Stim: a fast stabilizer circuit simulator

By Craig Gidney. Quantum, 2021

701 citations recorded by the source corpus.

This paper presents “Stim", a fast simulator for quantum stabilizer circuits. The paper explains how Stim works and compares it to existing tools. With no foreknowledge, Stim can analyze a distance 100 surface code circuit (20 thousand qubits, 8 million gates, 1 million measurements) in 15 seconds and then begin sampling full circuit shots at a rate of 1 kHz. Stim uses a stabilizer tableau representation, similar to Aaronson and Gottesman's CHP simulator, but with three main improvements. First, Stim improves the asymptotic complexity of deterministic measurement from quadratic to linear by tracking the inverse of the circuit's stabilizer tableau. Second, Stim improves the constant factors of the algorithm by using a cache-friendly data layout and 256 bit wide SIMD instructions. Third, Stim only uses expensive stabilizer tableau simulation to create an initial reference sample. Further samples are collected in bulk by using that sample as a reference for batches of Pauli frames propagating through the circuit.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/764276eaddc0bd27954d32bc489b0bc8f3ca0a30

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Citations in the corpus

Quantum error correction below the surface code threshold (2024, positive): “We note there are other accurate decoders that would be interesting to test on this data, including beliefmatch-ing [13], BP+OSD [14], and tensor network decoding [1].”
Magic state cultivation: growing T states as cheap as CNOT gates (2024, neutral): “Stim [Gid21] has a class stim.”
Suppressing quantum errors by scaling a surface code logical qubit (2022, positive): “This simulation is based on the Stim open source library[62].”
Logical quantum processor based on reconfigurable atom arrays (2023, methodology): “We start by constructing a decoding hypergraph based on a description of the logical algorithm, which describes how each physical error mechanism (for example, a Pauli-error channel after a two-qubit gate) propagates onto the measured stabilizers 76,81 .”
Sparse Blossom: correcting a million errors per core second with minimum-weight matching (2023, positive): “While there has been progress in the development of open-source software packages for decoding surface codes [Hig22; Tuc20], these tools are much slower than stabilizer circuit simulators [Gid21], and have therefore been a bottleneck in surface code simulations.”