9 citations · 9 across the 4 of their papers we have counts for
4 papers
Random Access Codes: Explicit Constructions, Optimality, and Classical-Quantum Gaps
Ruho Kondo, Yuki Sato, Hiroshi Yano +3
A random access code (RAC) encodes an -bit string into a -bit message, , so that any requested bit can be recovered with high probability; a quantum RAC (QRAC) uses …
Learning from imperfect quantum data via unsupervised domain adaptation with classical shadows
Kosuke Ito, Akira Tanji, Hiroshi Yano +2
Learning from quantum data using classical machine learning models has emerged as a promising paradigm toward realizing quantum advantages. Despite extensive analyses on their perf…
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…
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…