24 citations · 52 across the 9 of their papers we have counts for
9 papers
Qubit-Efficient 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…
Scalable circuit depth reduction in feedback-based quantum optimization with a quadratic approximation
Don Arai, Ken N. Okada, Yuichiro Nakano +2
Combinatorial optimization problems are one of the areas where near-term noisy quantum computers may have practical advantage against classical computers. Recently a novel feedback…
Experimental Demonstration of Fermionic QAOA with One-Dimensional Cyclic Driver Hamiltonian
Takuya Yoshioka, Keita Sasada, Yuichiro Nakano +1
Quantum approximate optimization algorithm (QAOA) has attracted much attention as an algorithm that has the potential to efficiently solve combinatorial optimization problems. Amon…
A comprehensive survey on quantum computer usage: How many qubits are employed for what purposes?
Tsubasa Ichikawa, Hideaki Hakoshima, Koji Inui +18
Quantum computers (QCs), which work based on the law of quantum mechanics, are expected to be faster than classical computers in several computational tasks such as prime factoring…
Fermionic Quantum Approximate Optimization Algorithm
Takuya Yoshioka, Keita Sasada, Yuichiro Nakano +1
Quantum computers are expected to accelerate solving combinatorial optimization problems, including algorithms such as Grover adaptive search and quantum approximate optimization a…