2 citations · 2 across the 7 of their papers we have counts for
9 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…
Scalable Message-Passing Quantum Graph Neural Networks in the Weisfeiler-Leman Hierarchy
Snehal Raj, Brian Coyle, Léo Monbroussou +3
Graphs provide a natural language for relational data in chemistry, biology and optimisation. Graph neural networks (GNNs) have driven much of the recent progress in learning from…
Adaptive directional gradients for parameterised quantum circuits
Brian Coyle, Snehal Raj, Virag Umathe +2
Training parameterised quantum circuits (PQCs) on quantum hardware is bottlenecked by the measurement cost of gradient estimation, which under the parameter-shift rule scales linea…
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…
Bayesian Quantum Orthogonal Neural Networks for Anomaly Detection
Natansh Mathur, Brian Coyle, Nishant Jain +4
Identification of defects or anomalies in 3D objects is a crucial task to ensure correct functionality. In this work, we combine Bayesian learning with recent developments in quant…
QuIC: Quantum-Inspired Compound Adapters for Parameter Efficient Fine-Tuning
Snehal Raj, Brian Coyle
Scaling full finetuning of large foundation models strains GPU memory and training time. Parameter Efficient Fine-Tuning (PEFT) methods address this issue via adapter modules which…
Training-efficient density quantum machine learning
Brian Coyle, Snehal Raj, Natansh Mathur +4
Quantum machine learning (QML) requires powerful, flexible and efficiently trainable models to be successful in solving challenging problems. We introduce density quantum neural ne…