08 June 2026, at 12pm

Heisingberg webinar 

Algorithms and use-cases for Spatial Photonic Ising Machines

 Victor H. González

Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge, CB3 0WA, United Kingdom

Physics-based computing is a cutting-edge area of research that holds the potential to revolutionize the way we perform calculations by leveraging the non-linear regimes of physical complex networks. In this talk, we introduce the novel algorithms enabled by Spatial Photonic Ising Machines (SPIMs) and study which use-cases suit them best. Using published results from the Heisingberg consortium, we show how SPIMs can be deployed for combinatorial optimization problems of fundamental andindustrial interest. Finally, we present a new direction for SPIMs: physical machine learning. SPIMs, along with other physics-based computers, hold the potential to revolutionize the way we acceleratecombinatorial and machine learning calculations with the added advantage of low power consumption for next-generation unconventional computing. 

19 January 2024, at 4pm

 QAMSS (Quantum and Advanced Materials technologies for a Sustainable Society) seminar series, 4pm in the Cambridge Graphene Centre Seminar room, Electrical Engineering Division, 9 JJ Thomson Avenue.

Physics-Inspired Computing (π-computing) as an emergent paradigm of analogue computations. 

 Natalia G Berloff 

Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge, CB3 0WA, United Kingdom

https://www.pi-computing.org

Pursuing superior computing speeds and enhanced power efficiency suggests going beyond the confines of conventional digital electronic systems. Analogue computing, guided by physics principles such as minimisation of energy, entropy or losses, is changing our approach to problem-solving on both hardware and software levels. Such physics-inspired computing systems function with continuous variables and present a viable strategy for tackling various challenges, from discrete combinatorial optimisation problems to machine learning tasks. This includes quadratic binary minimisation, a notable issue in the classical Ising minimisation of discrete spins, where both theoretical proposals and experimental realisations of physics-based analogue systems have been explored.

 In my talk, I will focus on emerging physical optimisers that utilise bifurcation dynamics and threshold operations to solve nonlinear problems. I will discuss various unconventional systems implementing optical neural networks and the problems suited to their architecture. In particular, I will discuss problems beyond spin Hamiltonians and show how to harness multiple degrees of freedom of physical optical systems for optimisation.