Efficient template matching in quantum circuits

Yuxiang Liu, Zaichen Zhang, Yi Yong Hu +4 more

Zahra Mirkhani, Naser Mohammadzadeh

Laxmidhar Biswal, Rakesh Das, Chandan Bandyopadhyay +2 more

Md. Mazder Rahman, Gerhard W. Dueck, Joseph D. Horton

Quantum circuits are often generated by decomposing gates from networks with classical reversible gates. Only in rare cases, the results are minimal. Post-optimization methods, such as template matching, are employed to reduce the quantum costs of circuits. Quantum templates are derived from identity circuits. All minimal realizations, within certain limitations, can be embedded into templates. Due to this property, templates matching has the potential to reduce quantum costs of circuits. However, one of the difficulties in finding templates matches is due to the mobility of the gates within the circuit. Thus far, template matching procedures have employed heuristics to reduce the search space. This article presents an in-depth study of exact template matching with a set of algorithms. A graph structure with the corresponding circuits facilitates the discovery of potential sequences of templates to be matched, and how exact minimization of circuits can be accomplished. The significance of the proposed method is verified in benchmarks optimization.

Xin Hong, Jintao Yu, Runhong He +3 more

Quantum circuit optimization is a key step toward the efficient execution of quantum algorithms. Template matching has emerged as one of the dominant approaches for simplifying quantum circuits, yet it confronts the intrinsic challenge of accommodating gate commutativity. The state-of-the-art template-matching algorithm relies on a directed acyclic graph (DAG) representation. While this DAG-based technique handles gate commutativity satisfactorily, it fails to preserve the local connectivity inherent to quantum circuits, thereby leading to relatively high matching complexity. In this paper, we introduce a hypergraph representation (HG), a commutativity-aware representation that collapses commuting gates into a single super-node while retaining all local connectivity. This enables matches to be extended locally without revisiting the rest of the circuit, with incremental updates limited to the immediate neighborhood. Experimental results demonstrate that our HG matcher achieves 11--606x speed-up over the DAG-based implementation in Qiskit (Iten et al., 2022) on both random and arithmetic benchmarks, while maintaining the same optimization quality. The acceleration increases with circuit size, confirming that preserving locality and connectivity is the key to scalable quantum circuit optimisation.

François Chapeau‐Blondeau