arXiv subjects

Akitada Sakurai

Publications and source records attributed to Akitada Sakurai.

15 recordsLinked to original sources

arXiv2026

Quantum computer-based simulation of Stark many-body localization in a 1D Fermi-Hubbard model

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 to a long-lived memory of initial states of local systems and slow growth of entanglement. In this work, we study Stark MBL in a 12-qubit correlated fermionic system described by the one-dimensional Fermi-Hubbard model using Hamiltonian simulation on an IBM superconducting qubit quantum computer. To enable such a computation on current-day noisy hardware, we combine a series of compilation steps, including the use of the spin-resolved Jordan-Wigner transformation, employing SWAP networks, and integrating a tensor-network-based quantum circuit optimization routine on top of a standard circuit optimization pipeline. As a result, there is approximately an 88$\%$ and 87$\%$ reduction in two-qubit gate count and circuit depth, respectively. Through such simulations of the real-time dynamics using Trotterized quantum circuits, we exhibit a crossover from thermalizing dynamics of the system at a weak tilt of the field to a strongly localized behavior at large tilt with short evolution times. We also benchmark our obtained results with respect to those from exact simulations.

quant-ph↗

arXiv2026

Quantum Random Features: A Spectral Framework for Quantum Machine Learning

Quantum machine learning (QML) models often require deep, parameterized circuits to capture complex frequency components, limiting their scalability and near-term implementation. We introduce \textit{Quantum Random Features} (QRF) and \textit{Quantum Dynamical Random Features} (QDRF), lightweight quantum reservoir models inspired by classical random Fourier features (RFF) that generate high-dimensional spectral representations without variational optimization. Using $Z$-rotation encoding combined with random permutations or Hamiltonian dynamics, these models achieve $N_f$-dimensional feature maps at preprocessing cost $O(\log(N_f))$. Spectral analysis shows that QRF and QDRF reproduce the behavior of RFF, while simulations on Fashion-MNIST reach up to 89.3\% accuracy-matching or surpassing classical baselines with scalable qubit requirements. By linking spectral theory with experimentally feasible quantum dynamics, this work provides a compact and hardware-compatible route to scalable quantum learning.

quant-ph↗

arXiv2026

Beyond Optimization: Harnessing Quantum Annealer Dynamics for Machine Learning

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 encodes classical data into an Ising Hamiltonian, evolves it on a quantum annealer, and uses the resulting probability distributions as feature maps for classification. Experiments on the quantum annealer machine with the Digits dataset, together with simulations on MNIST, demonstrate that short annealing times yield higher classification accuracy, while longer times reduce accuracy but lower sampling costs. We introduce the participation ratio as a measure of the effective model size and show its strong correlation with generalization.

quant-ph↗

arXiv2026

Quantum Extreme Reservoir Computing for Phase Classification of Polymer Alloy Microstructures

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 systems. In this work, we apply quantum extreme reservoir computing (QERC) to the classification of microstructure images of polymer alloys generated using self-consistent field theory (SCFT). While previous QML efforts have primarily focused on benchmark datasets such as MNIST, our work demonstrates the applicability of QERC to engineering data with direct materials relevance. Through numerical experiments, we examine the influence of key computational parameters-including the number of qubits, sampling cost (the number of measurement shots), and reservoir configuration-on classification performance. The resulting phase classifications are depicted as phase diagrams that illustrate the phase transitions in polymer morphology, establishing an understandable connection between quantum model outputs and material behavior. These results illustrate QERC performance on realistic materials datasets and suggest practical guidelines for quantum encoder design and model generalization. This work establishes a foundation for integrating quantum learning techniques into materials informatics.

quant-ph↗

arXiv2025

Photonic Quantum-Accelerated Machine Learning

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 sampling protocol -- is a resource that is classically hard to simulate and can be implemented on current quantum hardware. Here, we present a quantum accelerator for classical machine learning, using boson sampling to provide a high-dimensional quantum fingerprint for reservoir computing. We show robust performance improvements under various conditions: imperfect photon sources down to complete distinguishability; scenarios with severe class imbalances, classifying both handwritten digits and biomedical images; and sparse data, maintaining model accuracy with twenty times less training data. Crucially, we demonstrate the acceleration of our scheme on a photonic quantum processing unit, providing experimental validation that boson-sampling-enhanced learning with Fock states delivers real performance gains on actual quantum hardware.

