Application of ZX-calculus to Quantum Architecture Search
This paper presents a novel approach to quantum architecture search by integrating the techniques of ZX-calculus with Genetic Programming (GP) to optimize the structure of parameterized quantum circuits employed in Quantum Machine Learning (QML). Recognizing the challenges in designing efficient qua...
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Zusammenfassung: | This paper presents a novel approach to quantum architecture search by
integrating the techniques of ZX-calculus with Genetic Programming (GP) to
optimize the structure of parameterized quantum circuits employed in Quantum
Machine Learning (QML). Recognizing the challenges in designing efficient
quantum circuits for QML, we propose a GP framework that utilizes mutations
defined via ZX-calculus, a graphical language that can simplify visualizing and
working with quantum circuits. Our methodology focuses on evolving quantum
circuits with the aim of enhancing their capability to approximate functions
relevant in various machine learning tasks. We introduce several mutation
operators inspired by the transformation rules of ZX-calculus and investigate
their impact on the learning efficiency and accuracy of quantum circuits. The
empirical analysis involves a comparative study where these mutations are
applied to a diverse set of quantum regression problems, measuring performance
metrics such as the percentage of valid circuits after the mutation,
improvement of the objective, as well as circuit depth and width. Our results
indicate that certain ZX-calculus-based mutations perform significantly better
than others for Quantum Architecture Search (QAS) in all metrics considered.
They suggest that ZX-diagram based QAS results in shallower circuits and more
uniformly allocated gates than crude genetic optimization based on the circuit
model. |
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DOI: | 10.48550/arxiv.2406.01095 |