Polynomial-Time Classical Simulation of Noisy IQP Circuits with Constant Depth
Joel Rajakumar, James D. Watson, Yi-Kai Liu
Abstract
Sampling from the output distributions of quantum computations comprising only commuting gates, known as instantaneous quantum polynomial (IQP) computations, is believed to be intractable for classical computers, and hence this task has become a leading candidate for testing the capabilities of quantum devices. Here we demonstrate that for an arbitrary IQP circuit undergoing dephasing or depolarizing noise, whose depth is greater than a critical 𝑂 (1) threshold, the output distribution can be efficiently sampled by a classical computer. Unlike other simulation algorithms for quantum supremacy tasks, we do not require assumptions on the circuit's architecture, on anti-concentration properties, nor do we require Ω(log(𝑛)) circuit depth. We take advantage of the fact that IQP circuits have deep sections of diagonal gates, which allows the noise to build up predictably and induce a large-scale breakdown of entanglement within the circuit. Our results suggest that quantum supremacy experiments based on IQP circuits may be more susceptible to classical simulation than previously thought. Our results also imply that fault-tolerance within the IQP framework cannot be achieved past a critical circuit depth. Furthermore, we show that the critical depth threshold of our algorithm is tight, and below this threshold there are noisy IQP circuits which are hard to sample from. Thus we demonstrate that noisy IQP circuits exhibit a phase transition in the computational complexity of sampling, as circuit depth is increased.
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Install the CLIlune papers fulltext 504ad029-3f77-4971-9795-c2e4b5197c40Cited by top-tier papers2
- Quantum Computational Advantage with Constant-Temperature Gibbs SamplingThiago Bergamaschi, Chi-Fang Chen, Yunchao LiuFOCS 2024 · 10 citations
- Polynomial-Time Classical Simulation of Noisy Quantum Circuits with Naturally Fault-Tolerant GatesJon Nelson, Joel Rajakumar, Dominik Hangleiter, Michael J. GullansSODA 2026
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