Webinar: Hybrid Quantum/Classical Machine-Learning with a Photonic Quantum Processor
Tuesday, December 5 | 9:00am EST / 2:00pm GMT
Quantum computing is an emerging technology that has the potential to accelerate some machine learning algorithms. In this talk, we first present the ORCA PT Series photonic quantum processor: although it is not universal for quantum computation, it provides a scalable route to performing some tasks that cannot be performed by classical computers. We then discuss how such a processor can be combined with current classical neural network architectures to provide unique computational capabilities. One approach uses the quantum processor as a quantum neural network layer that replaces a classical layer. We demonstrate this approach on an image dataset, and investigate how this impacts the performance of the model. Alternatively, these states can be injected into the generator of a generative adversarial network (GAN). We show that this approach can yield a performance improvement compared to classical GANs on some datasets, and that this approach can scale to large image datasets.
Host
Nir Alfasi, Science Lead, Israeli Quantum Computing Center
Opening Remarks
Aviv Zeevi, VP, Technological Infrastructure Division, Israel Innovation Authority
Guest Speaker
William Clements, Head of Machine Learning, ORCA Computing
William leads the machine learning team at ORCA Computing and is responsible for designing and delivering novel machine learning solutions that harness the unique nature of the company’s quantum photonic processor. He has spent his career expanding the frontiers of technology in machine learning and photonics with previous roles in research in industry, consultancy and as a startup CTO. William has a PhD in physics from the University of Oxford and has co-authored over 20 publications in machine learning, photonics and quantum optics.