H-2

The 'H2' dataset/benchmark contains encodings of Markov chains used to prepare quantum states and evaluate the performance of a quantum Markov Chain Monte Carlo algorithm on quantum computers.

Papers using H-2 (18)

Quantum Annealer Accelerates The Variational Quantum Eigensolver In A Triple-hybrid Algorithm2024 · 4 citesFast and Noise-aware Machine Learning Variational Quantum Eigensolver Optimiser2025 · 1 citesExperimental Realization of the Markov Chain Monte Carlo Algorithm on a Quantum Computer2026Velocity Verlet-based optimization for variational quantum eigensolvers2026Classical Regularization in Variational Quantum Eigensolvers2026Quantum-Inspired Ising Machines for Quantum Chemistry Calculations2025Quantum Computing Approach to Atomic and Molecular Three-Body Systems2025Quantum Generative Adversarial Autoencoders: Learning latent representations for quantum data generation2025Estimating shots and variance on noisy quantum circuits2025Hardware-efficient Variational Quantum Algorithm In Trapped-ion Quantum Computer2024Subspace-Search Quantum Imaginary Time Evolution for Excited State Computations2024 · 1 citesMinimal evolution times for fast, pulse-based state preparation in silicon spin qubits2024A Study on Quantum Car-Parrinello Molecular Dynamics with Classical Shadows for Resource Efficient Molecular Simulation2024Hardware-efficient variational quantum algorithm in trapped-ion quantum computer2024Digital-analog quantum genetic algorithm using Rydberg-atom arrays2024Variational Quantum Imaginary Time Evolution for Matrix Product State Ansatz with Tests on Transcorrelated Hamiltonians2024Toward a Quantum Computing Formulation of the Electron Nuclear Dynamics Method via Fukutome Unitary Representation2024Quantum annealer accelerates the variational quantum eigensolver in a triple-hybrid algorithm2024