Simulating Clifford Circuits with Gaussian Elimination

• URL: http://arxiv.org/abs/2511.06127v1
• Date: Sat, 08 Nov 2025 20:37:49 GMT
• Title: Simulating Clifford Circuits with Gaussian Elimination
• Authors: Yuchen Pang, Edgar Solomonik,
• Abstract summary: Quantum circuits are more powerful than classical circuits and require exponential resources to simulate classically.<n>We present an algorithm that simulates Clifford circuits by performing Gaussian elimination on a modified adjacency matrix.<n>Our algorithm is also efficient when computing many amplitudes or samples of a Clifford circuit.
• Score: 2.8197894745858645
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum circuits are considered more powerful than classical circuits and require exponential resources to simulate classically. Clifford circuits are a special class of quantum circuits that can be simulated in polynomial time but still show important quantum effects such as entanglement. In this work, we present an algorithm that simulates Clifford circuits by performing Gaussian elimination on a modified adjacency matrix derived from the circuit structure. Our work builds on an ZX-calculus tensor network representation of Clifford circuits that reduces to quantum graph states. We give a concise formula of amplitudes of graph states based on the LDL decomposition of matrices over GF(2), and use it to get efficient algorithms for strong and weak simulation of Clifford circuits using tree-decomposition-based fast LDL algorithm. The complexity of our algorithm matches the state of art for weak graph state simulation and improves the state of art for strong graph state simulation by taking advantage of Strassen-like fast matrix multiplication. Our algorithm is also efficient when computing many amplitudes or samples of a Clifford circuit. Further, our amplitudes formula provides a new characterization of locally Clifford equivalent graph states as well as an efficient protocol to learn graph states with low-rank adjacency matrices.

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