Quantum Software for Simulation of molecular systems on NISQ devices

Lead Research Organisation: University College London
Department Name: London Centre for Nanotechnology

Abstract

Near-term Intermediate Scale Quantum (NISQ) devices describe quantum devices which are available today, or within the next few years. Quantum hardware is not yet fully fault tolerant and error corrected, meaning that any algorithms running on current devices are subject to noise. This project aims to develop and test algorithms for simulation of molecular systems on NISQ devices. An example of a scheme which can be simulated is the Hubbard model. There will be a strong focus upon validation throughout this project, where the goal is to develop the most accurate quantum algorithms possible.

Included in the PhD work is a continuation of a project developing a hybrid quantum - classical machine learning scheme for quantum state discrimination in the presence of noise. This is a scheme where the output of a quantum device is minimised by a classical machine learning algorithm. The work is an extension of previous work examining the case where the quantum device is subject to noise. This work will then be used later in the PhD to develop hybrid machine learning algorithms for molecular simulation.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/P510270/1 31/03/2016 30/08/2022
1918352 Studentship EP/P510270/1 24/09/2017 04/08/2022 Andrew Patterson
 
Description Discovered a method of introducing non-linearity into noisy quantum neural networks, and aided development of an experiment in simulating the DMFT system (a solid state system) on a quantum computer.
Exploitation Route Could be taken forward to other applications of quantum neural networks, and could be used to simulate more computationally intensive DMFT systems on a quantum computer.
Sectors Chemicals,Other

 
Description DMFT Theory and experiment on a Quantum Computer 
Organisation Cambridge Quantum Computing
Country United Kingdom 
Sector Private 
PI Contribution Development of minimisation procedures for the VQE experiment. Group provided knowledge of the problem at hand.
Collaborator Contribution Provided compiler and worked on VQE experiments for finding ground state of DMFT problem.
Impact Paper (currently pre-print) on solving the DMFT system in theory and experiment.
Start Year 2018
 
Description DMFT Theory and experiment on a Quantum Computer 
Organisation Rhako AI
Country United Kingdom 
Sector Private 
PI Contribution Development of minimisation procedures for the VQE experiment. Group provided knowledge of the problem at hand.
Collaborator Contribution Provided compiler and worked on VQE experiments for finding ground state of DMFT problem.
Impact Paper (currently pre-print) on solving the DMFT system in theory and experiment.
Start Year 2018
 
Description DMFT Theory and experiment on a Quantum Computer 
Organisation University of Maryland
Country United States 
Sector Academic/University 
PI Contribution Development of minimisation procedures for the VQE experiment. Group provided knowledge of the problem at hand.
Collaborator Contribution Provided compiler and worked on VQE experiments for finding ground state of DMFT problem.
Impact Paper (currently pre-print) on solving the DMFT system in theory and experiment.
Start Year 2018
 
Title Simulation of Quantum Neural Networks in Tensorflow 
Description Simulates a noise quantum device with measurements during application of the circuit, allowing for neural-network style dropout. 
Type Of Technology Software 
Year Produced 2019 
Impact Paper in pre-print on Noisy quantum neural networks.