arXiv subjects

Aoi Hayashi

Publications and source records attributed to Aoi Hayashi.

6 recordsLinked to original sources

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 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↗

arXiv2025

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↗

arXiv2025

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↗

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↗

arXiv2021

Competing insulating phases of dipolar bosons in a dimerized optical lattice

We study the ground state properties of dipolar bosons in a one dimensional dimerized optical lattice. In the limit of strong onsite repulsion i.e. hardcore bosons, and strong dipole-dipole interaction, a stable density wave (DW) phase is obtained at half filling as a function of lattice dimerization. Interestingly, at quarter filling we obtain the signatures of an insulating phase which has both the character the bond-order (BO) and the DW insulators which we call a bond-order density wave (BODW) phase. Moreover, we show that for a fixed hopping dimerization there occurs a BO-DW phase crossover as a function of the dipole-dipole interaction and the BODW phase is more robust when the hopping dimerization is stronger. We further examine the stability of the BODW phase in the limit of finite onsite interactions.

cond-mat.quant-gas↗