arXiv subjects

Hiroshi Ohno

Publications and source records attributed to Hiroshi Ohno.

At least 19 recordsLinked to original sources

arXiv2026

Bias-Corrected Machine-Learning Estimation of Chiral Condensate Cumulants: A Retrospective Lattice QCD Case Study

We present a retrospective case study of bias-corrected machine learning (ML) estimates of traces of the inverse Dirac operator, $\text{Tr}\,M^{-n}$ ($n=1,2,3,4$), using a fixed lattice QCD dataset and examining how the results depend on the relative proportions of the labeled and training sets. Two supervised learning approaches are examined: one using $\text{Tr}\,M^{-1}$ as the input feature, and the other employing gauge observables such as the plaquette and rectangle. Beyond the direct estimation of $\text{Tr}\,M^{-n}$, we further investigate two derived applications of the ML estimations: the evaluation of the cumulants of the chiral condensate within a single ensemble and that obtained through multi-ensemble reweighting across ensembles with different quark masses. Within this fixed dataset, the bias-corrected estimates show close agreement with the full-data reference under the adopted evaluation criteria, while the uncorrected estimates can exhibit amplified deviations after the nonlinear cumulant and reweighting steps. For the approach using $\text{Tr}\,M^{-1}$ as the input feature, nominal solve-count accounting suggests that the Dirac-inversion cost could be reduced to approximately $25.75\%$ of that of the conventional calculation in the present setup. This value is a cost projection rather than an end-to-end benchmark: it assumes comparable costs for successive inversions and excludes model-training and analysis overhead.

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arXiv2026

Scaling Behavior of Parameterized Quantum Circuits from a Lie-Algebraic Perspective

Understanding how the performance of parameterized quantum circuits scales with available resources is important for characterizing their trainability and effective model capacity. In this study, we numerically investigate data scaling, model scaling, and compute scaling in parameterized quantum circuits and examine Lie-algebraic quantities as alternative measures of model size. In addition to the number of circuit parameters, we consider the dimension of the dynamical Lie algebra, the observable-orbit dimension, and a Jacobian effective dimension defined as the rank of the Jacobian of the parameterized observable orbit. Using a regression task with randomly generated Pauli-string generators, we observe decreasing loss with increasing training dataset size, parameter size, and number of optimization iterations over the ranges investigated. For model scaling, the dynamical Lie algebra and observable orbit dimensions rapidly saturate as the parameter size increases, whereas the Jacobian effective dimension remains strongly correlated with the parameter size and exhibits comparable scaling behavior. These results suggest that the Jacobian effective dimension provides a geometry-aware measure of the locally accessible observable degrees of freedom of finite-depth parameterized quantum circuits and may serve as a useful scaling parameter beyond the nominal parameter size.

quant-ph↗

arXiv2026

Lie-Group Mode Connectivity in Quantum Machine Learning from a Dynamical Lie Algebra Perspective

Mode connectivity has been widely studied in classical machine learning as a geometric property of low-loss regions in parameter space. In quantum machine learning (QML), however, the physically relevant object is not the parameter vector itself but the unitary transformation implemented by a parameterized quantum circuit. In this study, we formulate mode connectivity on the reachable unitary Lie group generated by the dynamical Lie algebra of the generators. We show that, under a near-minimum connectedness assumption and the absence of critical values in a low-loss band, the corresponding low-loss sublevel set on the reachable Lie group is path-connected. This provides a geometric interpretation of mode connectivity in QML that is independent of a particular parameterization. We further discuss how overparameterization can enable the lifting of Lie-group paths to parameter space, thereby making Lie-group connectivity observable in parameter-space experiments. Finally, we present toy numerical experiments in which geodesic interpolations between trained unitaries exhibit nearly zero loss barriers, consistent with the proposed interpretation.

