Quantum state characterization with deep neural networks
Licentiatavhandling, 2021

In this licentiate thesis, I explain some of the interdisciplinary topics connecting machine learning to quantum physics. The thesis is based on the two appended papers, where deep neural networks were used for the characterization of quantum systems. I discuss the connections between parameter estimation, inverse problems and machine learning to put the results of the appended papers in perspective. In these papers, we have shown how to incorporate prior knowledge of quantum physics and noise models in generative adversarial neural networks. This thesis further discusses how automatic differentiation techniques allow training such custom neural-network-based methods to characterize quantum systems or learn their description. In the appended papers, we have demonstrated that the neural-network approach could learn a quantum state description from an order of magnitude fewer data points and faster than an iterative maximum-likelihood estimation technique. The goal of the thesis is to bring such tools and techniques from machine learning to the physicist’s arsenal and to explore the intersection between quantum physics and machine learning.

Machine learning

classification

optical quantum states.

quantum state tomography

deep neural networks

bosonic states

quantum computing

reconstruction

quantum state discrimination

Chalmers, Göteborg
Opponent: Prof. Roger Melko, University of Waterloo and Perimeter Institute for Theoretical Physics in Canada.

Författare

Shahnawaz Ahmed

Chalmers, Mikroteknologi och nanovetenskap (MC2), Tillämpad kvantfysik

Ahmed, S., Munoz, C. S., Nori, F., Kockum, A. F. "Quantum state tomography with conditional generative adversarial networks".

Ahmed, S., Munoz, C. S., Nori, F., Kockum, A. F. "Classification and reconstruction of quantum states with deep neural networks".

Styrkeområden

Nanovetenskap och nanoteknik (SO 2010-2017, EI 2018-)

Ämneskategorier

Data- och informationsvetenskap

Fysik

Fundament

Grundläggande vetenskaper

Utgivare

Chalmers tekniska högskola

Chalmers, Göteborg

Online

Opponent: Prof. Roger Melko, University of Waterloo and Perimeter Institute for Theoretical Physics in Canada.

Mer information

Senast uppdaterat

2021-03-19