3 papers
quant-ph2024
Impact of Measurement Noise on Escaping Saddles in Variational Quantum Algorithms
Eriko Kaminishi, Takashi Mori, Michihiko Sugawara +1
Stochastic gradient descent (SGD) is a frequently used optimization technique in classical machine learning and Variational Quantum Eigensolver (VQE). For the implementation of VQE…
quant-ph2024
Noise Robustness of Quantum Relaxation for Combinatorial Optimization
Kentaro Tamura, Yohichi Suzuki, Rudy Raymond +5
QRAO (Quantum Random Access Optimization) is a relaxation algorithm that reduces the number of qubits required to solve a problem by encoding multiple variables per qubit using QRA…
quant-ph2023
Digital quantum simulator for the time-dependent Dirac equation using discrete-time quantum walks
Shigetora Miyashita, Takahiko Satoh, Michihiko Sugawara +5
We introduce a quantum algorithm for simulating the time-dependent Dirac equation in 3+1 dimensions using discrete-time quantum walks. Thus far, promising quantum algorithms have b…