quant-ph↗

arXiv2025

Efficient Quantum Information-Inspired Ansatz for Variational Quantum Eigensolver Algorithm: Applications to Atomic Systems

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. Instead of adopting a heuristic approach, we start with an approximate multi-qubit target state and utilize two quantum information-theoretic quantities, i.e., von Neumann entropy and quantum mutual information, to construct our ansatz. The quantum information encoded in the target state helps us to design unique blocks and identify qubit pairs that share maximum quantum correlations among them in the multi-qubit system, thereby enabling us to deterministically place two-qubit entanglers in the suitably constructed parametrized quantum circuit. We find that our approach has the advantage of reduced circuit depth compared to the unitary coupled-cluster (UCC) ansatz (the gold standard for VQE), and yet yields accurate results. To test the performance of our ansatz, we apply it to compute ground-state energies of atomic systems. We find that for up to 12 qubits (or 12 spin orbitals) noiseless calculation, the proposed ansatz yields energies with 99.99% accuracy relative to the complete active space configuration interaction values, while utilizing only two blocks, which contain at most 99% fewer 2-qubit gates than the UCC ansatz.

quant-ph↗

arXiv2024

Modular quantum extreme reservoir computing

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 inter-module connections are sufficient to match the performance of a single reservoir? To address this, we explicitly separate intra-module dynamics from inter-module couplings and systematically examine different connectivity schemes. We find that even a small number of well-placed connections between two modules can match single-reservoir accuracy, with simple one-to-one connections proving highly effective. Performance generally improves with increasing inter-module entanglement, and these correlations persist for both $ZZ$-coupled and random modular reservoirs. Extensions to three modules and evaluations across multiple datasets (MNIST, Fashion-MNIST, CIFAR-10) suggest that the modular architecture can be applied to diverse reservoir types and image-classification datasets. These results motivate modular quantum reservoir designs that align naturally with realistic hardware, such as two-dimensional quantum-chip layouts or networks of small integrated quantum systems.

quant-ph↗

arXiv2024

Time crystal embodies chimeralike state in periodically driven quantum spin system

Chimera states are a captivating occurrence in which a system composed of multiple interconnected elements exhibits a distinctive combination of synchronized and desynchronized behavior. The emergence of these states can be attributed to the complex interdependence between quantum entanglement and the delicate balance of interactions among system constituents. The emergence of discrete-time crystal (DTC) in typical many-body periodically driven systems occurs when there is a breaking of time translation symmetry. Coexisting coupled DTC and a ferromagnetic dynamically many-body localized (DMBL) phase at distinct regions have been investigated under the controlled spin rotational error of a disorder-free spin-1/2 chain for different types of spin-spin interactions. We contribute a novel approach for the emergence of the DTC-DMBL-chimeralike state, which is robust against external static fields in a periodically driven quantum many-body system.

cond-mat.stat-mech↗

arXiv2024

Impact of the form of weighted networks on the quantum extreme reservoir computation

The quantum extreme reservoir computation (QERC) is a versatile quantum neural network model that combines the concepts of extreme machine learning with quantum reservoir computation. Key to QERC is the generation of a complex quantum reservoir (feature space) that does not need to be optimized for different problem instances. Originally, a periodically-driven system Hamiltonian dynamics was employed as the quantum feature map. In this work we capture how the quantum feature map is generated as the number of time-steps of the dynamics increases by a method to characterize unitary matrices in the form of weighted networks. Furthermore, to identify the key properties of the feature map that has sufficiently grown, we evaluate it with various weighted network models that could be used for the quantum reservoir in image classification situations. At last, we show how a simple Hamiltonian model based on a disordered discrete time crystal with its simple implementation route provides nearly-optimal performance while removing the necessity of programming of the quantum processor gate by gate.

quant-ph↗

arXiv2024

Simple Hamiltonian dynamics is a powerful quantum processing resource

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 from simulations of many-body physics to complex financial modeling. Despite few examples and demonstrations, it is still not clear how we can utilize such a large Hilbert space as a computational resource; in particular, how a simple and small quantum system could solve non-trivial computational tasks. In this paper, we show a simple Ising model capable of performing such non-trivial computational tasks in a quantum neural network model. An Ising spin chain as small as ten qubits can solve a practical image classification task with high accuracy. To evaluate the mechanism of its computation, we examine how the symmetries of the Hamiltonian would affect its computational power. We show how the interplay between complexity and integrability/symmetries of the quantum system dictates the performance as quantum neural network.

