…
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…
Time crystal embodies chimeralike state in periodically driven quantum spin system
Mahbub Rahaman, Akitada Sakurai, Analabha Roy
Chimera states are a captivating occurrence in which a system composed of multiple interconnected elements exhibits a distinctive combination of synchronized and desynchronized beh…
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…
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…
Quantum Neuronal Sensing of Quantum Many-Body States on a 61-Qubit Programmable Superconducting Processor
Ming Gong, He-Liang Huang, Shiyu Wang +33
Classifying many-body quantum states with distinct properties and phases of matter is one of the most fundamental tasks in quantum many-body physics. However, due to the exponentia…
Quantum reservoir computation utilising scale-free networks
Akitada Sakurai, Marta P. Estarellas, William J. Munro +1
Today's quantum processors composed of fifty or more qubits have allowed us to enter a computational era where the output results are not easily simulatable on the world's biggest…
Dephasing-induced growth of discrete crystalline order in spin networks
Akitada Sakurai, Victor M. Bastidas, Marta P. Estarellas +2
A quantum phase of matter can be understood from the symmetry of the system's Hamiltonian. The system symmetry along the time axis has been proposed to show a new phase of matter r…
Relativistic many body theory of the electric dipole moment of Xe and its implications for probing new physics beyond the Standard Model
Akitada Sakurai, B. K. Sahoo, K. Asahi +1
We report the results of our theoretical studies of the time-reversal and parity violating electric dipole moment (EDM) of Xe arising from the nuclear Schiff moment (NSM) a…
Enhanced sensitivity of the electron electric dipole moment from YbOH: The role of theory
V. S. Prasannaa, N. Shitara, A. Sakurai +2
The prospect of laser cooling of polyatomic molecules has opened a new avenue in the search for the electric dipole moment of the electron (eEDM). An upper bound on the eEDM would…
Studies of the Electric Dipole Polarizability of Xe using the Relativistic Coupled Cluster and the Normal Coupled Cluster Methods
Akitada Sakurai, B. K. Sahoo, B. P. Das
Xe is an important candidate for the searches of electric dipole moments due to violations of time-reversal and parity violations in diamagnetic atoms. In view of the simil…