## Mohammadreza Noormandipour

Field: Theoretical Physics

Specialty: Topological Condensed Matter Physics, Quantum Computing and Optimization, Particle Physics, Machine Learning

I am a Ph.D. candidate at the University of Cambridge, researching topological phases of matter and the connections between topological matter and fault-tolerant quantum computation. I'm particularly interested in using quantum neural network states to understand the properties of topological quantum systems. This line of research involves the application of many computational techniques such as variational Monte Carlo, simulated annealing, machine learning and tensor networks. I have also recently got interested in developing quantum algorithms, for example, using quantum kernels in computer vision applications such as point cloud matching or distribution learning in the context of quantum generative models.

I have joined Nokia Bell Labs as a research scientist, mainly focusing on quantum and quantum-inspired optimisation heuristics.

## PROFESSIONAL EXPERIENCE

### Research Scientist

Math and Algorithms Department, Artificial Intelligence Lab, Nokia Bell Labs

Cambridge, UK

The main area of research is related to solving satisfiability problems on a quantum-inspired solver. This involves mapping NP-hard problems (such as 3-SAT) to Ising Hamiltonians and then solving them. A similar approach can be done for other combinatorial optimisation problems, which are abundant in science and technology. We work on a big class of these problems. If you are interested, get in touch.

2023 - Present

## EDUCATION

### PhD in Theoretical Physics

TCM Group, Cavendish Laboratory, University of Cambridge

Cambridge, UK

2020 - 2023

### Master's in Applied Mathematics (Part-III at DAMTP)

Department of Applied Mathematics and Theoretical Physics, University of Cambridge

Cambridge, UK

2019-2020

## Research Areas

### Topological Phases of Matter

Transport properties and modelling of topological phases in the presence of disorder for various symmetry classes and types of systems such as Floquet and light-driven systems. We use both theoretical (supersymmetry, K-Theory, etc.) and numerical (neural network ansatz, machine learning, kernel polynomial method, tensor networks, many-body transport etc.) techniques to study these systems. The main focus was Euler and multi-band topology.

### Non-linear Supersymmetric Sigma Models and Topological Field Theory + Random Matrix Theory

Using these models to analytically compute dynamical correlation functions and transport properties in topological systems in 0D, 1D and quasi-1D. Moreover, we studied the fractional quantum Hall states via the beautiful and generic framework of quantum matrix models.

### Variational Monte Carlo and Neural Network Quantum States

We used VMC and quantum state tomography to successfully find the ground state and excited states of topological systems using neural network quantum ansatz and study the connections to conformal field theory and high energy physics.

### Matrix Product States and Tensor Network Techniques in Physics and Machine Learning

Due to the energy gap and the entanglement area law present in the topological systems, MPS and TN ansatz are very efficient in finding the ground states and studying the topological phase transitions.

### Particle Physics Phenomenology

I started physics by particle physics phenomenology and checking if supersymmetric particles of MSSM model can account for dark matter content of the universe and putting some bounds on the parameter space of these models to guide searches at CERN.