arXiv subjects

Ryo Sakai

Publications and source records attributed to Ryo Sakai.

At least 19 recordsLinked to original sources

arXiv2026

Anomalous Frame Detection by Grouping Frame Similarities between Two Videos Computed by Vision-Language Model to Extract Expert Workers' Unique Actions

Maintenance of critical infrastructures, such as railways and power plants, is essential for operational safety and reliability. However, the declining number of skilled maintenance workers poses a serious challenge to sustaining these operations, highlighting the need to effectively transfer expert know-how to less experienced workers. Although traditional interview-based approaches have been used to elicit maintenance skills, they struggle to capture know-how that experts themselves may not consciously recognize. To address this gap, we proposed a method that detects anomalous frames of candidate actions including know-how by comparing a video of manual-based work with that of expert maintenance workers. In a simulated maintenance experiment involving a distribution board, our method targeted 11 types of actions not described in the manual and achieved a 66.9% extraction rate, marking a 50-percentage-point improvement over conventional techniques. These findings underscore the effectiveness of our approach in revealing hidden maintenance knowledge, thereby contributing to enhanced skill transfer and workforce development in critical infrastructure maintenance.

cs.CV↗

arXiv2026

Anomalous Frame Detection Using VLM-Based Description Comparison for Extracting Expert-Specific Actions and Contextual Decision-Making Scenes with Intra-Video Self-Similarity

Maintenance of critical infrastructures, such as railways and power plants, is essential for ensuring operational safety and reliability. However, the declining number of skilled maintenance workers highlights the need to transfer expert know-how to less experienced workers. Previous studies have attempted to extract candidates of expert knowledge by comparing videos of manual-based work with those of expert workers, mainly focusing on differences in observable actions. However, expert know-how is often embedded not only in actions but also in contextual decision-making during task execution. This paper proposes a method that detects anomalous frames between two task videos to automatically extract candidate scenes containing expert-specific actions and contextual decision-making scenes. The method generates frame-wise visual descriptions using a vision-language model (VLM). Expert-specific actions are extracted based on frame similarities computed from description comparisons between two videos, while contextual decision-making scenes are extracted using segment similarities derived from intra-video self-similarity of the descriptions. In simulated distribution board maintenance experiments involving 27 task scenarios, the proposed method achieved extraction rates of 65% for action candidates and 61% for decision-scene candidates, improving over conventional methods that achieved 59% and 33%, respectively. These results demonstrate the effectiveness of the proposed approach in discovering candidate scenes containing expert know-how.

cs.CV↗

arXiv2026

Quantum hardware noise learning via differentiable Kraus representation on tensor networks

We present a method for learning quantum hardware noise from a measurement distribution of a single device experiment. Each noise channel is represented by automatically differentiable Kraus operators obtained from a Stinespring-based parameterization that is completely positive and trace preserving by construction, and circuits are simulated with a matrix product density operator forward model. Independent channels are attached to each native gate type, to each nearest-neighbor crosstalk interaction, and to state preparation and measurement, and all channels are optimized end-to-end against a distance between the simulated and observed measurement distributions. On ibm_fez, a Heron-generation superconducting processor, training on a ripple-carry adder circuit reproduces the device output distribution, and the same learned parameters, applied without retraining, also track the device distribution of an unrelated multiplier circuit, indicating that the method captures intrinsic device characteristics rather than overfitting to the training circuit. A systematic evaluation across a range of benchmark circuits confirms that this generalization is consistent. We further use the learned model to perform an offline feasibility assessment of the quantum approximate optimization algorithm with an error detection scheme, demonstrating the kind of noise-aware prediction the framework is designed to enable.

quant-ph↗

arXiv2026

Tensor Network Generator-Enhanced Optimization for Traveling Salesman Problem

We present an application of the tensor network generator-enhanced optimization (TN-GEO) framework to address the traveling salesman problem (TSP), a fundamental combinatorial optimization challenge. Our approach employs a tensor network Born machine based on automatically differentiable matrix product states (MPS) as the generative model, using the Born rule to define probability distributions over candidate solutions. Unlike approaches based on binary encoding, which require $N^2$ variables and penalty terms to enforce valid tour constraints, we adopt a permutation-based formulation with integer variables and use autoregressive sampling with masking to guarantee that every generated sample is a valid tour by construction. We also introduce a $k$-site MPS variant that learns distributions over $k$-grams (consecutive city subsequences) using a sliding window approach, enabling parameter-efficient modeling for larger instances. Experimental validation on TSPLIB benchmark instances with up to 52 cities demonstrates that TN-GEO can outperform classical heuristics including swap and 2-opt hill-climbing. The $k$-site variants, which put more focus on local correlations, show better results compared to the full-MPS case.

