6 papers
Scalable Variational Quantum Optimization via Pauli Correlation Encoding: Application to Large-Scale Power Demand Portfolio Optimization
Takuya Yoshioka, Keita Sasada, Riku Usuki +2
Variational quantum algorithms offer a promising route to combinatorial optimization, but their applicability is limited by the challenge of encoding large-scale problems within re…
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,…
Fair sampling of ground-state configurations using hybrid quantum-classical MCMC algorithms
Yuichiro Nakano, Keisuke Fujii
We study the fair sampling properties of hybrid quantum-classical Markov chain Monte Carlo (MCMC) algorithms for combinatorial optimization problems with degenerate ground states.…
Neural-network-assisted Monte Carlo sampling trained by Quantum Approximate Optimization Algorithm
Yuichiro Nakano, Ken N. Okada, Keisuke Fujii
Sampling problems are widely regarded as the task for which quantum computers can most readily provide a quantum advantage. Leveraging this feature, the quantum-enhanced Markov cha…
Electric Power Demand Portfolio Optimization by Fermionic QAOA with Self-Consistent Local Field Modulation
Takuya Yoshioka, Keita Sasada, Yuichiro Nakano +1
Quantum Approximation Optimization Algorithms (QAOA) have been actively developed, among which Fermionic QAOA (FQAOA) has been successfully applied to financial portfolio optimizat…
Optimal elemental configuration search in crystal using quantum approximate optimization algorithm
Kazuhide Ichikawa, Genta Hayashi, Satoru Ohuchi +3
Optimal elemental configuration search in crystal is a crucial task to discovering industrially important materials such as lithium-ion battery cathodes. In this paper we present a…