6 citations · 13 across the 10 of their papers we have counts for
12 papers
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…
Purified phase estimation samples spectra efficiently
Stefano Scali, Josh Kirsopp, Antonio Márquez Romero +1
Quantum phase estimation (QPE) is a cornerstone algorithm for extracting Hamiltonian eigenvalues, but its standard, eigenstate-centric form relies on carefully prepared coherent in…
Polaritonic Machine Learning for Graph-based Data Analysis
Yuan Wang, Stefano Scali, Oleksandr Kyriienko
Photonic and polaritonic systems offer a fast and efficient platform for accelerating machine learning (ML) through physics-based computing. To gain a computational advantage, howe…
Quantum community detection via deterministic elimination
Chukwudubem Umeano, Stefano Scali, Oleksandr Kyriienko
We propose a quantum algorithm for calculating the structural properties of complex networks and graphs. The corresponding protocol -- deteQt -- is designed to perform large-scale…
Addressing the Readout Problem in Quantum Differential Equation Algorithms with Quantum Scientific Machine Learning
Chelsea A. Williams, Stefano Scali, Antonio A. Gentile +2
Quantum differential equation solvers aim to prepare solutions as -qubit quantum states over a fine grid of points, surpassing the linear scaling of classical solvers.…
Can Geometric Quantum Machine Learning Lead to Advantage in Barcode Classification?
Chukwudubem Umeano, Stefano Scali, Oleksandr Kyriienko
We consider the problem of distinguishing two vectors (visualized as images or barcodes) and learning if they are related to one another. For this, we develop a geometric quantum m…
The topology of data hides in quantum thermal states
Stefano Scali, Chukwudubem Umeano, Oleksandr Kyriienko
We provide a quantum protocol to perform topological data analysis (TDA) via the distillation of quantum thermal states. Recent developments of quantum thermal state preparation al…
Quantum topological data analysis via the estimation of the density of states
Stefano Scali, Chukwudubem Umeano, Oleksandr Kyriienko
We develop a quantum topological data analysis (QTDA) protocol based on the estimation of the density of states (DOS) of the combinatorial Laplacian. Computing topological features…