cs.LG↗

arXiv2025

Enhancing NDAR with Delay-Gate-Induced Amplitude Damping

The Noise-Directed Adaptive Remapping (NDAR) method utilizes amplitude damping noise to enhance the performance of quantum optimization algorithms. NDAR alternates between exploration by sampling solutions from the quantum circuit and exploitation by transforming the cost Hamiltonian by changing the signs of its terms. Both exploration and exploitation are important components in classical heuristic algorithm design. In this study, we examine how NDAR performance improves by adjusting the balance between these components. We control the degree of exploitation by varying the delay time to 0, 50, and $100~\mu\text{s}$, and investigate exploration strategies using two quantum circuits, QAOA and a random circuit, on IBM's Heron processor. Our results show that increasing delay time in NDAR improves the best objective value found in each iteration. In single-layer QAOA and random circuits applied to unweighted Max-Cut problem with low edge density, both exploration strategies yield similar objective value trajectories and provide competitive solution quality to simulated annealing for the 80-node problem. Their similar performance indicates that, in most cases, increasing amplitude damping noise via additional delay time results in information loss. On the other hand, QAOA outperforms random circuits in specific cases, such as positive-negative weighted Max-Cut on a fully connected graph. This suggests potential advantages of QAOA in more complex settings. We further develop a classical NDAR to better understand exploration strategies, demonstrating that controlling the Hamming weight distribution of sampled bitstrings yields higher quality solutions. This suggests that identifying suitable quantum circuits for exploration could enhance NDAR performance.

quant-ph↗

arXiv2025

Transferring linearly fixed QAOA angles: performance and real device results

Quantum Approximate Optimization Algorithm (QAOA) enables solving combinatorial optimization problems on quantum computers by optimizing variational parameters for quantum circuits. We investigate a simplified approach that combines linear parameterization with parameter transferring, reducing the parameter space to just 4 dimensions regardless of the number of layers. This simplification draws inspiration from quantum annealing schedules providing both theoretical grounding and practical advantages. We compare this combined approach with standard QAOA and other parameter setting strategies such as INTERP and FOURIER, which require computationally demanding incremental layer-by-layer optimization. Notably, previously known methods like INTERP and FOURIER yield parameters that can be well fitted by linear functions, which supports our linearization strategy. Our analysis reveals that for the random Ising model, cost landscapes in this reduced parameter space demonstrate consistent structural patterns across different problem instances. Our experiments extend from classical simulation to actual quantum hardware implementation on IBM's Eagle processor, demonstrating the approach's viability on current NISQ devices. Furthermore, the numerical results indicate that parameter transferability primarily depends on the energy scale of problem instances, with normalization techniques improving transfer quality. Most of our numerical experiments are conducted on the random Ising model, while problem-dependence is also investigated across other models. A key advantage of parameter transferring is the complete elimination of instance-specific classical optimization overhead, as pre-trained parameters can be directly applied to other problem instances, reducing classical optimization costs by orders of magnitude for deeper circuits.

quant-ph↗

arXiv2024

Linearly simplified QAOA parameters and transferability

Quantum Approximate Optimization Algorithm (QAOA) provides a way to solve combinatorial optimization problems using quantum computers. QAOA circuits consist of time evolution operators by the cost Hamiltonian and of state mixing operators, and embedded variational parameter for each operator is tuned so that the expectation value of the cost function is minimized. The optimization of the variational parameters is taken place on classical devices while the cost function is measured in the sense of quantum. To facilitate the classical optimization, there are several previous works on making decision strategies for optimal/initial parameters and on extracting similarities among instances. In our current work, we consider simplified QAOA parameters that take linear forms along with the depth in the circuit. Such a simplification, which would be suggested from an analogy to quantum annealing, leads to a drastic reduction of the parameter space from 2p to 4 dimensions with the any number of QAOA layers p. In addition, cost landscapes in the reduced parameter space have some stability on differing instances. This fact suggests that an optimal parameter set for a given instance can be transferred to other instances. In this paper we present some numerical results that are obtained for instances of the random Ising model and of the max-cut problem. The transferability of linearized parameters is demonstrated for randomly generated source and destination instances, and its dependence on features of the instances are investigated.

