Thesis title: Machine Learning and Quantum Computing
This thesis presents original contributions at the intersection of quantum computing and machine
learning, investigating how these two fields can benefit from each other.
The first part of the thesis presents two applications of machine learning techniques in quantum
computing. The first contribution involves the use of artificial neural networks for the decoding of
quantum error correction codes, resulting in faster decoding times while maintaining a high level
of accuracy. The originality of this work lies in the application of explainable machine learning
techniques to both improve the decoding performance and to provide insights into the decoding
process. The second contribution introduces a novel technique based on reinforcement learning to
characterize and simulate the noise affecting a quantum chip. This technique reduces the heuristic
assumption on the noise model, making it more adaptable to the specific noise characteristics of the
quantum device.
In the second part of the thesis, we demonstrate, with two examples, how it is possible to im
plement machine learning algorithms on quantum devices by replacing artificial neural networks
with parametrized quantum circuits. The first contribution in this section introduces a quantum
anomaly detection algorithm, applied to the field of high-energy physics. This algorithm allows for
the identification of anomalous patterns in quantum data, maintaining a level of accuracy compa
rable to classical algorithms. The novelty of this work lies in the first use of quantum circuits for
the task of anomaly detection in a muon drift chamber trigger system, with possible future appli
cations to quantum detector systems in high-energy physics experiments. The second contribution
presents the quantum version of a generative diffusion model, able to sample quantum states from
a well-defined distribution. This first implementation of a quantum diffusion model is not meant to
outperform classical models, but rather to show potential advantages and limitations of these kinds
of algorithms.
The aim of the thesis is to show how quantum computing can benefit from machine learning,
and how machine learning can be implemented on quantum devices, setting the stage for future
advancements in both fields.