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Hiroshi Yamauchi

Publications and source records attributed to Hiroshi Yamauchi.

At least 19 recordsLinked to original sources
arXiv2026

From Rubble Simulation to Active Magnetic Mapping: Quantum Sensing for Disaster Response

Locating survivors of building collapses within the first 72 hours is a critical challenge in disaster response, and existing sensing modalities provide only partial information about the structure beneath the rubble. This paper proposes drone-based quantum magnetometry as a complementary modality and develops a simulation pipeline spanning rubble physics, sensor-array deployment, and active spatial reconstruction. We use Unreal Engine to generate a steel-reinforced concrete parking-garage collapse and compute the induced magnetic field via a per-triangle dipole approximation, establishing that meaningful magnetic structure is recoverable in the sub-pT to sub-nT range from roughly 1 m above the roofline. Then, we feed sparse multi-sensor samples into a Gaussian Process Regression back-end driven by Bayesian active sampling and validate the pipeline across multiple independent collapse realizations; a three-sensor array optimizes the trade-off between gradient resolution and UAV payload constraints, and active sampling reaches peak structural correlation in roughly $100$ samples. Together, these results indicate that quantum-grade sensing could become a useful tool for drone-based structural analysis and potentially void detection in collapsed buildings.

cs.RO↗
arXiv2026

Quantum-Inspired Contextual Learning for Sparse-Ring Fraud Detection in Dynamic Transaction Graphs

We present an exploratory benchmark and quantum-inspired modeling prototype for fraud screening in dynamic financial transaction graphs. Coordinated fraud may not be visible from individual transactions alone, but may emerge as a multi-period relational pattern. We focus on sparse-ring fraud, a stylized pattern in which a completed directed cycle is distributed across several days, requiring models to integrate evidence across both time and graph structure. We study this problem using a synthetic transaction simulator with completed sparse-ring injections and broken-ring decoys. Daily directed transaction graphs are aggregated into rolling windows and represented using raw graph features, persistent-homology summaries, or hybrid feature vectors that combine both. We compare a gated recurrent unit (GRU) baseline with quantum-inspired Contextual Machine Learning (CML) as sequence-level classifiers. Because the benchmark uses synthetic data, a modest sample size, and sequence-level labels, the results are exploratory. Within this scope, topology-only summaries are too compressed to solve the supervised ring-completion task by themselves, largely because they remove account-pair identity and edge direction. The strongest results come from hybrid representations that combine identity-preserving graph features with topological summaries. These findings suggest that topology is most useful as a contextual layer over dynamic graph features, and that CML is a promising candidate model for fraud patterns whose evidence is distributed across temporal and relational context.

cs.LG↗
arXiv2026

Gauge Geometry of Hodge Zero-Mode Transport in Parameter-Dependent Topological Data Analysis

We propose a practical computational framework for detecting structural changes in parameter-dependent topological data. In many applications, such as time-series data analysis, anomaly detection, and monitoring of systems under changing control parameters, persistence diagrams describe the birth and death of topological features at each parameter value, but they do not fully capture how these features are reorganized over time. To address this limitation, we represent homological features by zero modes of the ordinary combinatorial Hodge Laplacian and track the corresponding feature spaces in a common ambient chain space. This allows us to compute curvature and holonomy as descriptors of local reorganization and accumulated memory in evolving topological structures. Curvature highlights parameter regions where homological features mix or change rapidly, while holonomy summarizes the net effect of such changes after a closed cycle. We also establish stability estimates showing that these descriptors are robust under perturbations of the Hodge Laplacian on regular regions. Numerical experiments on controlled time-dependent point-cloud data show that the proposed method detects tracking instability, distinguishes systems with nearly identical persistence diagrams, and captures cycle-level memory invisible to pointwise feature matching. These results suggest that zero-mode transport geometry can serve as a useful computational tool for analyzing dynamic topological data.

math.AT↗
arXiv2026

Sequential Spatiotemporal Magnetic-Field Reconstruction via Quantum Hamiltonian Learning with NV-Center Spin-1 Hamiltonians

We propose a quantum-Hamiltonian-learning-based sequential reconstruction framework for dynamic two-dimensional magnetic-field maps using a local likelihood model derived from a nitrogen-vacancy-center spin-1 Hamiltonian. Local measurements are generated through nitrogen-vacancy spin dynamics governed by local magnetic-field values and a shared dipolar coupling parameter, rather than by direct observation of the latent field. Sequential Bayesian updates over overlapping scan windows are combined with temporal posterior propagation to reconstruct the evolving field. Numerical proof-of-concept experiments on controlled synthetic maze-like magnetic-field sequences show that the proposed method reconstructs the dominant spatial structure of the tested field class, achieving a final-frame RMSE of \(7.037\times10^{-7}\,\mathrm{T}\). Adaptive diagnostics show decreasing expected information gain and stable local convergence, while Fisher-information and leakage diagnostics reveal a sensitivity--leakage tradeoff under long-interrogation controls. Combined horizontal and vertical scans yield better reconstruction than single-direction acquisition in the tested setting. In contrast, the shared coupling parameter \(J\) is only partially identifiable: its posterior becomes narrow but remains frame-dependent and biased. At the final checkpoint, \(J_{\rm std}=87.0\,\mathrm{Hz}\), close to a finite-time product-state reference benchmark of \(73.3\,\mathrm{Hz}\), while remaining \(3.35\times\) above a gain-extrapolated ideal-state benchmark. The posterior mean remains biased by \(326.9\,\mathrm{Hz}\), indicating that posterior concentration alone does not imply unbiased coupling recovery. These results demonstrate feasibility for the tested structured field class and identify coupling estimation as the main identifiability bottleneck.

