5 papers · 1 filter
The generative quantum eigensolver (GQE) and its application for ground state search
Kouhei Nakaji, Lasse Bjørn Kristensen, Ryota Kemmoku +14
We introduce the generative quantum eigensolver (GQE), a new quantum computational framework that operates outside the variational quantum algorithm paradigm by applying classical…
Optimizing a parameterized controlled gate using Free Quaternion Selection
Hiroyoshi Kurogi, Katsuhiro Endo, Yuki Sato +6
In variational quantum algorithms, parameterization is typically applied to single-qubit gates.In this study, we instead parameterize a generalized controlled gate and propose an a…
SU(4) gate design via unitary process tomography: its application to cross-resonance based superconducting quantum devices
Michihiko Sugawara, Takahiko Satoh
We present a novel approach for implementing pulse-efficient SU(4) gates on cross resonance (CR)-based superconducting quantum devices. Our method introduces a parameterized unitar…
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…
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…