Our CEO & Co-founder Richard Murray recently jetted off to Santa Clara to speak at Q2B Silicon Valley.
Richard talked about photonic quantum systems for machine learning and how ORCA’s approach is addressing the needs of organizations ready to get started with quantum computing.
Q2B Silicon Valley is one of the world’s largest gatherings of the quantum computing community.
Please watch the presentation here.
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.
