11 papers
Geometric Quantum Physics Informed Neural Network
Wai-Hong Tam, Reza Safari, Hiromichi Matsuyama
Quantum physics-informed neural networks (QPINNs) have recently emerged as a promising framework for the solution of partial differential equations (PDEs), with several studies rep…
Recursive QAOA for Interference-Aware Resource Allocation in Wireless Networks
Kuan-Cheng Chen, Hiromichi Matsuyama, Wei-hao Huang +1
Discrete radio resource management problems in dense wireless networks are naturally cast as quadratic unconstrained binary optimization (QUBO) programs but are difficult to solve…
Quantum Annealing with Qubit-Resonator Systems for Simultaneous Optimization of Binary and Continuous Variables
Seiya Endo, Shohei Kawakatsu, Hiromichi Matsuyama +2
Quantum annealing is a method developed to solve combinatorial optimization problems by utilizing quantum bits. Solving such problems corresponds to minimizing a cost function defi…
Transferring linearly fixed QAOA angles: performance and real device results
Ryo Sakai, Hiromichi Matsuyama, Wai-Hong Tam +1
Quantum Approximate Optimization Algorithm (QAOA) enables solving combinatorial optimization problems on quantum computers by optimizing variational parameters for quantum circuits…
Learning to Learn with Quantum Optimization via Quantum Neural Networks
Kuan-Cheng Chen, Hiromichi Matsuyama, Wei-Hao Huang
Quantum Approximate Optimization Algorithms (QAOA) promise efficient solutions to classically intractable combinatorial optimization problems by harnessing shallow-depth quantum ci…
Sampling-based Quantum Optimization Algorithm with Quantum Relaxation
Hiromichi Matsuyama, Yu Yamashiro
Variational Quantum Algorithm (VQA) is a hybrid algorithm for noisy quantum devices. However, statistical fluctuations and physical noise degrade the solution quality, so it is dif…