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Classical Simulability of Quantum Circuits with Shallow Magic Depth

Quantum Physics 2025-02-07 v2 Computational Complexity

Abstract

Quantum magic is a necessary resource for quantum computers to be not efficiently simulable by classical computers. Previous results have linked the amount of quantum magic, characterized by the number of TT gates or stabilizer rank, to classical simulability. However, the effect of the distribution of quantum magic on the hardness of simulating a quantum circuit remains open. In this work, we investigate the classical simulability of quantum circuits with alternating Clifford and TT layers across three tasks: amplitude estimation, sampling, and evaluating Pauli observables. In the case where all TT gates are distributed in a single layer, performing amplitude estimation and sampling to multiplicative error are already classically intractable under reasonable assumptions, but Pauli observables are easy to evaluate. Surprisingly, with the addition of just one TT gate layer or merely replacing all TT gates with T12T^{\frac{1}{2}}, the Pauli evaluation task reveals a sharp complexity transition from P to GapP-complete. Nevertheless, when the precision requirement is relaxed to 1/poly(nn) additive error, we are able to give a polynomial time classical algorithm to compute amplitudes, Pauli observable, and sampling from log(n)\log(n) sized marginal distribution for any magic-depth-one circuit that is decomposable into a product of diagonal gates. Our research provides new techniques to simulate highly magical circuits while shedding light on their complexity and their significant dependence on the magic depth.

Keywords

Cite

@article{arxiv.2409.13809,
  title  = {Classical Simulability of Quantum Circuits with Shallow Magic Depth},
  author = {Yifan Zhang and Yuxuan Zhang},
  journal= {arXiv preprint arXiv:2409.13809},
  year   = {2025}
}

Comments

improved Algorithm 1 to non-local diagonal magic gates; improved multiplicative error in hardness; restructured the SUMMARY OF RESULTS section

R2 v1 2026-06-28T18:51:52.117Z