Distributed Quantum Optimisation for Machine Learning

Optimisation plays a central role in multiple aspect of machine learning, underpinning critical parts such as model training, feature selection, architecture search and hyperparameter tuning. As machine learning models become increasingly complex and datasets grow in size and dimensionality, classical optimisation algorithms face increasing computational challenges. High-dimensional, non-convex loss landscapes common in deep learning often result in slow convergence, sensitivity to initialisation, and highly prone to local minima. These limitations can hinder the efficiency and performance of classical machine learning approaches, especially in large-scale, real-world applications. Quantum computing offers a fundamentally new paradigm for addressing these optimisation challenges. By leveraging quantum phenomena such as superposition, entanglement, and quantum tunnelling, quantum algorithms are expected to explore solution spaces more efficiently than classical counterparts in specific problem domains. Notably, quantum approximate optimisation algorithms (QAOA), variational quantum eigensolvers (VQE), and other variational quantum-classical hybrid methods have shown promise in solving combinatorial and global optimisation problems with potential speedups. However, current quantum devices, known as Noisy Intermediate-Scale Quantum (NISQ) systems, are limited in terms of qubit count, fidelity, and connectivity—posing significant obstacles for standalone quantum solutions. This project aims to bridge the gap between the emerging field of quantum optimisation and the demands of large-scale machine learning by developing a scalable, distributed quantum-classical optimisation framework. The core innovation is a distributed framework in which multiple quantum computing nodes collaborate to decompose, distribute, and solve complex optimisation tasks for machine learning applications. By distributing workloads effectively across this distributed infrastructure, we aim to overcome the current hardware limitations of quantum devices while harnessing their unique computational advantages. The successful candidate on this project will work within a multidisciplinary team of experts in quantum computing, machine learning, and optimisation. Together, we aim to advance the research of quantum-enhanced machine learning, offering new tools and architectures capable of addressing the optimisation demands of complex AI systems.