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arXiv:2103.02202 · Quantum

Stim: a fast stabilizer circuit simulator

Craig Gidney

Published Jul 6, 2021• 415 citations
arXivHTMLPDF

Abstract

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 i n v e r s e 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.

DOI
10.22331/q-2021-07-06-497

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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 i n v e r s e 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.
We demonstrate that Pauli frames, a mathematical object used to efficiently sample qubit stabilizer circuits, can be used to efficiently sample qudit circuits of any prime dimension. Quantum error correcting codes are a central object of study in fault-tolerance quantum computing, which is necessary to realize useful universal quantum computing. Stabilizer codes belong to a large class of well-understood quantum codes that are crucially efficiently simulable on classical hardware using the so-called tableau representation. Popular simulation software like Stim exclusively studies and characterizes qubit codes, which entails repeatedly sampling a noisy quantum circuit with Pauli frames. We highlight the key differences (and fundamental similarities) between qubit and qudit stabilizer tableaus. It then teaches how Pauli frames generate batches of samples given a qudit tableau via two guided examples: one with a random measurement outcome but no noise, and one with a single noise gate.
We present Tsim, an open-source high-throughput simulator for universal noisy quantum circuits targeting quantum error correction. Tsim represents quantum circuits as ZX diagrams, where Pauli channels are modeled as parameterized vertices. Diagrams are simplified via parameterized ZX rules, and then compiled for vectorized sampling with GPU acceleration. After the one-time compilation, one can sample detector or measurement shots in linear time in the number of Clifford gates and exponentially only in the number of non-Clifford gates. Tsim implements the Stim API and fully supports the Stim circuit format, extending it with T and arbitrary single-qubit rotation instructions. For low-magic circuits, Tsim throughput can match the sampling performance of Stim.
Quantum computers have steadily improved over the last decade, but developing fault-tolerant quantum computing (FTQC) techniques, required for useful, universal computation remains an ongoing effort. Key elements of FTQC such as error-correcting codes and decoding are supported by a rich bed of stabilizer simulation software such as Stim and CHP, which are essential for numerically characterizing these protocols at realistic scales. Recently, experimental groups have built nascent high-dimensional quantum hardware, known as qudits, which have a myriad of attractive properties for algorithms and FTQC. Despite this, there are no widely available qudit stabilizer simulators. We introduce the first open-source realization of such a simulator for all dimensions. We demonstrate its correctness against existing state vector simulations and benchmark its performance in evaluating and sampling quantum circuits. This simulator is the essential computational infrastructure to explore novel qudit error correction as earlier stabilizer simulators have been for qubits.
This paper proposes an efficient stabilizer circuit simulation algorithm that only traverses the circuit forward once. We introduce phase symbolization into stabilizer generators, which allows possible Pauli faults in the circuit to be accumulated explicitly as symbolic expressions in the phases of stabilizer generators. This way, the measurement outcomes are also symbolic expressions, and we can sample them by substituting the symbolic variables with concrete values, without traversing the circuit repeatedly. We show how to integrate symbolic phases into the stabilizer tableau and maintain them efficiently using bit-vector encoding. A new data layout of the stabilizer tableau in memory is proposed, which improves the performance of our algorithm (and other stabilizer simulation algorithms based on the stabilizer tableau). We implement our algorithm and data layout in a Julia package named SymPhase.jl, and compare it with Stim, the state-of-the-art simulator, on several benchmarks. We show that SymPhase.jl has superior performance in terms of sampling time, which is crucial for generating a large number of samples for further analysis.
We introduce new parallel algorithms for efficiently simulating stabilizer (Clifford) circuits on GPUs, with a focus on data-parallel tableau evolution and scalable handling of projective measurements. Our approach reformulates key bottlenecks in stabilizer simulation -- such as Gaussian elimination and measurement updates -- into GPU-tailored primitives that eliminate sequential dependencies and maximize memory coalescing. We implement these techniques in QuaSARQ, a GPU-accelerated stabilizer simulator designed for large qubit counts and many-shot sampling. Across a broad benchmark suite reaching 180,000 qubits and depth 1,000 (roughly 130M gates), QuaSARQ shows substantial runtime improvements, with up to 105×\times× speedup, and over 80% energy reduction on demanding instances. Moreover, QuaSARQ consistently outperforms Stim, a state-of-the-art CPU-optimized stabilizer simulator, as well as Qiskit-Aer (CPU/GPU), Qibo, Cirq, and PennyLane. Finally, QuaSARQ exhibits a significant advantage in many-shot sampling on large workloads. These results demonstrate that our parallel algorithms can significantly advance the scalability of stabilizer-circuit simulation, particularly for workloads involving extensive measurements and sampling.
Abstract Equivalence checking plays a crucial role in quantum circuit compilation, optimization, and verification. Stabilizer circuits can be simulated classically by tracking the so-called stabilizer operators in linear time. But the simulation of large stabilizer circuits with thousands of qubits and gates, arising e.g. in the study of novel quantum error-correction protocols, still poses a challenge. In this work, we propose a GPU-based deterministic algorithm for equivalence checking of stabilizer circuits using the stabilizer tableau formalism. We explore various design choices and implement the most efficient version. Our algorithm significantly outperforms the state-of-the-art CCEC checker (which relies on the Stim simulator) in terms of time, memory, and energy. Our approach demonstrates up to two orders of magnitude speedup over existing methods. Notably, previous attempts at GPU acceleration in this area were unsuccessful, making this the first effective implementation.