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arXiv2026

A regularization method for quantum neural networks using data symmetry

Leveraging data symmetries has recently become a key strategy in quantum neural networks (QNNs) to improve training efficiency. In this study, we propose a symmetry-informed regularization method for QNNs based on an input density matrix. By introducing a penalty term that encourages the model to align with data symmetry, our method enables improved training speed. This symmetry-based regularization is simple to implement and does not require an explicitly specified symmetry group, although it requires access to training samples or to the input distribution from which an empirical or theoretical input density matrix can be constructed. We evaluate the method through numerical experiments on both classification tasks and quantum generative adversarial networks. In the small-scale classification experiments, the regularizer produced modest improvements in early-stage convergence and test loss. Our findings highlight the potential of symmetry-aware regularization in enhancing the performance of QML models.

quant-ph↗

arXiv2026

Observable-Guided Generator Selection for Improving Trainability in Quantum Machine Learning with a $ \mathfrak{g} $-Purity Interpretation under Restricted Settings

To study generator design for parameterized unitaries in quantum machine learning (QML), we propose an observable-guided generator selection algorithm for $ n $-qubit Pauli-string generator pools. The proposed method selects generators based on two criteria: maintaining large first-order sensitivity in the gradients and suppressing second-order interference in the Hessian matrix. Under a restricted setting with Pauli-string observables and candidate generators, the selection problem can be formulated as a binary optimization problem that favors mutually anti-commuting generators. Numerical experiments on a synthetic dataset with a small-scale five-qubit circuit show that the selected generators yield faster training than random generator selection in our setting, while exhibiting similar expressibility. Furthermore, under additional algebraic assumptions, the proposed criteria admit an interpretation in terms of the $ \mathfrak{g} $-purity of the observable: the first-order sensitivity is proportional to the $ \mathfrak{g} $-purity, whereas the second-order interference, namely the off-diagonal elements of the Hessian matrix, is upper-bounded by it. These results suggest that observable-guided generator selection is a promising direction for improving trainability in restricted QML settings.

quant-ph↗

arXiv2026

Rademacher Complexity Bounds for Parameterized Quantum Circuits Generated by Pauli Strings

In this study, we analyze the Rademacher complexity $ \mathcal{R}_{M} $ of a parameterized unitary whose generators are chosen from $ n $-qubit Pauli strings. Although generalization bounds for quantum machine learning models have been studied in several settings, explicit Rademacher-complexity bounds for parameterized unitaries generated by Pauli strings remain less transparent. We derive simple scaling bounds in terms of the number of parameters $ L $ and the number of training samples $ M $: $ \mathcal{O}(\frac{L^{\frac{3}{2}}}{\sqrt{M}}) $ for the full parameter domain and $ \mathcal{O}(\frac{L}{\sqrt{M}}) $ for a restricted parameter domain. Furthermore, we compare the obtained results with those for a classical linear model class and suggest a potential statistical-complexity advantage when the norms of both the input and the parameter in the classical model scale with the number of parameters. Numerical experiments provide qualitative evidence consistent with the predicted scaling.

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arXiv2026

A putative model of the gut-muscle axis in aged livestock

The gut-muscle axis has been proposed to link gut microbiota with skeletal muscle physiology, yet its universality across livestock species remains unclear. Using aged laying hens, a livestock model with a relatively short digestive tract, we examined the gut microbiota, faecal metabolome, and breast-muscle metabolome by integrative multi-omics analyses in hens fed a Caldifermentibacillus hisashii-containing fermented feed or a control diet. Non-metric multidimensional scaling revealed clear separation of the microbial community between groups (stress = 0.0097), characterised by a marked expansion of Lactobacillus with the administration of the fermented feed. Variance partitioning showed that the 16S microbiota shared substantial variance with both the faecal (shared R2 adj = 0.54) and muscle (shared R2 adj = 0.48) metabolomes, and partial dbRDA demonstrated that the faecal-to-muscle metabolite association was largely retained after controlling for 16S (direct R2 = 0.538, partial R2 = 0.485), consistent with faecal metabolites acting as an integral layer linking microbiota to muscle. Cliff's delta-based selection showed depletion of proteolytic taxa and faecal amino acids, and reduced muscle Ornithine and uric acid alongside elevated Hypoxanthine. Because both groups were processed identically post-slaughter, these differences reflect in vivo states: amino acid depletion despite reduced bacterial proteolytic capacity points to enhanced host utilisation, and reduced uric acid, a post-mortem-stable purine end-product in uricotelic chickens, indicates efficient nitrogen turnover rather than accumulation. Collectively, these findings support a putative tripartite model of the gut-muscle axis in aged laying hens, providing a statistically grounded framework for understanding microbial contributions to muscle physiology in aged livestock.

