Meet the team

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.

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 explores understanding computation and learning from a statistical mechanics and physics perspective and trying to implement this with analog computing hardware for more efficient and scalable optimization and machine learning. The way gradients propagate through physical systems, both conservative and dissipative, allows for training such a system and is often much more energy efficient and faster than digital computation for AI or optimization. 

I study the mathematical and computational capabilities of an in-memory computing architecture called the Ising machine (IM). In short, an IM is an embedding of a combinatorial optimization problem into an ensemble of coupled physical systems such that the equilibrium state configurations of the ensemble represent the optimal solutions of the problem. Currently, I am a part of the Heisingberg consortium, where we construct photonic IMs, but I am interested more broadly in the architectural and informational constraints of in-memory computers regardless of physical substrate. My side interests include spin glasses, complex network dynamics, phase transitions in IMs, and the statistical mechanics of artificial neural networks.

The emergence of collective phenomena—such as phase transitions and associative memory—arises from interactions among the individual elements of complex systems. Quantum many-body dynamics can now be explored in detail, in both equilibrium and non-equilibrium settings, thanks to advances in quantum simulators and analog computing platforms. Analysis of the mesoscopic regime can reveal the fundamental mechanisms governing the quantum-to-classical transition. Furthermore, by framing complex physical systems as networks, natural complexity can be mapped onto the operational principles of neuromorphic computation for use in classical, purely quantum, or hybrid devices.

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.

My I am a postdoctoral research associate at the University of Strathclyde, where I work on supersolid and quantum droplet phases of matter in laser-driven Bose-Einstein condensates, as well as various other topics under the umbrella of (quantum) many-body physics. Before that, I earned my PhD at the University of Cambridge, supervised by Natalia Berloff in the pi-computing group, where I (among other things) worked on using techniques from the statistical physics of spin glasses to understand physics-inspired analogue computing devices. Broadly speaking, my interests lie in understanding the dynamical properties of complex systems both in the classical and quantum domain. Examples range from the study of dynamical phase transitions in out-of-equilibrium quantum many-body systems to the analysis of rugged energy landscapes that emerge in "glassy" classical systems.

I recently graduated as a PhD in Natalia's group. Since graduation, I have remained an active member of the group and my research interests mainly focus on designing algorithms for solving hard computational problems in physical hardware, as well as using analogue systems for machine learning tasks.