Classical AI models can optimise workflows and logistics, classify images, simulate unique materials, and detect anomalies, but often lack the necessary precision or entail costs that prevent broader use.
Classical models are restricted by the simple nature of their building blocks. A photonic quantum accelerator samples from complex distributions and can explore more of the images, material combinations, and workflows that matter. You can reach beyond the limits of classical computing alone at a more reasonable cost.
ORCA provides a practical, hybrid solution to the computational hurdles faced in advanced manufacturing. ORCA's photonic quantum accelerator works alongside your current classical workflow — same models, same training, same Python frameworks. Only the diversity and quality of what comes out change.
There is no bespoke quantum stack for your team to learn, and no separate discovery pipeline to maintain. The accelerator substitutes the prior your generative model samples from — everything downstream stays as it is.
Talk to us about your most demanding workloads and let’s define a benchmark together.
David Hall DPhil
Head of Delivery
Prof. Ian Walmsley is Chairman of the ORCA Computing Board and a leading figure in quantum optics, quantum memories and waveguide circuits. He is Provost of Imperial College, London, an Honorary Fellow at St Hugh's College, Oxford and a Fellow of the Royal Society, The Optical Society, the Institute of Physics and the American Physical Society. Previously, he was President of the Optical Society of America, Pro-Vice-Chancellor for Research and Innovation, Hooke Professor of Experimental Physics at the University of Oxford and Director of the NQIT (Networked Quantum Information Technologies) hub. Prof. Walmsley is recognised for developing the SPIDER technique for characterising ultra-fast laser pulses.
Enhance renewable energy optimisation and accelerate the development of biofuels.
Investigating molecular structures is an important pursuit in computational chemistry, especially in fields likes biofuel formulation, material innovation, and pharmaceutical development where research acceleration is critical. The specific problem considered here is significant across the energy industry, as molecule’s possible structures directly determine many of its physical and chemical traits. However, the vast array of possible configurations and high computational requirements make it difficult for traditional methods to find low-energy conformations for certain molecules.
ORCA partnered has with bp to explore a hybrid quantum-classical approach using generative adversarial network (GAN) algorithms. This approach aims to generate low-energy conformations of small and medium size hydrocarbon molecules, offering a potential solution to the computational hurdles faced in molecular exploration.
