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arXiv:2111.13728

QECV: Quantum Error Correction Verification

Anbang Wu, Gushu Li, Hezi Zhang, Gian Giacomo Guerreschi, Yuan Xie, Yufei Ding

Published Nov 26, 2021• 0 citations
arXivHTMLPDF

Abstract

Quantum Error Correction (QEC) is essential for fault-tolerant quantum copmutation, and its implementation is a very sophisticated process involving both quantum and classical hardware. Formulating and verifying the decomposition of logical operations into physical ones is a challenge in itself. In this paper, we propose QECV, a verification framework that can efficiently verify the formal correctness of stabilizer codes, arguably the most important class of QEC codes. QECV first comes with a concise language, QECV-Lang, where stabilizers are treated as a first-class object, to represent QEC programs. Stabilizers are also used as predicates in our new assertion language, QECV-Assn, as logical and arithmetic operations of stabilizers can be naturally defined. We derive a sound quantum Hoare logic proof system with a set of inference rules for QECV to efficiently reason about the correctness of QEC programs. We demonstrate the effectiveness of QECV with both theoretical complexity analysis and in-depth case studies of two well-known stabilizer QEC codes, the repetition code and the surface code.

DOI
10.48550/arxiv.2111.13728

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Quantum Error Correction (QEC) is essential for fault-tolerant quantum copmutation, and its implementation is a very sophisticated process involving both quantum and classical hardware. Formulating and verifying the decomposition of logical operations into physical ones is a challenge in itself. In this paper, we propose QECV, a verification framework that can efficiently verify the formal correctness of stabilizer codes, arguably the most important class of QEC codes. QECV first comes with a concise language, QECV-Lang, where stabilizers are treated as a first-class object, to represent QEC programs. Stabilizers are also used as predicates in our new assertion language, QECV-Assn, as logical and arithmetic operations of stabilizers can be naturally defined. We derive a sound quantum Hoare logic proof system with a set of inference rules for QECV to efficiently reason about the correctness of QEC programs. We demonstrate the effectiveness of QECV with both theoretical complexity analysis and in-depth case studies of two well-known stabilizer QEC codes, the repetition code and the surface code.
Quantum error correction (QEC) is fundamental for suppressing noise in quantum hardware and enabling fault-tolerant quantum computation. In this paper, we propose an efficient verification framework for QEC programs. We define an assertion logic and a program logic specifically crafted for QEC programs and establish a sound proof system. We then develop an efficient method for handling verification conditions (VCs) of QEC programs: for Pauli errors, the VCs are reduced to classical assertions that can be solved by SMT solvers, and for non-Pauli errors, we provide a heuristic algorithm. We formalize the proposed program logic in Coq proof assistant, making it a verified QEC verifier. Additionally, we implement an automated QEC verifier, Veri-QEC, for verifying various fault-tolerant scenarios. We demonstrate the efficiency and broad functionality of the framework by performing different verification tasks across various scenarios. Finally, we present a benchmark of 14 verified stabilizer codes.
Abstract Quantum computers have advanced rapidly in qubit count and gate fidelity. However, large-scale fault-tolerant quantum computing still relies on quantum error correction code (QECC) to suppress noise. Manually or experimentally verifying the fault-tolerance property of complex QECC implementation is impractical due to the vast error combinations. This paper formalizes the fault-tolerance of QECC implementations within the language of quantum programs. By incorporating the techniques of quantum symbolic execution, we provide an automatic verification tool for quantum fault-tolerance. We evaluate and demonstrate the effectiveness of our tool on a universal set of logical operations across different QECCs.
We define QSE, a symbolic execution framework for quantum programs by integrating symbolic variables into quantum states and the outcomes of quantum measurements. The soundness of QSE is established through a theorem that ensures the correctness of symbolic execution within operational semantics. We further introduce symbolic stabilizer states, which symbolize the phases of stabilizer generators, for the efficient analysis of quantum error correction (QEC) programs. Within the QSE framework, we can use symbolic expressions to characterize the possible discrete Pauli errors in QEC, providing a significant improvement over existing methods that rely on sampling with simulators. We implement QSE with the support of symbolic stabilizer states in a prototype tool named QuantumSE.jl . Our experiments on representative QEC codes, including quantum repetition codes, Kitaev’s toric codes, and quantum Tanner codes, demonstrate the efficiency of QuantumSE.jl for debugging QEC programs with over 1000 qubits. In addition, by substituting concrete values in symbolic expressions of measurement results, QuantumSE.jl is also equipped with a sampling feature for stabilizer circuits. Despite a longer initialization time than the state-of-the-art stabilizer simulator, Google’s Stim, QuantumSE.jl offers a quicker sampling rate in the experiments.
