mlsanityData to 14 Oct 2026

Graph-theoretic Simplification of Quantum Circuits with the ZX-calculus

arXiv 1902.03178 ↗First posted 8 Feb 2019quant-ph cs.LO math.COcited by 20

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Abstract (arXiv)

We present a completely new approach to quantum circuit optimisation, based on the ZX-calculus. We first interpret quantum circuits as ZX-diagrams, which provide a flexible, lower-level language for describing quantum computations graphically. Then, using the rules of the ZX-calculus, we give a simplification strategy for ZX-diagrams based on the two graph transformations of local complementation and pivoting and show that the resulting reduced diagram can be transformed back into a quantum circuit. While little is known about extracting circuits from arbitrary ZX-diagrams, we show that the underlying graph of our simplified ZX-diagram always has a graph-theoretic property called generalised flow, which in turn yields a deterministic circuit extraction procedure. For Clifford circuits, this extraction procedure yields a new normal form that is both asymptotically optimal in size and gives a new, smaller upper bound on gate depth for nearest-neighbour architectures. For Clifford+T and more general circuits, our technique enables us to to `see around' gates that obstruct the Clifford structure and produce smaller circuits than naive 'cut-and-resynthesise' methods.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Feb 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 7; 2022: 1; 2023: 3; 2024: 4; 2025: 3; 2026: 2.

20 papers.

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  1. 2021Noisy intermediate-scale quantum (NISQ) algorithmscited by 79
  2. 2021Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculuscited by 30
  3. 2021Compositionality as we see it, everywhere around uscited by 11
  4. 2024Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospectscited by 8
  5. 2023Optimizing ZX-Diagrams with Deep Reinforcement Learningcited by 6
  6. 2021Diagrammatic Differentiation for Quantum Machine Learningcited by 5
  7. 2021How to make qubits speakcited by 5
  8. 2021Kindergarden quantum mechanics graduates (...or how I learned to stop gluing LEGO together and love the ZX-calculus)cited by 4
  9. 2023Solving Quantum-Inspired Perfect Matching Problems via Tutte's Theorem-Based Hybrid Boolean Constraintscited by 3
  10. 2024AltGraph: Redesigning Quantum Circuits Using Generative Graph Models for Efficient Optimizationcited by 3
  11. 2025Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection Systemscited by 3
  12. 2025Optimizing Quantum Circuits via ZX Diagrams using Reinforcement Learning and Graph Neural Networkscited by 2
8 more
  1. 2026Equivariant Reinforcement Learning for Clifford Quantum Circuit Synthesiscited by 2
  2. 2022Differentiating and Integrating ZX Diagrams with Applications to Quantum Machine Learningcited by 1
  3. 2026Quantum Models with Multi-Stage Training for Compositional Concept Generalizationcited by 1
  4. 2021Representing and Implementing Matrices Using Algebraic ZX-calculuscited by 0
  5. 2023Compilation of product-formula Hamiltonian simulation via reinforcement learningcited by 0
  6. 2024Virtual Reality for Understanding Artificial-Intelligence-driven Scientific Discovery with an Application in Quantum Opticscited by 0
  7. 2024Tackling Decision Processes with Non-Cumulative Objectives using Reinforcement Learningcited by 0
  8. 2025One-Shot Structured Pruning of Quantum Neural Networks via q-Group Engineering and Quantum Geometric Metricscited by 0