5 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…
Tensor Network Generator-Enhanced Optimization for Traveling Salesman Problem
Ryo Sakai, Chen-Yu Liu
We present an application of the tensor network generator-enhanced optimization (TN-GEO) framework to address the traveling salesman problem (TSP), a fundamental combinatorial opti…
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…
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…
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…