1.1k citations · 2.4k across the 13 of their papers we have counts for
13 papers
The Variational Quantum Eigensolver: a review of methods and best practices
Jules Tilly, Hongxiang Chen, Shuxiang Cao +8
The variational quantum eigensolver (or VQE) uses the variational principle to compute the ground state energy of a Hamiltonian, a problem that is central to quantum chemistry and…
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…
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…
Machine learning logical gates for quantum error correction
Hongxiang Chen, Michael Vasmer, Nikolas P. Breuckmann +1
Quantum error correcting codes protect quantum computation from errors caused by decoherence and other noise. Here we study the problem of designing logical operations for quantum…
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…
Dynamical mean field theory algorithm and experiment on quantum computers
I. Rungger, N. Fitzpatrick, H. Chen +12
The developments of quantum computing algorithms and experiments for atomic scale simulations have largely focused on quantum chemistry for molecules, while their application in co…
Structure optimization for parameterized quantum circuits
Mateusz Ostaszewski, Edward Grant, Marcello Benedetti
We propose an efficient method for simultaneously optimizing both the structure and parameter values of quantum circuits with only a small computational overhead. Shallow circuits…
An initialization strategy for addressing barren plateaus in parametrized quantum circuits
Edward Grant, Leonard Wossnig, Mateusz Ostaszewski +1
Parametrized quantum circuits initialized with random initial parameter values are characterized by barren plateaus where the gradient becomes exponentially small in the number of…
Modelling Non-Markovian Quantum Processes with Recurrent Neural Networks
Leonardo Banchi, Edward Grant, Andrea Rocchetto +1
Quantum systems interacting with an unknown environment are notoriously difficult to model, especially in presence of non-Markovian and non-perturbative effects. Here we introduce…
Adversarial quantum circuit learning for pure state approximation
Marcello Benedetti, Edward Grant, Leonard Wossnig +1
Adversarial learning is one of the most successful approaches to modelling high-dimensional probability distributions from data. The quantum computing community has recently begun…
Hierarchical quantum classifiers
Edward Grant, Marcello Benedetti, Shuxiang Cao +5
Quantum circuits with hierarchical structure have been used to perform binary classification of classical data encoded in a quantum state. We demonstrate that more expressive circu…
Compact Neural Networks based on the Multiscale Entanglement Renormalization Ansatz
Andrew Hallam, Edward Grant, Vid Stojevic +2
This paper demonstrates a method for tensorizing neural networks based upon an efficient way of approximating scale invariant quantum states, the Multi-scale Entanglement Renormali…
Learning hard quantum distributions with variational autoencoders
Andrea Rocchetto, Edward Grant, Sergii Strelchuk +2
Studying general quantum many-body systems is one of the major challenges in modern physics because it requires an amount of computational resources that scales exponentially with…