12 papers
Transformers as Intrinsic Optimizers for Quantum Approximate Optimization Algorithm
Kuan-Cheng Chen, Xiaotian Xu, Hiromichi Matsuyama +3
The Quantum Approximate Optimization Algorithm (QAOA) is a leading variational framework for combinatorial optimization on noisy intermediate-scale quantum hardware, but its practi…
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…
Enhancing NDAR with Delay-Gate-Induced Amplitude Damping
Wai-Hong Tam, Hiromichi Matsuyama, Ryo Sakai +1
The Noise-Directed Adaptive Remapping (NDAR) method utilizes amplitude damping noise to enhance the performance of quantum optimization algorithms. NDAR alternates between explorat…
Solving Capacitated Vehicle Routing Problem with Quantum Alternating Operator Ansatz and Column Generation
Wei-hao Huang, Hiromichi Matsuyama, Yu Yamashiro
This study proposes a hybrid quantum-classical approach to solving the Capacitated Vehicle Routing Problem (CVRP) by integrating the Column Generation (CG) method with the Quantum…
Quantum Local Search for Traveling Salesman Problem with Path-Slicing Strategy
Chen-Yu Liu, Hiromichi Matsuyama, Wei-hao Huang +1
We present novel path-slicing strategies integrated with quantum local search to optimize solutions for the Traveling Salesman Problem (TSP), addressing the limitations of current…
Efficient Internal Strategies in Quantum Relaxation based Branch-and-Bound
Hiromichi Matsuyama, Wei-hao Huang, Kohji Nishimura +1
A combinatorial optimization problem is to find an optimal solution under the constraints. This is one of the potential applications for quantum computers. Quantum Random Access Op…
Linearly simplified QAOA parameters and transferability
Ryo Sakai, Hiromichi Matsuyama, Wai-Hong Tam +2
Quantum Approximate Optimization Algorithm (QAOA) provides a way to solve combinatorial optimization problems using quantum computers. QAOA circuits consist of time evolution opera…