As we enter the last year of the project, the NEASQC teams have more and more results to share. Three deliverables have recently been published, covering two use cases: Hard optimisation problems for smart-charging of electric vehicles and Quantum Natural Language Processing (QNLP), as well as the NEASQC Benchmark Suite. Check them if your have an interest in these topics:
This deliverable describes the specification of an algorithm to find the minimum number of colors required to color
the vertices of a graph such that two connected vertices do not have the same color. This quantity is known as the
This problem arises naturally when considering scheduling problems for charging stations, that can easily be encoded
into a graph coloring problem.
The main specificity of the algorithm is that the QAO (Quantum Approximate Optimization) algorithm is not used
directly on the problem, but is part of a general Branch and Price method, where the QAO algorithm serves as a
In this deliverable D6.10 we present a series of variations of former algorithms and their application to the task of binary sentiment analysis.
At its core, a large part of D6.10 is centered around the task of sentence classification which, together with clustering, is pivotal to a wide range of applications including sentiment analysis (Wankhade et al., 2022), intent detection (Weld et al., 2022) and language identification (Burchell et al., 2023). The ability to classify and cluster sentences effectively is not only vital for streamlining information retrieval and content organisation, but is also a key application in fields such as healthcare (Demner-Fushman et al., 2009), finance (Kalra & Prasad, 2019), government policy (Jin & Mihalcea, 2022) or in certain versions of recommendation systems (Al-Ghuribi & Noah, 2021; Rich, 1979).
The project aims to investigate possibilities and limitations of hybrid quantum-classical methods for such tasks. Due to current hardware limitations, it seems unlikely that quantum methods could present major computational or accuracy benefits over their existing high-performing classical counterparts. This project aims at investigating the possibility of using hybrid classical-quantum NLP approaches on near-term hardware. Thus, we have worked with low numbers of qubits in line with the capabilities of such devices, and we plan to introduce noise into our models in future.
This report begins by laying out the theoretical foundations that underpin our work in Sec. 3, followed by a description of our models and the datasets used in Sec. 4. The results and details of our experiments are then displayed in Sec. 5 and subsequently analysed and discussed in Sec. 6. The report then ends with a discussion of the limitations of our models and future work in Sec. 7, followed by a bibliography and an appendix (Sec. 8) with further information on the theoretical aspects and user instructions for running the discussed models.
Tools: User-centric benchmark suite
This document describes The NEASQC Benchmark Suite (TNBS). The objective of the document is to define the benchmarks that compose it, and the methodology for executing them and reporting their results. It includes also a short description of the TNBS website and the associated repository of submitted benchmark results.
TNBS has been designed to take into account four main objectives:
To achieve these objectives, TNBS has found representative kernels among the NEASQC uses cases to define a set of well-defined tests.