8 papers
Explicit block-encoding for partial differential equation-constrained optimization
Yuki Sato, Jumpei Kato, Hiroshi Yano +2
Partial differential equation (PDE)-constrained optimization, where an optimization problem is subject to PDE constraints, arises in various applications such as design, control, a…
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 …
Hamiltonian simulation for 3D elastic wave equations in homogeneous elastic media
Kosuke Nakanishi, Hiroshi Yano, Yuki Sato
We present an explicit quantum circuit construction for Hamiltonian simulation of a first-order velocity--stress formulation of the three-dimensional elastic wave equation in homog…
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…
Quantum framework for parameterizing partial differential equations via diagonal block-encoding
Hiroshi Yano, Yuki Sato
We study a quantum-algorithmic framework for parameterizing partial differential equations (PDEs). For a broad class of problems in which the discretized parameter field admits a d…
Quantum phase classification via partial tomography-based quantum hypothesis testing
Akira Tanji, Hiroshi Yano, Naoki Yamamoto
Quantum phase classification is a fundamental problem in quantum many-body physics, traditionally approached using order parameters or quantum machine learning techniques such as q…