6 papers
Quantum hardware noise learning via differentiable Kraus representation on tensor networks
Ryo Sakai, Yu Yamashiro
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 differenti…
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…
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…
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…