quant-ph↗

arXiv2022

Quantum Neuronal Sensing of Quantum Many-Body States on a 61-Qubit Programmable Superconducting Processor

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 exponential complexity that emerges from the enormous numbers of interacting particles, classifying large-scale quantum states has been extremely challenging for classical approaches. Here, we propose a new approach called quantum neuronal sensing. Utilizing a 61 qubit superconducting quantum processor, we show that our scheme can efficiently classify two different types of many-body phenomena: namely the ergodic and localized phases of matter. Our quantum neuronal sensing process allows us to extract the necessary information coming from the statistical characteristics of the eigenspectrum to distinguish these phases of matter by measuring only one qubit. Our work demonstrates the feasibility and scalability of quantum neuronal sensing for near-term quantum processors and opens new avenues for exploring quantum many-body phenomena in larger-scale systems.

quant-ph↗

arXiv2021

Quantum reservoir computation utilising scale-free networks

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 supercomputers. What we have not seen yet, however, is whether or not such quantum complexity can be ever useful for any practical applications. A fundamental question behind this lies in the non-trivial relation between the complexity and its computational power. If we find a clue for how and what quantum complexity could boost the computational power, we might be able to directly utilize the quantum complexity to design quantum computation even with the presence of noise and errors. In this work we introduce a new reservoir computational model for pattern recognition showing a quantum advantage utilizing scale-free networks. This new scheme allows us to utilize the complexity inherent in the scale-free networks, meaning we do not require programing nor optimization of the quantum layer even for other computational tasks. The simplicity in our approach illustrates the computational power in quantum complexity as well as provide new applications for such processors.

quant-ph↗

arXiv2021

Dephasing-induced growth of discrete crystalline order in spin networks

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 referred as discrete-time crystals (DTCs). A DTC is a quantum phase of matter in non-equilibrium systems, and it is also intimately related to the symmetry of the initial state. DTCs that are stable in isolated systems are not necessarily resilient to the influence from the external reservoir. In this paper, we discuss the dynamics of the DTCs under the influence of an environment. Specifically, we consider a non-trivial situation in which the initial state is prepared to partly preserve the symmetry of the Liouvillian. Our analysis shows that the entire system evolves towards a DTC phase and is stabilised by the effect of dephasing. Our results provide a new understanding of quantum phases emerging from the competition between the coherent and incoherent dynamics in dissipative non-equilibrium quantum systems.

quant-ph↗

arXiv2019

Relativistic many body theory of the electric dipole moment of $^{129}$Xe and its implications for probing new physics beyond the Standard Model

We report the results of our theoretical studies of the time-reversal and parity violating electric dipole moment (EDM) of $^{129}$Xe arising from the nuclear Schiff moment (NSM) and the electron-nucleus tensor-pseudotensor (T-PT) interaction based on the self-consistent and the normal relativistic coupled-cluster methods. The important many-body effects are highlighted and their contributions are explicitly presented. The uncertainties in the calculations of the correlation and relativistic effects are determined by estimating the contributions of the triples excitations, and the Breit interaction respectively, which together amount to about 0.7% for the NSM and 0.2% for the T-PT interactions. The results of our present work in combination with improved experimental limits for $^{129}$Xe EDM in the future would tighten the constraints on the hadronic CP violating quantities, and this could provide important insights into new physics beyond the Standard Model of elementary particles.

physics.atom-ph↗

arXiv2018

Studies of the Electric Dipole Polarizability of $^{129}$Xe using the Relativistic Coupled Cluster and the Normal Coupled Cluster Methods

$^{129}$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 similarities between the electric dipole moment and the polarizability from the point of view of many-body theory, we have performed rigorous calculations of the ground state polarizability of $^{129}$Xe using a self consistent relativistic coupled-cluster method and the relativistic normal coupled-cluster method. The discrepancy between the results from these two methods is two percent, but each of them differs from the accurate measurement of the polarizability of the ground state of $^{129}$Xe by one percent. Our results clearly suggest that the two theoretical methods, we have employed in this work, can be applied in the future to capture electron correlation effects in the electric dipole moment of $^{129}$Xe to a high degree of accuracy.

physics.atom-ph↗