友野 孝夫

ABSTRACT This study formulates a novel, elevation‐aware Quadratic Unconstrained Binary Optimization (QUBO) model for multi‐vehicle route optimization that simultaneously reduces fuel consumption and traffic congestion. Candidate routes are generated using a gradient‐corrected Dijkstra algorithm, and route selection is optimized by minimizing a rigorously constructed QUBO Hamiltonian that incorporates fuel cost, route overlap, and constraint satisfaction. Extensive numerical validation is performed using classical annealing simulations across multiple regions with diverse topographical and road network characteristics, including San Francisco. The results demonstrate that incorporating elevation information significantly reduces fuel consumption, while the proposed overlap penalty effectively mitigates congestion. A clear trade‐off between overlap reduction and fuel efficiency is quantitatively characterized. The mathematical consistency of the formulation is ensured through a theoretically derived penalty coefficient, which guarantees constraint satisfaction and stable optimization behavior. Scaling experiments further reveal the limitations of classical solvers as the number of candidate routes increases, highlighting the importance of robust QUBO formulations and motivating future implementation on quantum processing units. Overall, this work establishes a validated and theoretically sound QUBO framework for sustainable transportation optimization and provides a reliable performance baseline for future quantum hardware–based investigations.

Abstract Liquid crystal waveguide film that we can control through wide visible wavelength has been proposed. Recently, small optical system is needed for various industrial application. Among the application, liquid crystal is the material for controlling optical ray. The application includes optical switch and display by controlling birefringence. These applications include AR/VR display, for manufacturing, inspection, and so on. In this study, we have investigated liquid crystal (LC) waveguide film that light is emitted from the waveguide film on glasses toward the eye when the power is turned on. The cut-off of waveguide film is based on liquid crystal switch using refractive index anisotropy. We select smart glasses as one of the applications for AR/VR display. The potential of LC waveguide films is demonstrated by simulating the propagation characteristics of waveguides film, LC reorientations, and diffraction gratings in the field of RGB wavelength range. By using the waveguide structure, the film thickness can be configured to be less than 1 mm. We can expect the liquid crystal waveguide has much application to optical integrated circuit as other application except for smart glasses.

In recent years, the diversification of consumer values has led to an increase in the number of small-quantity, high-mix products. For many manufacturing companies, shipping inspections of such products are of great importance. As all products have the same value, good and defective products need to be efficiently identified. Now, a promising future application of quantum technology is considered to be quantum machine learning. We believe that the quantum classifier for SVMs using quantum kernels is one of the areas where quantum advantages can be demonstrated. At present, there are few examples of quantum classifiers applied to real problems in manufacturing processes. In this study, we aim to build a classifier that can demonstrate the quantum advantage and compare SVMs using classical and quantum kernels with conventional ResNet. Initially, a binarised image was generated after image pre-processing. After principal component analysis and dimensionality reduction were performed on the images, SVM with kernels was carried out. The kernel-based SVMs was then compared with the conventionally implemented Residual neural network (ResNet) using an evaluation index: F1-score. The results showed that the F1-score of SVMs using classical kernels was equivalent to that of Resnet. In addition, SVMs using quantum kernels showed higher F1-score than ResNet. In addition, the impact of the feature map and principal components of the quantum kernel was also investigated. It was found that when the feature map became more complex, conversely, circuit generation took more time. It was also found that the principal components are highly relevant to the image and cannot lead to simple results. In the future, we plan to accumulate more experimental data, look for scenes where quantum machine learning can be used and apply it to the manufacturing field.

Variational Quantum Eigensolver (VQE) method is the way to derive the wave functions from their eigen energies using quantum computers. But, the methods to utilize the superposition states between them haven't been developed yet. The method to calculate the off diagonal element of observables between two states derived by VQE method requires large numbers of gate operations for Noisy Intermediate Scale Quantum (NISQ) devices. We proposed the novel method to drive the superposition state between the states the derived by VQE method. We call it Frame superposition cluster (FSC) method. Using the method, we confirmed that dipole transition moment could be calculated with the highest accuracy compared to other methods.

Subspace-Search Variational Quantum Eigen solver (SSVQE) is a searching method of multiple states and relies on the unitarity of transformations to ensure the orthogonality of output states for multiple states. Therefore, this method is thought to be a promising method for quantum chemistry because ordinary Variational Quantum Eigen solver (VQE) can only calculate the excited states step by step from the ground state based on Variational Quantum deflation (VQD). We compare the advantage of VQE, SSVQE with/without the constraint term and/or Tabu search term, which is added by the Lagrange multiplier method so as to calculate the desired energy levels. We evaluated the advantage by calculating each level of H2 and HeH, respectively. As there simulation results, the accuracy calculated by constrained VQE with Tabu search indicates higher accuracy than that of our other algorithm, for analysis on H2. The accuracy calculated by constrained SSVQE indicates higher that of the constrained VQE with Tabu search . We found it is beneficial for enhancing the accuracy to use constraint terms decreasing convergence times to use Tabu search terms according to the nature of molecules. We demonstrate that constraint and Tabu search terms contribute to the accuracy and convergence time on quantum chemical calculating.