quant-ph↗

arXiv2024

Tensor Renormalization Group for fermions

We review the basic ideas of the Tensor Renormalization Group method and show how they can be applied for lattice field theory models involving relativistic fermions and Grassmann variables in arbitrary dimensions. We discuss recent progress for entanglement filtering, loop optimization, bond-weighting techniques and matrix product decompositions for Grassmann tensor networks. The new methods are tested with two-dimensional Wilson--Majorana fermions and multi-flavor Gross--Neveu models. We show that the methods can also be applied to the fermionic Hubbard model in 1+1 and 2+1 dimensions.

hep-lat↗

arXiv2023

Symmetry Breaking in an Extended O(2) Model

Motivated by attempts to quantum simulate lattice models with continuous Abelian symmetries using discrete approximations, we study an extended-O(2) model in two dimensions that differs from the ordinary O(2) model by the addition of an explicit symmetry breaking term $-h_q\cos(q\varphi)$. Its coupling $h_q$ allows to smoothly interpolate between the O(2) model ($h_q=0$) and a $q$-state clock model ($h_q\rightarrow\infty$). In the latter case, a $q$-state clock model can also be defined for noninteger values of $q$. Thus, such a limit can also be considered as an analytic continuation of an ordinary $q$-state clock model to noninteger $q$. In previous work, we established the phase diagram for noninteger $q$ in the infinite coupling limit ($h_q\rightarrow\infty$). We showed that there is a second-order phase transition at low temperature and a crossover at high temperature. In this work, we seek to establish the phase diagram at finite values of the coupling using Monte Carlo and tensor methods. We show that for noninteger $q$, the second-order phase transition at low temperature and crossover at high temperature persist to finite coupling. For integer $q=2,3,4$, we know there is a second-order phase transition at infinite coupling (i.e. the well-known clock models). At finite coupling, we find that the critical exponents for $q=3,4$ vary with the coupling, and for $q=4$ the transition may turn into a BKT transition at small coupling. We comment on the similarities and differences of the phase diagrams with those of quantum simulators of the Abelian-Higgs model based on ladder-shaped arrays of Rydberg atoms.

hep-lat↗

arXiv2023

Tensor network representation of non-abelian gauge theory coupled to reduced staggered fermions

We show how to construct a tensor network representation of the path integral for reduced staggered fermions coupled to a non-abelian gauge field in two dimensions. The resulting formulation is both memory and computation efficient because reduced staggered fermions can be represented in terms of a minimal number of tensor indices while the gauge sector can be approximated using Gaussian quadrature with a truncation. Numerical results obtained using the Grassmann TRG algorithm are shown for the case of $SU(2)$ lattice gauge theory and compared to Monte Carlo results.

hep-lat↗

arXiv2022

Quantum Simulation of the N flavor Gross-Neveu Model

We discuss the use of quantum simulation to study an $N$ flavor theory of interacting relativistic fermions in(1+1) dimensions on NISQ era machines. The case of two flavors is particularly interesting as it can be mapped to the Hubbard model. We derive the appropriate qubit Hamiltonians and associated quantum circuits. We compare classical simulation and DMRG/TEBD calculations with the results of quantum simulation on various platforms for $N$=2 and 4. We demonstrate that the four steps of the calculations of real-time scattering can actually be implemented using current NISQ devices.

hep-lat↗

arXiv2022

Symmetry Breaking in an Extended-O(2) Model

Motivated by attempts to quantum simulate lattice models with continuous Abelian symmetries using discrete approximations, we consider an extended-O(2) model that differs from the ordinary O(2) model by an explicit symmetry breaking term. Its coupling allows to smoothly interpolate between the O(2) model (zero coupling) and a $q$-state clock model (infinite coupling). In the latter case, a $q$-state clock model can also be defined for non-integer values of $q$. Thus, such a limit can also be considered as an analytic continuation of an ordinary $q$-state clock model to non-integer $q$. The phase diagram of the extended-O(2) model in the infinite coupling limit was established in our previous work, where it was shown that for non-integer $q$, there is a second-order phase transition at low temperature and a crossover at high temperature. In this work, we investigate the model at finite values of the coupling using Monte Carlo and tensor methods. The results may be relevant for configurable Rydberg-atom arrays.

hep-lat↗

arXiv2022

Improved coarse-graining methods on two dimensional tensor networks including fermions

