3 papers
quant-ph2026
Divide-and-Conquer Neural Network Surrogates for Quantum Sampling: Accelerating Markov Chain Monte Carlo in Large-Scale Constrained Optimization Problems
Yuya Kawamata, Yuichiro Nakano, Keisuke Fujii
Sampling problems are promising candidates for demonstrating quantum advantage, and one approach known as quantum-enhanced Markov chain Monte Carlo [Layden, D. et al., Nature 619,…
quant-ph2026
Quasi-Monte Carlo Method for Linear Combination Unitaries via Classical Post-Processing
Yuya Kawamata, Kosuke Mitarai, Keisuke Fujii
We propose the quasi-Monte Carlo method for linear combination of unitaries via classical post-processing (LCU-CPP) on quantum applications. The LCU-CPP framework has been proposed…
cs.ET2024
Designing Unit Ising Models for Logic Gate Simulation through Integer Linear Programming
Shunsuke Tsukiyama, Koji Nakano, Xiaotian Li +3
An Ising model is defined by a quadratic objective function known as the Hamiltonian, composed of spin variables that can take values of either or . The goal is to assign…