Probabilistic computing — a new energy-efficient solution

How could randomness make computation more efficient? We introduce probabilistic computing, the problems it suits and the challenges of moving towards practical adoption.

Modern artificial intelligence (AI) is advancing at a breathtaking pace, arguably unmatched since the industrial revolution. Building on the transformer architecture, larger models and training datasets, and increasingly sophisticated post-training and inference techniques such as reinforcement learning, inference-time reasoning and tool use, AI systems are now capable of tasks that until recently would have been considered science fiction. These gains in capability drive wider adoption, which in turn incentivises further scaling and improvement in a self-reinforcing feedback loop.

To support this growth, graphics processing units have so far scaled remarkably well with the demands of AI software. Increasingly, however, the supporting infrastructure is struggling to keep pace. Memory shortages, pressure on power grids, water consumption and noise pollution associated with large data centres are now recurring concerns. Part of the problem lies in a mismatch between conventional computing architectures and the structure of certain computational problems, resulting in substantial computational overhead.

Bayesian inference and combinatorial optimisation are some examples of problem families that can be poorly matched to conventional deterministic computing systems, often requiring extensive sampling or brute-force searches over enormous solution spaces. Researchers have therefore explored more physics-aware computing approaches that exploit the inherent structure of these problems more efficiently. One such approach is probabilistic computing, which does not merely tolerate stochasticity but incorporates probability directly into the computational model. This can enable algorithms such as Boltzmann-machine sampling and parallel tempering to explore complex probability distributions and optimisation landscapes more efficiently, potentially reducing both computational and energy costs.

Interest in these alternative computing architectures has grown rapidly. We explore the steps needed to move probabilistic computing from an academic novelty toward an impactful technology, with emphasis on the role of academic–industry partnerships, which we believe is key to the scaling of this technology.