We show how to apply renormalization group algorithms incorporating entanglement filtering methods and a loop optimization to a tensor network which includes Grassmann variables which represent fermions in an underlying lattice field theory. As a numerical test a variety of quantities are calculated for two dimensional Wilson--Majorana fermions and for the two flavor Gross--Neveu model. The improved algorithms show much better accuracy for quantities such as the free energy and the determination of Fisher's zeros.

hep-lat↗

arXiv2022

The Evolution of Investor Activism in Japan

Activist investors have gradually become a catalyst for change in Japanese companies. This study examines the impact of activist board representation on firm performance in Japan. I focus on the only two Japanese companies with activist board representation: Kawasaki Kisen Kaisha, Ltd. ("Kawasaki") and Olympus Corporation ("Olympus"). Overall, I document significant benefits from the decision to engage with activists at these companies. The target companies experience greater short- and long-term abnormal stock returns following the activist engagement. Moreover, I show operational improvements as measured by return on assets and return on equity. Activist board members also associate with important changes in payout policy that help explain the positive stock returns. My findings support the notion that Japanese companies should consider engagements with activist investors to transform and improve their businesses. Such interactions can lead to innovative and forward-thinking policies that create value for Japanese businesses and their stakeholders.

q-fin.GN↗

arXiv2022

Tensor networks for High Energy Physics: contribution to Snowmass 2021

Tensor network methods are becoming increasingly important for high-energy physics, condensed matter physics and quantum information science (QIS). We discuss the impact of tensor network methods on lattice field theory, quantum gravity and QIS in the context of High Energy Physics (HEP). These tools will target calculations for strongly interacting systems that are made difficult by sign problems when conventional Monte Carlo and other importance sampling methods are used. Further development of methods and software will be needed to make a significant impact in HEP. We discuss the roadmap to perform quantum chromodynamics (QCD) related calculations in the coming years. The research is labor intensive and requires state of the art computational science and computer science input for its development and validation. We briefly discuss the overlap with other science domains and industry.

hep-lat↗

arXiv2021

From tensors to qubits

We discuss recent progress in Tensor Lattice Field Theory and economical, symmetry preserving, truncations suitable for quantum computations or simulations. We focus on spin and gauge models with continuous Abelian symmetries such as the Abelian Higgs model and emphasize noise-robust implementations of Gauss's law. We discuss recent progress concerning the comparison between field digitizations and character expansions, symmetry breaking in tensor language, wave-packet preparation and possible new implementations of Abelian models using Rydberg atoms.

hep-lat↗

arXiv2021

Tensor lattice field theory with applications to the renormalization group and quantum computing

We discuss the successes and limitations of statistical sampling for a sequence of models studied in the context of lattice QCD and emphasize the need for new methods to deal with finite-density and real-time evolution. We show that these lattice models can be reformulated using tensorial methods where the field integrations in the path-integral formalism are replaced by discrete sums. These formulations involve various types of duality and provide exact coarse-graining formulas which can be combined with truncations to obtain practical implementations of the Wilson renormalization group program. Tensor reformulations are naturally discrete and provide manageable transfer matrices. Combining truncations with the time continuum limit, we derive Hamiltonians suitable to perform quantum simulation experiments, for instance using cold atoms, or to be programmed on existing quantum computers. We review recent progress concerning the tensor field theory treatment of non-compact scalar models, supersymmetric models, economical four-dimensional algorithms, noise-robust enforcement of Gauss's law, symmetry preserving truncations and topological considerations. We discuss connections with other tensor network approaches.

hep-lat↗

arXiv2021

Clock model interpolation and symmetry breaking in O(2) models

The $q$-state clock model is a classical spin model that corresponds to the Ising model when $q=2$ and to the $XY$ model when $q\to\infty$. The integer-$q$ clock model has been studied extensively and has been shown to have a single phase transition when $q=2$,$3$,$4$ and two phase transitions when $q>4$.We define an extended $q$-state clock model that reduces to the ordinary $q$-state clock model when $q$ is an integer and otherwise is a continuous interpolation of the clock model to noninteger $q$. We investigate this class of clock models in 2D using Monte Carlo (MC) and tensor renormalization group (TRG) methods, and we find that the model with noninteger $q$ has a crossover and a second-order phase transition. We also define an extended-$O(2)$ model (with a parameter $γ$) that reduces to the $XY$ model when $γ=0$ and to the extended $q$-state clock model when $γ\to\infty$, and we begin to outline the phase diagram of this model. These models with noninteger $q$ serve as a testbed to study symmetry breaking in situations corresponding to quantum simulators where experimental parameters can be tuned continuously.

hep-lat↗