arXiv subjects

Tomah Sogabe

Publications and source records attributed to Tomah Sogabe.

7 recordsLinked to original sources

arXiv2026

Tree Tensor Network Reservoir Computing: Hierarchical Ensemble with Invariant Phase Boundaries

We propose Tree Tensor Network Reservoir Computing (TTN-RC), a quantum-inspired reservoir computing framework for time-series prediction that uses the hierarchical structure of Tree Tensor Networks as a random reservoir. To control the exponential concentration or divergence of TTN outputs, we introduce a hierarchical ensemble method that partitions a fixed-size reservoir into multiple independent sub-reservoirs. In the tested NARMA benchmarks, TTN-RC achieves competitive or improved performance compared with conventional Echo State Networks, especially for tasks requiring higher-order nonlinear processing and longer contextual dependence. We also derive an expected contraction rate based on the reservoir Jacobian and develop a mean-field description of the reservoir-state statistics. These analyses identify an asymptotic stability boundary at $\sigma_{T}=\sqrt{2}$ in the large per-tree-size limit, where several theoretical indicators converge. Our results provide a design principle for tensor-network-based reservoir computing and clarify how hierarchical reservoir topology controls stability and nonlinear information processing.

quant-ph↗

arXiv2025

Quantum-Circuit Framework for Two-Stage Stochastic Programming via QAOA Integrated with a Quantum Generative Neural Network

Two-stage stochastic programming often discretizes uncertainty into scenarios, but scenario enumeration makes expected recourse evaluation scale at least linearly in the scenario count. We propose qGAN-QAOA, a unified quantum-circuit workflow in which a pre-trained quantum generative adversarial network encodes the scenario distribution and QAOA optimizes first-stage decisions by minimizing the full two-stage objective, including expected recourse cost. With the qGAN parameters fixed after training, we evaluate the objective as the expectation value of a problem Hamiltonian and optimize only the QAOA variational parameters. We interpret non-anticipativity as a condition on measurement outcome statistics and prove that the first-stage measurement marginal is independent of the scenario. For uniformly discretized uncertainty, the diagonal operator encoding the uncertainty admits a sparse Pauli-Z expansion via the Walsh--Hadamard transform, yielding polylogarithmic scaling of gate count and circuit depth with the number of scenarios. Numerical experiments on the stochastic unit commitment problem (UCP) with photovoltaic (PV) uncertainty compare the expected cost of the proposed method with classical expected-value and two-stage stochastic programming baselines, demonstrating the effectiveness of qGAN-QAOA as a two-stage decision model.

quant-ph↗

arXiv2025

Hamiltonian-Driven Architectures for Non-Markovian Quantum Reservoir Computing

We propose a Hamiltonian-level framework for non-Markovian quantum reservoir computing directly tailored for analog hardware implementations. By dividing the reservoir into a system block and an environment block and evolving their joint state under a unified Hamiltonian, our architecture naturally embeds memory backflow by harnessing entanglement-induced information backflow with tunable coupling strengths. Numerical benchmarks on short-term memory tasks demonstrate that operating in non-Markovian regimes yields significantly slower memory decay compared to the Markovian limit. Further analyzing the echo-state property (ESP), showing that the non-Markovian quantum reservoir evolves from two different initial states, they do not converge to the same trajectory even after a long time, strongly suggesting that the ESP is effectively violated. Our work provides the first demonstration in quantum reservoir computing that strong non-Markovianity can fundamentally violate the ESP, such that conventional linear-regression readouts fail to deliver stable training and inference. Finally, we experimentally showed that, with an appropriate time-evolution step size, the non-Markovian reservoir exhibits superior performance on higher-order nonlinear autoregressive moving-average(NARMA) tasks.

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

Hot Carrier Transportation Dynamics in InAs/GaAs Quantum Dot Solar Cell

The hot carrier dynamics and its effect on the device performance of GaAs solar cell and InAs/GaAs quantum dot solar cell (QDSC) was investigated. At first, the fundamental operation feature of conventional hot carrier solar cell was simulated based on the detailed balance thermodynamic model. Then we investigated the hot carrier dynamics in the normal junction based solar cell using hydrodynamic/energy Boltzmann transportation model (HETM) where the two temperature (carrier temperature and lattice temperature are treated separately. For the first time, we report an inherent quasi-equivalence between the detailed balance model and HETM model. The inter-link revealed here addresses the energy conservation law used in the detailed balance model from different angle and it paves a way toward an alternative approach to curtail the selective contact constraints used in the conventional hot carrier solar cell. In simulation, a specially designed InAs/GaAs quantum dot solar cell was used in the simulation. By varying the hot carrier energy relaxation time , an increase in the open circuit voltage was clearly found with the increase of . Detailed analysis was presented regarding the spatial distribution of hot carrier temperature and its interplay with electric field and three hot carrier recombination processes (Auger, SRH and radiative)

cond-mat.mes-hall↗

arXiv2019

Quantum Circuit Parameters Learning with Gradient Descent Using Backpropagation

Quantum computing has the potential to outperform classical computers and is expected to play an active role in various fields. In quantum machine learning, a quantum computer has been found useful for enhanced feature representation and high dimensional state or function approximation. Quantum-Classical hybrid algorithms are proposed in recent years for this purpose under the Noisy-Intermediate Scale Quantum computer (NISQ) environment. Under this scheme, the role played by classical computer is the parameter tuning, parameter optimization, and parameter update for the quantum circuit. In this paper, we propose a gradient descent based backpropagation algorithm that can efficiently calculate the gradient in parameter optimization and update the parameter for quantum circuit learning, which outperforms the current parameter search algorithms in terms of computing speed while presents the same or even higher test accuracy.

quant-ph↗

arXiv2019

Convolution filter embedded quantum gate autoencoder

The autoencoder is one of machine learning algorithms used for feature extraction by dimension reduction of input data, denoising of images, and prior learning of neural networks. At the same time, autoencoders using quantum computers are also being developed. However, current quantum computers have a limited number of qubits, which makes it difficult to calculate big data. In this paper, as a solution to this problem, we propose a computation method that applies a convolution filter, which is one of the methods used in machine learning, to quantum computation. As a result of applying this method to a quantum autoencoder, we succeeded in denoising image data of several hundred qubits or more using only a few qubits under the autoencoding accuracy of 98%, and the effectiveness of this method was obtained. Meanwhile, we have verified the feature extraction function of the proposed autoencoder by dimensionality reduction. By projecting the MNIST data to two-dimension, we found the proposed method showed superior classification accuracy to the vanilla principle component analysis (PCA). We also verified the proposed method using IBM Q Melbourne and the actual machine failed to provide accurate results implying high error rate prevailing in the current NISQ quantum computer.

quant-ph↗