Quantum inspired algorithms for sustainable artificial intelligence and energy efficient machine learning optimisation

The growing scale of artificial intelligence (AI) and machine learning (ML) has sparked public discussion about their energy use and environmental impacts — especially during training and deployment of large, deep-learning models. Existing approaches, such as Bayesian optimisation, evolutionary algorithms, and pruning or quantisation alone, provide minimal energy savings and do not account for multiple optimisation stages. This paper presents a Quantum-Inspired Algorithm (QIA) for sustainable machine learning that overcomes these limitations by jointly optimising hyperparameters, model compression, and scheduling in a single framework. To answer that, QIA uses tunnelling-based search and amplitude reweighting to find energy-efficient configurations faster, and builds a metric comparison to evaluate trade-offs among accuracy, energy consumption, and carbon footprint using a new Quantum-Sustainable AI (Q-SAI) index. QIA reduces training energy consumption by 20–30% compared to several baseline optimisation methods while keeping accuracy losses under 1.5% across experiments on CIFAR-10, ImageNet, IMDB, and GLUE. Optimised inference latency and per-query energy consumption are demonstrated, along with tangible reductions in carbon footprint in deployment regions. Our results show that QIA offers a scalable, domain-agnostic solution for embedding sustainability considerations into contemporary machine learning workflows.