Q-CALC (Quantum Contextual Artificial intelligence for Long-range Correlations)

Analysis of complex data sets is of practical interest to the UK government. Classical machine learning (ML), and in particular generative modeling, offers the ability to extract useful representations of the phenomena underlying these empirical data. Current state-of-the-art neural network models, including transformer-based architectures such as (Chat)GPT, struggle to accurately analyse data with long-range dependencies, i.e., with a large context for any given data point. This especially characterises data sets of national security interest, including: super-resolution image processing (e.g., threat detection in low-light, high-noise multi-frame satellite images), natural language processing (e.g., efficient intelligence processing), AND automated real-time analysis of time series sensor data (e.g., anomaly detection, failure prediction). Quantum machine learning (QML) models provide an opportunity to address the challenge of long-range correlations in complex data sets, via equipping ML with the power of quantum contextuality: the underlying nature of a quantum system whereby a measurement outcome depends on the full context of preceding measurement outcomes. Contextuality is a core principle by which the reality of quantum systems eclipse classical models. Indeed, the 2022 Nobel Prize in Physics was awarded to experimental verification of quantum phenomena emerging from the principle of contextuality. We propose the Quantum Contextual Artificial intelligence for Long-range Correlations project (Q-CALC) to develop a QML model that leverages quantum contextuality. Our project will leverage modest quantum resource requirements achieve a large speedup over state-of-the-art classical ML models that are fundamentally limited by comparably shallow context.

The Q-CALC project primarily addresses three interlocking technical challenges: (1) enhancing the data-processing capability of state-of-the-art classical ML algorithms by incorporating quantum contextuality, (2) achieving accurate characterisation of complex data sets with long-range dependencies, (3) integrating this technology into real-world data analysis workflows to provide impactful solutions within the defence sector. CQUK is particularly well-suited to tackle these challenges, given significant IP in the quantum space of algorithms, software, and broader technology, as well as our existing relationships with multiple UK governmental security and defence organisations.