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.
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.
Planned Impact
Quantum technologies promise a transformation of the fields of measurement, communication and information processing. They present a particular opportunity since they are disruptive technologies: not only do they offer a chance for rapid growth but they also allow lesser participants in a field (such as the UK in IT) to become major players through appropriate risk-taking and manpower development. Students graduating from the InQuBATE Skills Hub will have the right mindset to work in the industries where quantum technologies will be applied, and help to break down the traditional barriers between those sectors to make this transformation happen. They will have all the necessary technical and transferable skills, plus a network of contacts with our partners, their fellow cohort members and the academic supervisors.
Our commercial partners are keen to help our students realise their potential and achieve the impact we expect of them, through the training they offer and their contributions to the centre's research. They include companies who have already developed quantum technologies to products in quantum communication (Toshiba) and optimization (D-Wave), large corporates who are investing in quantum technology because they see its potential to transform their businesses in aerospace, defence, instrumentation and internet services (Lockheed Martin, Google,) and government agencies with key national responsibilities (NPL). We want to see the best communication of our students' research, so our students will benefit from the existing training programme set up with a leading scientific publisher (Nature Publishing Group); we also want to see more of the future companies that lead this field based the UK, so we have partnered with venture capital group DFJ Esprit to judge and mentor the acceleration of our students' innovations toward the market.
Our commercial partners are keen to help our students realise their potential and achieve the impact we expect of them, through the training they offer and their contributions to the centre's research. They include companies who have already developed quantum technologies to products in quantum communication (Toshiba) and optimization (D-Wave), large corporates who are investing in quantum technology because they see its potential to transform their businesses in aerospace, defence, instrumentation and internet services (Lockheed Martin, Google,) and government agencies with key national responsibilities (NPL). We want to see the best communication of our students' research, so our students will benefit from the existing training programme set up with a leading scientific publisher (Nature Publishing Group); we also want to see more of the future companies that lead this field based the UK, so we have partnered with venture capital group DFJ Esprit to judge and mentor the acceleration of our students' innovations toward the market.
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 | 30/09/2017 | 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. |