317 citations
- University College LondonGB12 papers
- University of OxfordGB4 papers
- Cambridge Quantum Computing (United Kingdom)GB3 papers
- Imperial College LondonGB2 papers
- Institut national de recherche en sciences et technologies du numériqueFR2 papers
- King's College LondonGB2 papers
- National Physical LaboratoryGB2 papers
- The University of Texas at AustinUS2 papers
- Google (United States)US1 paper
- Institute of Theoretical and Applied InformaticsPL1 paper
- Johnson Matthey (United Kingdom)GB1 paper
- Kavli Institute for Theoretical PhysicsUS1 paper
12 papers
A variational quantum eigensolver for dynamic correlation functions
Hongxiang Chen, Max Nusspickel, Jules Tilly +1
Recent practical approaches for the use of current generation noisy quantum devices in the simulation of quantum many-body problems have been dominated by the use of a variational…
Reduced Density Matrix Sampling: Self-consistent Embedding and Multiscale Electronic Structure on Current Generation Quantum Computers
Jules Tilly, P. V. Sriluckshmy, Akashkumar Patel +6
We investigate fully self-consistent multiscale quantum-classical algorithms on current generation superconducting quantum computers, in a unified approach to tackle the correlated…
Statistical Limits of Supervised Quantum Learning
Carlo Ciliberto, Andrea Rocchetto, Alessandro Rudi +1
Within the framework of statistical learning theory it is possible to bound the minimum number of samples required by a learner to reach a target accuracy. We show that if the boun…
Computation of molecular excited states on IBM quantum computers using a discriminative variational quantum eigensolver
Jules Tilly, Glenn Jones, Hongxiang Chen +2
Solving for molecular excited states remains one of the key challenges of modern quantum chemistry. Traditional methods are constrained by existing computational capabilities, limi…
Quantum State Discrimination Using Noisy Quantum Neural Networks
Andrew Patterson, Hongxiang Chen, Leonard Wossnig +3
Near-term quantum computers are noisy, and therefore must run algorithms with a low circuit depth and qubit count. Here we investigate how noise affects a quantum neural network (Q…
Cost function embedding and dataset encoding for machine learning with parameterized quantum circuits
Shuxiang Cao, Leonard Wossnig, Brian Vlastakis +2
Machine learning is seen as a promising application of quantum computation. For near-term noisy intermediate-scale quantum (NISQ) devices, parametrized quantum circuits (PQCs) have…