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U(1)-symmetric recurrent neural networks for quantum state reconstruction

Published in Physical Review A, 2021

In this work, we propose a novel recurrent neural network ansatz which respects the U(1) symmetry of an underlying Hamiltonian, significantly improving the performance of neural network quantum state reconstruction.

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Neural Annealing and Visualization of Autoregressive Neural Networks in the Newman–Moore Model

Published in Condensed Matter, 2022

We explore the connection between the glassy and frustrated dynamics of the Newman-Moore model and difficulty in training an neural network to capture the ground state(s).

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Efficient Paths for Local Counterdiabatic Driving

Published in Physical Review B, 2024

We systematically identify experimentally realistic modifications to annealing Hamiltonians where local control can prepare the ground state of annealing Hamiltonians with exponentially greater fidelity, and replicates adiabatic dynamics in shorter times.

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Universal Counterdiabatic Driving in Krylov Space

Published in PRX Quantum, 2025

We demonstrate, via an analogy between optimal variational counterdiabatic protocols and polynomial fitting, that there are system-independent optimal coefficients for an expansion of the adiabatic gauge potential in Krylov space.

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Efficient Paths for Local Counterdiabatic Driving

Published: August 20, 2024

Talk given about the paper Efficient Paths for Local Counterdiabatic Driving. Given at the Workshop on Adiabatic and Dynamical Algorithms for Quantum Hardware at Keble College, Oxford University.

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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