RT6: Quantum Machine Learning

Theme Leaders: Dr Jonte Hance

Quantum Machine Learning is an emerging interdisciplinary field that combines the principles of quantum computing with machine learning algorithms. By leveraging quantum phenomena such as superposition, entanglement, and interference, QML aims to accelerate data processing and enable new computational paradigms that are infeasible with classical systems.

Why QML is Interesting:

  • Exponential Speedups: Certain QML algorithms promise exponential improvements in training times and optimisation over classical counterparts, particularly for high-dimensional data.
  • Enhanced Pattern Recognition: Quantum-enhanced models may uncover complex patterns in data that are difficult to detect using traditional methods.
  • Frontier Innovation: QML sits at the cutting edge of both quantum computing and AI, making it a strategic area for long-term investment and leadership in next-generation technologies.

Benefits to the National Edge AI Hub:

  1. Future-Proofing AI Research: Integrating QML ensures the Hub remains at the forefront of AI innovation as quantum hardware matures [1].
  2. Cross-Disciplinary Collaboration: QML fosters collaboration between quantum physicists, AI researchers, and engineers, enriching the Hub’s research ecosystem.
  3. Strategic National Advantage: Establishing expertise in QML positions the UK as a leader in quantum-era AI, aligning with national priorities in science and technology (quantum being one of DSIT’s 5 priority technologies of tomorrow) [2].
  4. New Use Cases at the Edge: As quantum hardware becomes more accessible, hybrid quantum-classical models could enable powerful edge AI applications in areas like cryptography, drug discovery, and real-time optimization [3].

The Quantum Machine Learning research theme will not only expand the scientific scope of the National Edge AI Hub, but also ensure it remains a pioneering force in shaping the future of AI and computing.

[1] Wang, Y and Liu, J. A comprehensive review of quantum machine learning: from NISQ to fault tolerance. Rep. Prog. Phys. 87 116402 (2024)

[2] National quantum strategy – GOV.UK [3] Biamonte, J., Wittek, P., Pancotti, N. et al. Quantum machine learning. Nature 549, 195–202 (2017).

[3] Biamonte, J., Wittek, P., Pancotti, N. et al. Quantum machine learning. Nature 549, 195–202 (2017).