Uncertainty Evaluation for Quantum Chemistry Simulations

Cambridge Quantum Computing Ltd

National Physical Laboratory
Round 3

Quantum computers have the potential to be an invaluable tool to solve major problems in chemistry and materials science, which are relevant for industrial applications in areas ranging from the design of new drugs to the engineering of advanced materials.

However, current and near-term machines are very sensitive to small perturbations that introduce error into their outputs and reduce their accuracy.

Since these calculations are infeasible for a conventional computer, it’s impossible to simply check the answer: we need to construct a mathematical model of the quantum computer and its algorithm to calculate how accurate the results are.

Therefore, systematic studies of the sources of error and their relative impact on the accuracy of the computation are needed.

The aim of our project is to estimate the degree of this uncertainty based on the measurable error parameters of the quantum device.

To simulate a physical system on a quantum computer its mathematical description is decomposed into a sequence of primitive operations (gates) which the quantum hardware performs on the quantum memory (qubits).

Current and near-term machines, while large enough to perform useful calculations, are not large enough to incorporate error correction to protect the simulation from imprecision in the gates and errors in the qubits.

Given the extreme sensitivity of these devices, the results of the simulation will inevitably have some degree of error due to noise in the quantum computer.

There are many sources of error within a quantum computer: gate timing errors, qubit decoherence, thermal noise, and measurement errors, among others.

Each of these contributes an undesirable noise term to the computation process which produces uncertainty in the final result.

Quantum computers may be based on a variety of different physical effects — microwave pulses or magnetic fields for example — and the contribution of each error type will vary accordingly.

Quantum metrology can determine the magnitude of each error source for a given device, but the key measurement challenge is to link, via metrological analysis and uncertainty propagation, the contributions of the different errors to the uncertainty of the final result.

To address this need we will develop an analytical and numerical framework that accounts for all the sources of error within a quantum computer and relates them to the results of the algorithm running on it.

The success of this project will enable better algorithms that will improve the accuracy of physical simulations on quantum computers.

Analysis for Innovators