Application of ZX-calculus to Quantum Architecture Search
arXiv:2406.01095 · doi:10.1007/s42484-025-00264-6
Abstract
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.
10 Pages, 10 figures, 1 algorithm
References in corpus (21)
- Quantum Machine Learning
- Barren plateaus in quantum neural network training landscapes
- Parameterized quantum circuits as machine learning models
- An adaptive variational algorithm for exact molecular simulations on a quantum computer
- qubit-ADAPT-VQE: An adaptive algorithm for constructing hardware-efficient ansatze on a quantum processor
- Is quantum advantage the right goal for quantum machine learning?
- Structure optimization for parameterized quantum circuits
- Graph-theoretic Simplification of Quantum Circuits with the ZX-calculus
- Quantum circuit architecture search for variational quantum algorithms
- Differentiable Quantum Architecture Search
- Reducing T-count with the ZX-calculus
- PyZX: Large Scale Automated Diagrammatic Reasoning
- There and back again: A circuit extraction tale
- Robust resource-efficient quantum variational ansatz through evolutionary algorithm
- Quantum Architecture Search via Deep Reinforcement Learning
- A semi-agnostic ansatz with variable structure for quantum machine learning
- Graphical Structures for Design and Verification of Quantum Error Correction
- Evolutionary Quantum Architecture Search for Parametrized Quantum Circuits
- Annealing Optimisation of Mixed ZX Phase Circuits
- Quantum Architecture Search for Quantum Monte Carlo Integration via Conditional Parameterized Circuits with Application to Finance
- Differentiating and Integrating ZX Diagrams with Applications to Quantum Machine Learning