Quantum Architecture Search for Solving Quantum Machine Learning Tasks

Quantum Architecture Search for Solving Quantum Machine Learning Tasks

Abstract:

Quantum computing is a computing paradigm based on the principles of quantum mechanics. This makes it fundamentally different from classical computing. For selected problem domains, quantum computers are expected to offer a performance advantage, the so-called quantum advantage, which manifests itself in exponentially faster computation times or lower resource requirements. In the current Noisy Intermediate Scale Quantum era, quantum hardware is still limited in performance and highly error-prone. Variational Quantum Circuits represent an approach that is comparatively robust to these limitations. The performance of these quantum circuits is highly dependent on the underlying architecture of the parameterized quantum circuit. The development of powerful, hardware-compatible circuit architectures is therefore an important task, also known as Quantum Architecture Search. Developing good architectures manually is an inefficient and error-prone process. First attempts have been made to automate this process. In addition to Evolutionary Algorithms, Differentiable Architecture Search, and Monte Carlo Tree Search, Reinforcement Learning is another potentially suitable approach for finding good architectures, but it has been relatively little studied. In particular, little is known about its suitability as a search strategy for Machine Learning problems. The goal of this work is to investigate Reinforcement Learning as a suitable search strategy for quantum circuits in the context of Machine Learning problems. For this purpose, the RL-QAS framework is presented, which enables the automated search for circuit architectures using a Reinforcement Learning Agent. The RL-QAS framework is evaluated on the Iris and binary MNIST classification problems. RL-QAS enabled the discovery of architectures that achieve high test accuracy in the classification of the aforementioned datasets while exhibiting low complexity. RL-QAS demonstrated that Reinforcement Learning is indeed suitable for architecture discovery. However, in order for RL QAS to be applied to more complex problems, further development of the approach is necessary.

Author:

Simon Salfer

Advisors:

Michael Kölle, Philipp Altmann, Claudia Linnhoff-Popien

Student Thesis | Published June 2025 | Copyright © QAR-Lab
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