q-bio.TO↗

arXiv2026

A novel sustainable role of compost as a universal protective substitute for fish, chicken, pig, and cattle, and its estimation by structural equation modeling

Natural decomposition of organic matter is essential in food systems, and compost is used worldwide as an organic fermented fertilizer. However, as a feature of the ecosystem, its effects on the animals are poorly understood. Here we show that oral administration of compost and/or its derived thermophilic Bacillaceae, i.e., Caldibacillus hisashii and Weizmannia coagulans, can modulate the prophylactic activities of various industrial animals. The fecal omics analyses in the modulatory process showed an improving trend dependent upon animal species, environmental conditions, and administration. However, structural equation modeling (SEM) estimated the grouping candidates of bacteria and metabolites as standard key components beyond the animal species. In particular, the SEM model implied a strong relationship among partly digesting fecal amino acids, increasing genus Lactobacillus as inhabitant beneficial bacteria and 2-aminoisobutyric acid involved in lantibiotics. These results highlight the potential role of compost for sustainable protective control in agriculture, fishery, and livestock industries.

q-bio.QM↗

arXiv2026

Approximate Cosine Similarity Estimation via an Angle-Encoding Hadamard Test

The Hadamard test is a standard quantum primitive for estimating inner products and expectation values, but in data-processing settings its practical utility is often limited by the cost of preparing amplitude-encoded quantum states. In this study, we investigate an angle-encoding variant of the Hadamard test for estimating cosine similarity between normalized real-valued vectors. The proposed method decomposes the similarity computation into elementwise two-qubit Hadamard-test circuits that can, in principle, be executed in parallel, resulting in constant circuit depth with respect to the vector dimension at the expense of a larger qubit footprint and classical post-processing. Because the resulting estimator is approximate, we analyze the induced bias and show that it is non-negative under the approximation used in our derivation. Numerical experiments on random normalized vectors show that, in the tested setting, the estimation error decreases as the vector dimension increases. We further illustrate a possible application to cosine-attention-based Transformer models. These results suggest that the angle-encoding Hadamard test may provide a useful design point for near-term similarity estimation when shallow circuit depth is preferred over compact qubit usage.

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arXiv2026

A Machine Learning Approach for Lattice Gauge Fixing

Gauge fixing is an essential step in lattice QCD calculations, particularly for studying gauge-dependent observables. Traditional iterative algorithms are computationally expensive and often suffer from critical slowing down and scaling bottlenecks on large lattices. We present a novel machine learning framework for lattice gauge fixing, where Wilson lines are utilized to construct gauge transformation matrices within a convolutional neural network. The model parameters are optimized via backpropagation, and we introduce a hybrid strategy that combines a neural-network-based transformation with subsequent iterative methods. Preliminary tests on SU(3) gauge theory ensembles for Coulomb gauge demonstrate the potential of this approach to improve the efficiency of lattice gauge fixing. Furthermore, we show that the model exhibits lattice size transferability, where parameters optimized on smaller lattices remain effective for larger volumes without additional training. This framework provides a scalable path toward mitigating critical slowing down in high-precision gauge fixing.

hep-lat↗

arXiv2026

Machine Learning-Based Estimation of Cumulants of Chiral Condensate via Multi-Ensemble Reweighting with Deborah.jl

