Machine learning applications for noisy intermediate-scale quantum
computers
- URL: http://arxiv.org/abs/2205.09414v1
- Date: Thu, 19 May 2022 09:26:57 GMT
- Title: Machine learning applications for noisy intermediate-scale quantum
computers
- Authors: Brian Coyle
- Abstract summary: We develop and study three quantum machine learning applications suitable for NISQ computers.
These algorithms are variational in nature and use parameterised quantum circuits (PQCs) as the underlying quantum machine learning model.
We propose a variational algorithm in the area of approximate quantum cloning, where the data becomes quantum in nature.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum machine learning has proven to be a fruitful area in which to search
for potential applications of quantum computers. This is particularly true for
those available in the near term, so called noisy intermediate-scale quantum
(NISQ) devices. In this Thesis, we develop and study three quantum machine
learning applications suitable for NISQ computers, ordered in terms of
increasing complexity of data presented to them. These algorithms are
variational in nature and use parameterised quantum circuits (PQCs) as the
underlying quantum machine learning model. The first application area is
quantum classification using PQCs, where the data is classical feature vectors
and their corresponding labels. Here, we study the robustness of certain data
encoding strategies in such models against noise present in a quantum computer.
The second area is generative modelling using quantum computers, where we use
quantum circuit Born machines to learn and sample from complex probability
distributions. We discuss and present a framework for quantum advantage for
such models, propose gradient-based training methods and demonstrate these both
numerically and on the Rigetti quantum computer up to 28 qubits. For our final
application, we propose a variational algorithm in the area of approximate
quantum cloning, where the data becomes quantum in nature. For the algorithm,
we derive differentiable cost functions, prove theoretical guarantees such as
faithfulness, and incorporate state of the art methods such as quantum
architecture search. Furthermore, we demonstrate how this algorithm is useful
in discovering novel implementable attacks on quantum cryptographic protocols,
focusing on quantum coin flipping and key distribution as examples.
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