6 citations · 13 across the 10 of their papers we have counts for
5 papers · 2 filters
Problem-aware sampling for bias and variance reduction in randomised Hamiltonian simulation
Brian Coyle, Jonathan H. Fetherolf, Antonio Márquez Romero +3
Randomised quantum simulation algorithms such as qDRIFT reduce circuit depth by replacing deterministic product formulas with stochastic compilations, but achieving high accuracy c…
OpenQARP: a modular framework for quantum application research
Stefano Scali, Vicente P. Soloviev, Antonio Márquez Romero +7
We introduce OpenQARP, the Open Quantum Application Research Package: an open-source Python framework for quantum application research, built on a compiled C++ core. In OpenQARP, a…
Learning structural balance of graphs from quantum spectral features
Stefano Scali, Oleksandr Kyriienko
We develop a quantum approach to spectral feature extraction from the density of states (DOS) of a problem-dependent Hamiltonian, and apply it to machine learning on signed graphs.…
A unified quantum computing quantum Monte Carlo framework through structured state preparation
Giuseppe Buonaiuto, Antonio Marquez Romero, Brian Coyle +4
We extend Quantum Computing Quantum Monte Carlo (QCQMC) beyond ground-state energy estimation by systematically constructing the quantum circuits used for state preparation. Replac…
Quantum Randomized Subspace Iteration
Stefano Scali, Brian Coyle, Giuseppe Buonaiuto +1
Resolving degenerate quantum eigenspaces - including topologically ordered ground states and frustrated magnets - requires preparing high-fidelity states that span every direction…