analiza aiqbench
SymFT introduces a significant performance leap in simulating fault-tolerant quantum circuits by combining symbolic Clifford-Pauli frame factorization with adaptive stabilizer-coordinate planning. This approach reduces computational overhead for near-Clifford operations, achieving up to 3.5x speedups over existing tools like Stim and Clifft on single CPU cores while handling complex noise models and feedback loops. The work directly addresses the bottleneck of exact sampling in universal quantum protocols, offering a practical tool for scaling error-correction research without requiring specialized hardware emulation.
SymFT: Universal Fault-Tolerant Quantum Circuit Simulation via Symbolic Clifford--Pauli Frames and Stabilizer Coordinates
arXiv:2607.28600v1 Announce Type: new Abstract: Fault-tolerant protocols often consist largely of stabilizer subcircuits, yet the non-Clifford operations required for universality make exact sampling costly. We present SymFT, a high-throughput simulator for Clifford-dominated circuits with Pauli rotations, stochastic Pauli noise, mid-circuit Pauli measurements, and measurement-record-controlled Pauli feedback. It combines two ideas. First, symbolic Clifford--Pauli frame factorization reduces branch-probability sampling to Pauli rotations and measurement projectors, with noise and feedback represented by symbolic signs. Since the residual Clifford and Pauli frames are unitary, they do not affect branch probabilities and need not be applied in every shot. Second, adaptive stabilizer-coordinate planning uses a shared stabilizer--destabilizer tableau to define the basis and stores only the active non-stabilizer degrees of freedom in a dynamically sized dense active-state vector. It resolves basis changes once and emits direct multi-coordinate sampling instructions, thereby avoiding per-shot tableau updates and localization-induced Clifford transformations of the dense vector. Across the tested pure-Clifford and near-Clifford circuits, SymFT achieves state-of-the-art sampling performance. On a single CPU core, it is $2.51\text{--}2.56\times$ faster than Stim for surface-code circuits and $1.86\text{--}3.51\times$ faster than Clifft for magic-state cultivation and distillation circuits. For the tested cultivation circuits, its sampling throughput also exceeds that of our previous simulator, SOFT, by more than two orders of magnitude.
Abstract:Fault-tolerant protocols often consist largely of stabilizer subcircuits, yet the non-Clifford operations required for universality make exact sampling costly. We present SymFT, a high-throughput simulator for Clifford-dominated circuits with Pauli rotations, stochastic Pauli noise, mid-circuit Pauli measurements, and measurement-record-controlled Pauli feedback. It combines two ideas. First, symbolic Clifford--Pauli frame factorization reduces branch-probability sampling to Pauli rotations and measurement projectors, with noise and feedback represented by symbolic signs. Since the residual Clifford and Pauli frames are unitary, they do not affect branch probabilities and need not be applied in every shot. Second, adaptive stabilizer-coordinate planning uses a shared stabilizer--destabilizer tableau to define the basis and stores only the active non-stabilizer degrees of freedom in a dynamically sized dense active-state vector. It resolves basis changes once and emits direct multi-coordinate sampling instructions, thereby avoiding per-shot tableau updates and localization-induced Clifford transformations of the dense vector. Across the tested pure-Clifford and near-Clifford circuits, SymFT achieves state-of-the-art sampling performance. On a single CPU core, it is $2.51\text{--}2.56\times$ faster than Stim for surface-code circuits and $1.86\text{--}3.51\times$ faster than Clifft for magic-state cultivation and distillation circuits. For the tested cultivation circuits, its sampling throughput also exceeds that of our previous simulator, SOFT, by more than two orders of magnitude.
28 pages, 1 figure. SymFT is the second-generation successor to SOFT, see GitHub repository: this https URL
arXiv:2607.28600 [quant-ph]
(or arXiv:2607.28600v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2607.28600 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 311 cuvinte
