As the end of the project draws nearer, the NEASQC teams have more and more results to share. Five deliverables have recently been published, covering five different use cases: CO2 recapture, Reinforcement learning for inventory management, Hard optimisation problems for smart-charging of electric vehicles, Quantum rule-based systems (QRBS) for breast cancer detection, and Quantum probabilistic safety assessment (QPSA). Check them if your have an interest in these topics:
Use case: Chemistry / CO2 recapture
RO stands for readout optimization and QCCC stands for quantum computing for carbon capturing. This deliverable consists of three main contributions:
Two methods for improving the measurement results of a quantum computation, 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. Furthermore, a VQE ansatz investigating the formation of bound states between CO2 and benzene in the context of utilizing benzene structures to perform CO2 recapturing.
This report describes how we successfully deployed a QRL agent on a real quantum computer to tackle a simplified version of the challenge of inventory management. We describe the setup that solved this problem and examines some interesting conclusions drawn from comparing the real device noise to simulators.
This deliverable provides a graphical representation of Ising Hamiltonians, with the explicit goal of using this repre-sentation to analyse later the QAOA algorithm. This analysis entails computations with both the Hamiltonian and its exponential.
This report presents the work carried out so far, and includes a preliminary version of the RBS and QRBS software along with the specification of the IDC application that will be developed. The report begins with an introduction of the preliminary version of the QRBS software, providing some use examples of common operations users will make with the library, and a brief commentary on the library’s documentation which is presented in an appendix. Following that, we present the specification for the Invasive Ductal Carcinoma (IDC) application, based on previous work on quantum computing techniques and applying them to the clinical problem specifically.
In this work, we present a quantum walks based approach for Probabilistic Safety Assessment problems in the dynamic Markovian framework.
After presenting well known quantum walk algorithms for detecting or finding marked elements in a graph or for finding paths in specific graphs, we propose two algorithms to address the more general problem of finding paths from some point to marked elements.