3 citations · 3 across the 7 of their papers we have counts for
9 papers
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…
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 Learning of Classical Correlations with continuous-domain Pauli Correlation Encoding
Vicente P. Soloviev, Bibhas Adhikari
We propose a quantum machine learning framework for estimating classical covariance matrices using parameterized quantum circuits within the Pauli-Correlation-Encoding (PCE) paradi…
Progressive Binarization - Pauli Correlation Encoding: a Continuation Method for Constrained Optimization
Jacobo Padín-Martínez, Vicente P. Soloviev, Alejandro Borrallo-Rentero +3
Pauli Correlation Encoding (PCE) reduces the qubit requirements of quantum optimization by embedding the problem variables into the expectation values of Pauli observables, so that…
Large-scale portfolio optimization using Pauli Correlation Encoding
Vicente P. Soloviev, Michal Krompiec
Portfolio optimization is a cornerstone of financial decision-making, traditionally relying on classical algorithms to balance risk and return. Recent advances in quantum computing…
A simple method for seniority-zero quantum state preparation
Michal Krompiec, Josh J. M. Kirsopp, Antonio Márquez Romero +1
Quantum Phase Estimation (QPE), the quantum algorithm for estimating eigenvalues of a given Hermitian matrix and preparing its eigenvectors, is considered the most promising approa…
Scaling Portfolio Diversification with Quantum Circuit Cutting Techniques
Vicente P. Soloviev, Antonio Márquez Romero, Josh Kirsopp +1
Quantum Approximate Optimization Algorithms (QAOA) have demonstrated a strong potential in addressing graph-based optimization problems. However, the execution of large-scale quant…
Trainability maximization using estimation of distribution algorithms assisted by surrogate modelling for quantum architecture search
Vicente P. Soloviev, Vedran Dunjko, Concha Bielza +2
Quantum architecture search (QAS) involves optimizing both the quantum parametric circuit configuration but also its parameters for a variational quantum algorithm. Thus, the probl…
Quantum Approximate Optimization Algorithm for Bayesian network structure learning
Vicente P. Soloviev, Concha Bielza, Pedro Larrañaga
Bayesian network structure learning is an NP-hard problem that has been faced by a number of traditional approaches in recent decades. Currently, quantum technologies offer a wide…