NEASQC webinar: Methods to improve the results of quantum computing measurements

Date: 5 March 2024
Time: 11:00-12:00 CET

NEASQC is organising a series of interactive webinars to share our findings with the quantum computing community. Each month NEASQC experts present an Open Source library available in the NEASQC GitHub or a technique they investigated, and answer your questions.

In March, we will present some outcomes of the NEASQC use cases on Chemistry.

Abstract: An important aspect of quantum computing is retrieving the desired information from the quantum device through measurements. While it is usually clear how this could be done in principle, actually following a certain measurement protocol might constitute a serious resource strain for the whole computation. In algorithms where the expectation value of an operator should be estimated, often the number of measurements needed to gain a result with a certain accuracy is massive. In particular, this can be problematic for iterative algorithms where values need to be frequently obtained from the quantum computer, e.g., within optimization loops of variational algorithms.

In this talk, two methods to reduce the number of necessary measurements in a quantum computation of chemicals will be presented. One of these methods relies on Bayesian statistics, where during the process of collecting data trough measurements, the quantum circuit is altered continuously to increase the amount of information gathered per subsequent measurement. The main method that will be discussed in the presentation uses information that is known a priori about the chemical system to correct imprecise measurements. Specifically, when calculating the ground state energy of a system one often derives it from the one- and two-particle reduced density matrices (RDMs). Valid RDMs need to obey so-called n-representability constraints, and by projecting the measurement of these result into a subspace that fulfills some of these constraints, one can reduce errors. In particular, data will be shown that demonstrates how the measurement variance can be lowered significantly. Furthermore, adverse effects of decoherence are mitigated as well. On top of an analysis of the method’s performance a simple instruction is given how to utilize the scheme as an efficient post-processing step after collecting the quantum measurement data, which is also implemented as open-source software and available on the NEASQC GitHub.

Speaker: Dr Jan Reiner, HQS

Jan Reiner is a co-founder of HQS Quantum Simulations, a company that provides software for materials scientists in the chemical industry, as well as in academia. This software utilizes sophisticated quantum-level simulation methods on conventional computers, but also leverages the power of quantum computing. In his function as Chief Scientific Officer, he manages various research projects within his area of expertise: quantum information and computing. Regarding the NEASQC project, he leads the chemistry work package where quantum computing approaches for quantum chemistry use cases are studied.

Related publications and Open Source libraries

• Deliverable: D4.5 RO (Readout Optimization) Beta and QCCC (Quantum Computing for Carbon Capturing) Beta
• Publication: Post-processing noisy quantum computations utilizing N-representability constraints
• Repository: https://github.com/NEASQC/Variationals_algorithms/tree/enhanced_sampling