We investigate a bias-corrected machine learning (ML) strategy for estimating traces of the inverse Dirac operator, $\text{Tr}\, M^{-n}$ ($n=1,2,3,4$), motivated by the need for higher-order cumulants of the chiral condensate near the finite-temperature QCD critical endpoint. Our supervised regression framework is trained on Wilson-clover ensembles with the Iwasaki gauge action, and we explore two input feature scenarios: one using $\text{Tr}\, M^{-1}$ and another relying solely on gauge observables (plaquette and rectangle), enabling a fully feature-based prediction pipeline. Using $\text{Tr}\, M^{-1}$ both as a physical input to cumulant construction and as a feature for predicting higher powers, we find that even with $\sim1\%$ labeled data, the resulting susceptibility, skewness, and kurtosis remain statistically consistent with fully measured baselines, reducing computational cost to about $26\%$. In the feature-only approach, where correlations rather than explicit stochastic traces drive the predictions, bias correction plays a more pronounced role. We quantify this impact through multi ensemble reweighting across nearby quark masses. Our results demonstrate that bias-corrected ML estimates can significantly reduce measurement overhead while preserving the stability of higher-order observables relevant for locating the QCD critical endpoint. Code for this work is available at https://github.com/saintbenjamin/Deborah.jl .

hep-lat↗

arXiv2026

Lattice Gauge Theory via LLVM-Level Automatic Differentiation

We enable the automatic construction of Hybrid Monte Carlo (HMC) forces in lattice gauge theory by performing reverse-mode automatic differentiation at the level of optimized LLVM intermediate representation, making the approach applicable to any language that lowers lattice action code to LLVM. In practice, this means that once the action evaluation routine is implemented, the corresponding HMC force can be generated automatically from the same code path, without deriving or maintaining a separate force routine. The method preserves conventional imperative, in-place implementations and enables a single-source workflow in which forces are generated directly from the action code while inheriting compiler optimizations. We perform end-to-end reverse-mode differentiation of both gauge and Wilson fermion actions. For the Wilson fermion case, we find that the force generated by automatic differentiation achieves performance comparable to a conventional hand-written fermion force implementation. The same differentiation pipeline targets both CPU and GPU backends, providing a practical route to performance-portable force construction for compositional lattice actions.

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arXiv2026

Sparse modeling study of extracting charmonium spectral functions from lattice QCD at finite temperature

We present charmonium spectral functions extracted from Euclidean-time correlation functions using sparse modeling (SpM). SpM solves inverse problems by considering only the sparsity of the target solution. To assess the applicability of the method, we first test it with mock data designed to mimic charmonium correlation functions. We demonstrate that while resonance peaks in the spectral functions can be reconstructed using this method, transport peaks are difficult to resolve without introducing further assumptions beyond sparsity. We then apply the method to charmonium correlation functions obtained from lattice QCD at temperatures below and above the critical temperature. The results are found to be qualitatively consistent with those obtained using the maximum entropy method, although the transport peak is not clearly resolved. This indicates that, even when relying solely on the assumption of sparsity, the method can capture some relevant features of the underlying physics.

hep-lat↗

arXiv2026

Lie-Algebraic Analysis of Generators: Approximation-Error Bounds and Barren-Plateau Heuristics

Lie algebras provide a useful framework for theoretical analysis in quantum machine learning, particularly in hybrid quantum-classical learning. From the viewpoint of function approximation, expectation values of parameterized quantum circuits can be viewed as trigonometric polynomials whose accessible Fourier modes are determined by the spectra of the generators. In this study, we describe: (1) a minimax lower bound on the $ L^{2} $-approximation error over a Sobolev ball when the circuit's effective frequency set is contained in a radius-$K$ ball, which yields a scaling law of the form $ Ω(K^{\frac{d}{2} - r}) $ for $ r > \frac{d}{2} $ (assuming the target function belongs to the Sobolev space $ W_2^{r}(\mathbb{T}^{d}) $), and we also derive a Jackson-type upper bound on the approximation error of quantum circuits under Sobolev regularity of the target function, expressed in terms of an effective bandwidth determined by generator spectral gaps; (2) a generator-selection rule motivated by enlarging the effective frequency set via non-commuting generators; and (3) a simple heuristic metric based on the trace component of generators, aimed at characterizing training behaviors related to barren plateaus. Simulation experiments on toy problems illustrate the practical implications of the frequency-spectrum perspective and the proposed heuristics.

