D4.7: Readout Optimization V1.0

This document gives an overview of the software delivery D4.7 “Readout Optimization (RO) V1.0”, comprised of the three readout optimization methods for quantum computing measurements that were researched and implemented by HQS Quantum Simulations within the NEASQC project. The software implementations can be found on the NEASQC GitHub [1].

The three measurement optimization methods studied include one utilizing shadow measurement in the context of spectroscopy, one based on enhanced sampling using Bayesian statistics, and one based on projecting the result to fulfill so-called n-representability constraints which may be violated in a noisy quantum computation. The later two were already reported in the earlier NEASQC delivery D4.5 [2], but are included within this document as well for the sake of completeness and reader convenience. The document is structured as follows:

In Chap. 2, we introduce the technique for utilizing the measurement of classical shadows in the context of spectroscopy, a promising use case for near future quantum computing. We also show numerical results. The method was implemented based on a recent paper [3], and implemented on our NEASQC GitHub repository [1], where the associated code can be found here:
https://github.com/NEASQC/Variationals_algorithms/tree/main/classic_shadows

Next, in Chap. 3 we discuss the method for enhanced sampling and show some key results, which was already reported in a previous NEASQC deliverable [2]. The software implementation we developed is based on what can be found in literature [4], and we will make the code available in the NEASQC GitHub [1]. Currently, it can be found here:
https://github.com/NEASQC/Variationals_algorithms/tree/main/enhanced_sampling

Finally, in Chap. 4 we present briefly the projection method based on n-representability constraints and show selected results (also already reported in the previous deliverable [2]). A detailed analysis can be found in our publication on the subject [5]. The software is available on the NEASQC GitHub[1], with the corresponding code in the following folder:
https://github.com/NEASQC/Variationals_algorithms/tree/main/n-rep_projection