The growing demand for fault-tolerant quantum computing drives the need for efficient, scalable Quantum Error Correction (QEC) strategies. Conventional decoders designed for worst-case error scenarios incur significant overhead, prompting the development of local decoders, that leverage the sparse and often trivial nature of many quantum errors, to support the conventional decoders. The previously proposed Clique decoder addresses this by handling isolated, length-1 space and time errors within the cryogenic environment with minimal hardware costs, thereby mitigating I/O bandwidth constraints between cryogenic quantum systems and room-temperature processors. Building on this foundation, we propose Clique_L2 that extends the Clique-based approach by relaxing some original constraints and incorporating additional low-cost logic to also correct length-2 error chains in space, which become non-trivial occurrences at higher physical error rates and code distances. This enhanced capability not only further reduces out-of-thefridge data transmission but also adapts more effectively to clustered errors observed under a variety of noise models. Specifically, under data-qubit-only errors and uniformly random noise, Clique_L2 achieves up to 8.95x decoding bandwidth reduction over the original Clique (or Clique_L1) decoder, especially beneficial at higher code distances. When clustered errors and longer error chains are more likely to occur, Clique_L2 achieves up to$18.3 x$decoding bandwidth reduction over Clique_L1, achieving substantial benefits across a wide range of physical qubit error rates.
The growing demand for fault-tolerant quantum computing drives the need for efficient, scalable Quantum Error Correction (QEC) strategies. Conventional decoders designed for worst-case error scenarios incur significant overhead, prompting the development of local decoders, that leverage the sparse and often trivial nature of many quantum errors, to support the conventional decoders. The previously proposed Clique decoder addresses this by handling isolated, length-1 space and time errors within the cryogenic environment with minimal hardware costs, thereby mitigating I/O bandwidth constraints between cryogenic quantum systems and room-temperature processors. Building on this foundation, we propose Clique_L2 that extends the Clique-based approach by relaxing some original constraints and incorporating additional low-cost logic to also correct length-2 error chains in space, which become non-trivial occurrences at higher physical error rates and code distances. This enhanced capability not only further reduces out-of-thefridge data transmission but also adapts more effectively to clustered errors observed under a variety of noise models. Specifically, under data-qubit-only errors and uniformly random noise, Clique_L2 achieves up to 8.95x decoding bandwidth reduction over the original Clique (or Clique_L1) decoder, especially beneficial at higher code distances. When clustered errors and longer error chains are more likely to occur, Clique_L2 achieves up to18.3xdecoding bandwidth reduction over Clique_L1, achieving substantial benefits across a wide range of physical qubit error rates.
Quantum error correction (QEC) is considered essential for the development of scalable, fault-tolerant quantum computers in the medium- to long-term. By encoding quantum information across many physical qubits, carefully designed measurements can be taken to gain limited knowledge as to errors which may have occurred in the system, known as the error syndrome, with out causing quantum decoherence. In surface codes, qubits are arranged in a two-dimensional topological space, such as a torus or plane. In quantum low-density parity-check (qLDPC) codes, this is generalised to effectively arbitrary arrangements of qubits and syndrome checks. This work studies decoders: the algorithms responsible for inferring error correcting decisions from syndromes. In particular, the union–find decoder is studied in terms of its performance at scale. A simulation of a union–find decoding architecture is used to identify computational bottlenecks and pro pose improvements. In literature, it is assumed that the decoder runs in time scaling quadratically in the number of qubits under a naive implementation, but near-linearly when including two well-known optimisations. A key result is that, under independent and phenomenological noise models on surface codes, this complexity is strictly linear regardless of these optimisations. A supporting analytical argument is presented using percolation theory. Generalisation of the decoding problem to qLDPC codes is also studied, with approaches broadly relying on Gaussian elimination to find appropriate solutions. A strategy is proposed which uses a novel online variant of the Gaussian elimination algorithm to solve this linear system incrementally on growing local clusters, with accompanying complexity analysis and empir ical data demonstrating a reduction in runtime. An investigation into the use of metachecks is also presented, inspired by single-shot decoding, with implications for how such qLDPC decoders could be further improved.