quant-ph↗

arXiv2025

Symbiotic causal network of seagrass-bacteria-algae-diatoms interactions

Seagrass meadows contribute to the conservation of marine ecosystems, reduction in global warming impacts and pathogen controls. However, the decline in seagrass habitats due to environmental loads has become an urgent global issue. One way to address this issue is to better understand healthy seagrass habitats. Here, we estimate the structural characteristics of symbiotic and metabolic systems in sediments from eight coastal regions of Japan, with each region containing both seagrass-covered areas and adjacent unvegetated areas. Notably, seagrasses commonly maintain a balanced symbiotic relationship characterized by a positive association with cable bacteria (Desulfobulbaceae), nitrogen-cycling bacteria (Hyphomonadaceae), and coral algae (Corallinophycidae) and a negative association with diatoms (Diatomea). Furthermore, seagrass growth conditions influence metabolic pathways by activating nitrogen-related metabolism while attenuating methanogenesis. Our findings highlight the crucial roles of marine plants and their symbiotic systems in ensuring environmental conservation within the context of blue carbon storage across environmental gradients.

q-bio.QM↗

arXiv2025

Anti-pathogenic property of thermophile-fermented compost as a feed additive and its in vivo external diagnostic imaging in a fish model

Fermentative recycling of organic matter is important for a sustainable society, but the functionality of fermented products needs to be adequately evaluated. Here, we clarify the antipathogenic properties for fish of a compost-type feed additive fermented by thermophilic Bacillaceae using non-edible marine resources as raw materials. After prior administration of the compost extract to seabream as a fish model for 70 days, the mortality rate after 28 days of exposure to the fish pathogen Edwardsiella reached a maximum of 20%, although the rate was 60% without prior administration. Under such conditions, the serum complement activity of seabream increased, and the recovery time after anesthesia treatment was also fasten. Furthermore, the differences in the degree of smoothness and glossiness of the fish body surface depending on the administration were statistically shown by imaging techniques to evaluate the texture and color tone of field photographs. These results suggest that thermophile-fermented compost is effective as a functional feed additive against fish disease infection, and that such conditions can be estimated by body surface analysis. This study provides a new perspective for the natural symbiosis industry, as well as for the utilization of non-invasive diagnosis to efficiently estimate the quality of its production activities

q-bio.QM↗

arXiv2025

Generalization analysis of quantum neural networks using dynamical Lie algebras

The paper presents a generalization bound for quantum neural networks based on a dynamical Lie algebra. Using covering numbers derived from a dynamical Lie algebra, the Rademacher complexity is derived to calculate the generalization bound. The obtained result indicates that the generalization bound is scaled by O(sqrt(dim(g))), where g denotes a dynamical Lie algebra of generators. Additionally, the upper bound of the number of the trainable parameters in a quantum neural network is presented. Numerical simulations are conducted to confirm the validity of the obtained results.

quant-ph↗

arXiv2025

CASK: A Gauge Covariant Transformer for Lattice Gauge Theory

We propose a Transformer neural network architecture specifically designed for lattice QCD, focusing on preserving the fundamental symmetries required in lattice gauge theory. The proposed architecture is gauge covariant/equivariant, ensuring it respects gauge symmetry on the lattice, and is also equivariant under spacetime symmetries such as rotations and translations on the lattice. A key feature of our approach lies in the attention matrix, which forms the core of the Transformer architecture. To preserve symmetries, we define the attention matrix using a Frobenius inner product between link variables and extended staples. This construction ensures that the attention matrix remains invariant under gauge transformations, thereby making the entire Transformer architecture covariant. We evaluated the performance of the gauge covariant Transformer in the context of self-learning HMC. Numerical experiments show that the proposed architecture achieves higher performance compared to the gauge covariant neural networks, demonstrating its potential to improve lattice QCD calculations.

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