quant-ph↗
arXiv2026

Quantum-Enhanced Processing with Tensor-Network Frontends for Privacy-Aware Federated Medical Diagnosis

We propose a privacy-aware hybrid framework for federated medical image classification that combines tensor-network representation learning, MPC-secured aggregation, and post-aggregation quantum refinement. The framework is motivated by two practical constraints in privacy-aware federated learning: MPC can introduce substantial communication overhead, and direct quantum processing of high-dimensional medical images is unrealistic with a small number of qubits. To address both constraints within a single architecture, client-side tensor-network frontends, Matrix Product State (MPS), Tree Tensor Network (TTN), and Multi-scale Entanglement Renormalization Ansatz (MERA), compress local inputs into compact latent representations, after which a Quantum-Enhanced Processor (QEP) refines the aggregated latent feature through quantum-state embedding and observable-based readout. Experiments on PneumoniaMNIST show that the effect of the QEP is frontend-dependent rather than uniform across architectures. In the present setting, the TTN+QEP combination exhibits the most balanced overall profile. The results also suggest that the QEP behaves more stably when the qubit count is sufficiently matched to the latent dimension, while noisy conditions degrade performance relative to the noiseless setting. The MPC benchmark further shows that communication cost is governed primarily by the dimension of the protected latent representation. This indicates that tensor-network compression plays a dual role: it enables small-qubit quantum processing on compressed latent features and reduces the communication overhead associated with secure aggregation. Taken together, these results support a co-design perspective in which representation compression, post-aggregation quantum refinement, and privacy-aware deployment should be optimized jointly.

quant-ph↗
arXiv2025

Quantum spectroscopy of topological dynamics via a supersymmetric Hamiltonian

Topological data analysis (TDA) characterizes complex dynamics through global invariants, but classical computation becomes prohibitive for high-dimensional data. We reinterpret time-domain dynamics as the eigenvalue spectrum of a supersymmetric (SUSY) Hamiltonian and thereby estimate topological descriptors through quantum spectroscopy. While zero modes correspond to Betti numbers, we show that low-lying excited states quantify the stability of topological features. Using a Takens embedding of the Lorenz system together with a resource-efficient quantum phase estimation implemented on IBM quantum hardware, we observe that the spectral gap of the SUSY Laplacian tracks the persistence of homological structures. Notably, the minimum of this spectral gap coincides with the onset of chaos, whereas its reopening reflects the geometric maturation of the attractor. Validated on small complexes yet offering an exponential advantage over classical diagonalization (from $O(N^3)$ to $\mathrm{poly}(\log N)$), this framework suggests that quantum hardware can function as a spectrometer for data topologies beyond classical reach.

quant-ph↗
arXiv2025

Tensor-based quantum phase difference estimation for large-scale demonstration

We develop an energy calculation algorithm leveraging quantum phase difference estimation (QPDE) scheme and a tensor-network-based unitary compression method in the preparation of superposition states and time-evolution gates. Alongside its efficient implementation, this algorithm reduces depolarization noise affections exponentially. We demonstrated energy gap calculations for one-dimensional Hubbard models on IBM superconducting devices using circuits up to 32-system (plus one-ancilla) qubits, a five-fold increase over previous QPE demonstrations, at the 7242 controlled-Z gate level of standard transpilation, utilizing a Q-CTRL error suppression module. Additionally, we propose a technique towards molecular executions using spatial orbital localization and index sorting, verified linear polyene simulations up to 21 qubits. Since QPDE can handle the same objectives as QPE, our algorithm represents a leap forward in quantum computing on real devices.

quant-ph↗
arXiv2024

Parametrized Energy-Efficient Quantum Kernels for Network Service Fault Diagnosis