Quantum error correction (QEC) enables reliable computation on noisy hardware by encoding logical information across many physical qubits and periodically measuring parities to detect errors. A decoder is the classical algorithm that uses these measurements to infer which error most likely occurred, so that the system can correct it. The decoder's accuracy-how rarely it makes the wrong guess-directly determines the scale of quantum computation that can be reliably executed. With a wealth of competing decoding algorithms, a QEC system designer needs reliable methods to evaluate them. Today, the dominant approach is to evaluate decoders using Monte Carlo simulation. However, simulation has several drawbacks such as requiring many samples to produce low variance estimates. In this work, we develop a new systematic analysis for evaluating decoders. We introduce a novel formal semantics of a core language for QEC programs that captures the de facto standard Stim circuit format, providing a principled theoretical foundation for the emerging space of fault-tolerant quantum systems design. Given a QEC program and a decoder, our verifier can quantify both the decoder accuracy and the decoder robustness to drift in physical error rate. Our approach has two key components: (i) a structured search over the space of possible errors; and (ii) a constrained polynomial optimization kernel. A thorough empirical evaluation of our approach suggests that it can outperform simulation, especially in low error rate regimes, and that it can be deployed to quantify decoder robustness over an interval of physical error rates.
This paper presents a novel approach to quantum error correction (QEC) by formally specifying QEC codes using mathematical logic. The core idea is to translate the mathematical definitions and properties of QEC codes into logical formulas, enabling automated verification and facilitating the design of new, more efficient codes. This approach moves beyond intuitive understanding and provides a rigorous, verifiable framework. The paper outlines the methodology, demonstrates its application with a simplified example, and highlights the potential benefits of this formal specification technique for QEC research and development. The focus is on building a foundation for automated code design and analysis, crucial for the practical implementation of large-scale quantum computers. This work contributes to a more robust and trustworthy approach to QEC, addressing the challenges of complexity and verification inherent in traditional design methods.
Quantum error-correcting codes (QECCs) sit between noisy quantum hardware and reliable computation, so the code parameters used in practice must be trustworthy. The single number that summarizes a code's strength is its distance, yet certifying a distance lower bound is NP-hard in general, placing it beyond the reach of pen-and-paper proofs as well as direct proof-assistant scripting. As a result, distance values in the literature come either from non-scaling hand proofs, or from unverified solvers that leave a trust gap exactly where the code is supposed to provide a guarantee. We present Lean-QEC, the first Lean 4 formalization of stabilizer-code theory that delivers end-to-end, machine-checked distance certificates at industrial code sizes. Lean-QEC formalizes the linear algebra of qubit states, the Pauli group, stabilizer codes, the binary symplectic representation, classical coding theory, and the CSS and Bivariate Bicycle families. To break the combinatorial barrier, Lean-QEC translates the distance condition into a Boolean satisfiability formula through a verified reduction. The pipeline scales through a BitVec-flattened encoding that replaces Lean's Matrix representation, and an error-location encoding that reduces the variable count from nnn to k⌈log⁡2n⌉k\lceil \log_2 n\rceilk⌈log2​n⌉. With these, we obtain automatically-generated Lean-checked distance proofs for a large range of industrially viable qLDPC codes within the Bivariate Bicycle and Generalized Bicycle families, including [[90, 8, 10]] and [[70, 6, 9]] BB codes, with the formulation scaling up to 144 qubits when performed outside the Lean kernel. The resulting library is reusable and is designed to plug into broader Lean-based efforts toward end-to-end verification of fault-tolerant quantum computation.