Physics-inspired (π-) computing

Natalia Berloff is a professor of applied mathematics at the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge and Fellow of Jesus College, Cambridge. She gained her undergraduate qualifications the Lomonosov Moscow State University and PhD from Florida State University. Before her appointment as a faculty member in DAMTP in 2002, she had been the beneficiary of the UC Presidents Postdoctoral Fellowship and subsequently held the post of assistant professor in the Program in Computing at the University of California, Los Angeles. Her areas of research include nonlinear waves, quantum fluids, complexity, and analogue computing.

Given the colossal reliance on solving optimisation problems in industries such as the logistical, pharmaceutical, and data management sectors, it is increasingly important that quick and novel techniques are found that can accurately determine optimal solutions. I work on analysing and benchmarking physical platforms that efficiently solve NP-hard optimisation problems. Such platforms operate by locating the ground state of specially constructed spin Hamiltonians.

My research focuses on enhancing the efficiency of physics-inspired optimization methods. Many of those methods rely on the dynamics of large networks of interacting oscillators.  I aim to improve our understanding of the intricate interplay between the complex dynamics of these networks and the sophisticated structure inherent to hard optimization problems. To achieve this, I employ numerical simulations as well as analytical calculations.

My research mainly involves the use of physical systems, especially polariton condensate network, to realise unconventional computing paradigms, such as neuromorphic and reservoir computing. These computing approaches based on a network of nodes demonstrate structural similarity to many physical systems. Through numerical simulations guided by theoretical calculations, my objective is to find ways to utilise these physical systems to implement these computing architectures and solve difficult optimisation problems traditionally tackled by other methods, such as deep neural networks.

I am interested in physically inspired computation and solving NP hard optimization problems. By exploiting the physical representations of the computational problems, we can analyze the physical networks geometry, dynamics of topological defects, landscape roughness and connect them to the computational complexity. By developing an analytical description and simulating the behavior of these physical systems, I am trying to find approaches that will allow the system to avoid the local minima to find more reliable solutions faster.

One of the promising research directions for faster and more cost-effective alternatives is physics inspired computing. I am fascinated in particular how networks of coupled oscillators or spins can be used to offer new solutions to complex optimisation problems arising in combinatorics, logistics, machine learning and mathematical modelling. My research focuses on understanding the dynamics of these systems and their potential for gain-based or annealing computing, with the goal of harnessing their unique capabilities for enhanced problem-solving.

My research primarily focuses on open quantum systems and quantum many-body systems, with a particular emphasis on the study of exciton-polaritons in quantum well microcavity structures. My projects encompass various aspects of fundamental physics, including quasiparticle interactions based on the T-matrix scattering method, superfluid dynamics by simulating the nonlinear Schrödinger equation, and vortex dynamics on spherical surfaces. Building on the substantial advancements made in experiments over the past decade, polaritons have emerged as potential physical solvers, often referred to as analogue computers, for numerous NP-hard problems. My recent work has centred on implementing analogue machines rooted in nonlinear optimisation theory and designing appropriate classical and quantum algorithms.

Dr Hayder Salman (UEA)

I am currently an Associate Professor based within the School of Mathematics at the University of East Anglia (UEA). Previously, I held appointments as a postdoctoral research associate at the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge; Department of Mechanical Engineering, Massachusetts Institute of Technology (MIT); Division of Applied Mathematics, Brown University; Department of Mathematics, University of North-Carolina at Chapel Hill. 

My current research is mainly focused on quantum mechanics, and non-equilibrium statistical physics and their application in describing properties of superfluids as well as in solving optimisation problems using quantum annealing. By using physics inspired methods to understand the nature of quantum phase transitions, and by contrasting quantum annealing vs classical algorithms, I aim to uncover the advantages of harnessing quantum effects in optimisation algorithms.

Dr Siddharth Ghosh

I am a Research Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP) and St John's College CRA by Fellow, University of Cambridge. My research interest. I have led a multidisciplinary career journey, driven by my vocation to understand  complexity in the natural world. I  work on superfluidity in nanofluidic  environment and aiming to use it for  unconventional computing. My fundamental interest lies within statistical mechanics light-mater interaction and molecular transport at the interface of quantum and classical domain.  My interdisciplinary mindset drives my activity in innovation to cofound two deep-science startup companies. I received a PhD in Physics with an Excellence Fellowship as an International Max Planck Research Fellow in Physics of Biological and Complex Systems from Goettingen, Germany in 2016. Our researches are recognised by the the German Research Foundation, The Royal Society, Isaac Newton Trust, Honeywell, Eurofins through research and industrial fundings.

Fanyi (Kaitlyn) Wu

I have conducted research in the fields of quantum and nonlinear optics, metasurfaces, and optical neural networks. Currently, I am delving into data-intensive science to enhance my knowledge in advanced research computing and machine learning. My current research interests are in physics-driven algorithms, particularly in the quantum domain, such as quantum computing and simulation -- with the hope of integrating AI and ML into physics research. Recently, I focus on exploring optimization techniques in Ising machines.

Anjie Le

Anjie Le is an MPhil student in Data Intensive Science at Cambridge. Her research interests are mainly in AI for Medicine, focusing on privacy and personalisation. She did the mathematical tripos as an undergraduate in Cambridge.