In quantum kernel learning, the primary method involves using a quantum computer to calculate the inner product between feature vectors, thereby obtaining a Gram matrix used as a kernel in machine learning models such as support vector machines (SVMs). However, a method for consistently achieving high performance has not been established. In this study, we investigate the diagnostic accuracy using a commercial dataset of a network service fault diagnosis system used by telecommunications carriers, focusing on quantum kernel learning, and propose a method to stably achieve high performance.We show significant performance improvements and an efficient achievement of high performance over conventional methods can be attained by applying quantum entanglement in the portion of the general quantum circuit used to create the quantum kernel, through input data parameter mapping and parameter tuning related to relative phase angles. Furthermore, experimental validation of the quantum kernel was conducted using IBM' s superconducting quantum computer IBM-Kawasaki, and its practicality was verified while applying the error suppression feature of Q-CTRL' s Fire Opal.

quant-ph↗
arXiv2022

3-transposition groups arising in VOA theory

We review 3-transposition groups arising in vertex operator algebra theory. One can construct a commutative algebra called the Matsuo algebra out of a 3-transposition group. Some 3-transposition groups arise as automorphism groups of vertex operator algebras via Matsuo algebras but there exist some 3-transposition groups which do not arise through Matsuo algebras. We will exhibit examples of those groups together with VOAs. This article is based on the author's talk at the seminar on ``Majorana, Axial, Vertex Algebras and the Monster (MAVAM)'' held online on June 4th, 2021.

math.QA↗
arXiv2021

The Conway-Miyamoto correspondences for the Fischer 3-transposition groups

In this paper, we present a general construction of 3-transposition groups as automorphism groups of vertex operator algebras. Applying to the moonshine vertex operator algebra, we establish the Conway-Miyamoto correspondences between Fischer 3-transposition groups $\mathrm{Fi}_{23}$ and $\mathrm{Fi}_{22}$ and $c=25/28$ and $c=11/12$ Virasoro vectors of subalgebras of the moonshine vertex operator algebra.

math.QA↗
arXiv2020

Classification of extremal vertex operator algebras with two simple modules

In recent work, Wang and the third author defined a class of 'extremal' vertex operator algebras (VOAs), consisting of those with at least two simple modules and conformal dimensions as large as possible for the central charge. In this article we show that there are exactly 15 character vectors of extremal VOAs with two simple modules. All but one of the 15 character vectors is realized by a previously known VOA. The last character vector is realized by a new VOA with central charge 33.

math-ph↗
arXiv2019

$\mathbb{Z}_{2k}$-code vertex operator algebras

We study a simple, self-dual, rational, and $C_2$-cofinite vertex operator algebra of CFT-type whose simple current modules are graded by $\mathbb{Z}_{2k}$. Based on those simple current modules, a vertex operator algebra associated with a $\mathbb{Z}_{2k}$-code is constructed. The classification of irreducible modules for such a vertex operator algebra is established. Furthermore, all the irreducible modules are realized in a module for a certain lattice vertex operator algebra.

math.RT↗
arXiv2019

Simple current extensions of tensor products of vertex operator algebras

We study simple current extensions of tensor products of two vertex operator algebras satisfying certain conditions. We establish the relationship between the fusion rule for the simple current extension and the fusion rule for a tensor factor. In a special case, we construct a chain of simple current extensions. We discuss certain irreducible twisted modules for the simple current extension as well.

math.RT↗
arXiv2019

$\mathbb{Z}_k$-code vertex operator algebras

We introduce a simple, self-dual, rational, and $C_2$-cofinite vertex operator algebra of CFT-type associated with a $\mathbb{Z}_k$-code for $k \ge 2$ based on the $\mathbb{Z}_k$-symmetry among the simple current modules for the parafermion vertex operator algebra $K(\mathfrak{sl}_2,k)$. We show that it is naturally realized as the commutant of a certain subalgebra in a lattice vertex operator algebra. Furthermore, we construct all the irreducible modules inside a module for the lattice vertex operator algebra.

math.RT↗
arXiv2018

Vertex operator algebras generated by Ising vectors of $σ$-type

We prove the uniqueness of the simple vertex operator algebra of OZ-type generated by Ising vectors of $σ$-type. We also prove that the simplicity can be omitted if the Griess algebra is isomorphic to the Matsuo algebra associated with the root system of type $A_n$.

math.QA↗
arXiv2016

3-dimensional Griess algebras and Miyamoto involutions

We consider a series of VOAs generated by 3-dimensional Griess algebras. We will show that these VOAs can be characterized by their 3-dimensional Griess algebras and their structures are uniquely determined. As an application, we will determine the groups generated by the Miyamoto involutions associated to Virasoro vectors of our VOAs.

math.QA↗
arXiv2013

On 3-transposition groups generated by $σ$-involutions associated to c=4/5 Virasoro vectors

In this paper, we show that $σ$-involutions associated to extendable c=4/5 Virasoro vectors generate a 3-transposition group in the automorphism group of a vertex operator algebra (VOA). Several explicit examples related to lattice VOA are also discussed in details. In particular, we show that the automorphism group of the VOA $V_{K_{12}}^{\hatν}$ associated to the Coxeter Todd lattice $K_{12}$ contains a subgroup isomorphic to ${}^+Ω^{-}(8,3)$.

math.QA↗