Quantum error correction (QEC) represents a critical enabler for fault-tolerant quantum computing, protecting fragile quantum states from decoherence and operational errors. This paper provides a comprehensive analysis of leading quantum error correction schemes, focusing on surface codes and topological codes within the context of Noisy Intermediate-Scale Quantum (NISQ) era processors. We examine the theoretical foundations of stabilizer codes, compare surface code implementations with code distances ranging from d=3 to d=17, and analyze topological codes including toric codes and color codes. Our investigation encompasses syndrome extraction circuits, logical qubit encoding strategies, and threshold analysis for contemporary quantum hardware platforms. Performance metrics reveal that surface codes achieve error thresholds of approximately 1% with realistic noise models, while topological codes offer architectural advantages for specific qubit connectivity constraints. We discuss implementation challenges on current NISQ processors including IBM Quantum, Google Sycamore, and IonQ systems, addressing qubit overhead, gate fidelity requirements, and decoding algorithms. This analysis provides insights for selecting appropriate error correction schemes based on application requirements and hardware capabilities.
Quantum Error Correction (QEC) is essential for future quantum computers due to its ability to exponentially suppress physical errors. The surface code is a leading error-correcting code candidate because of its local topological structure, experimentally achievable thresholds, and support for universal gate operations with magic states. However, its physical overhead scales quadratically with number of correctable errors. Conversely, quantum low-density parity-check (qLDPC) codes offer superior scaling but lack, on their own, a clear path to universal logical computation. Therefore, it is becoming increasingly evident is becoming that there are significant advantages to designing architectures using multiple codes. Heterogeneous architectures provide a clear path to universal logical computation as well as the ability to access different resource trade offs. To address this, we propose integrating the surface code and gross code using an ancilla bus for inter-code data movement. This approach involves managing trade-offs, including qubit overhead, a constrained instruction set, and gross code (memory) routing and management. While our focus is on the gross-surface code architecture, our method is adaptable to any code combination and the constraints generated by that specific architecture. Motivated by the potential reduction of physical qubit overhead, an ever important feature in the realization of fault tolerant computation, we perform the first full system study of heterogeneous error-correcting codes, discovering architectural trade-offs and optimizing around them. We demonstrate physical qubit reductions of up to 6.42x when executing an algorithm to a specific logical error rate, at the cost of up to a 3.43x increase in execution time.
Quantum Error Correction (QEC) is essential for building robust, fault-tolerant quantum computers; however, the decoding process often presents a significant computational bottleneck. Tesseract is a novel Most-Likely-Error (MLE) decoder for QEC that employs the A* search algorithm to explore an exponentially large graph of error hypotheses, achieving high decoding speed and accuracy. This paper presents a systematic approach to optimizing the Tesseract decoder through low-level performance enhancements. Based on extensive profiling, we implemented four targeted optimization strategies, including the replacement of inefficient data structures, reorganization of memory layouts to improve cache hit rates, and the use of hardware-accelerated bit-wise operations. We achieved significant decoding speedups across a wide range of code families and configurations, including Color Codes, Bivariate-Bicycle Codes, Surface Codes, and Transversal CNOT Protocols. Our results demonstrate consistent speedups of approximately 2x for most code families, often exceeding 2.5x. Notably, we achieved a peak performance gain of over 5x for the most computationally demanding configurations of Bivariate-Bicycle Codes. These improvements make the Tesseract decoder more efficient and scalable, serving as a practical case study that highlights the importance of high-performance software engineering in QEC and providing a strong foundation for future research.
A fault-tolerant quantum computer will be supported by a classical decoding system interfacing with quantum hardware to perform quantum error correction. It is important that the decoder can keep pace with the quantum clock speed, within the limitations on communication that are imposed by the physical architecture. To this end, we propose a local ``predecoder,'' which makes greedy corrections to reduce the amount of syndrome data sent to a standard matching decoder. We study these classical overheads for the surface code under a phenomenological phase-flip noise model with imperfect measurements. We find substantial improvements in the run time of the global decoder and the communication bandwidth by using the predecoder. For instance, to achieve a logical-failure probability of $f={10}^{\ensuremath{-}15}$ using qubits with physical error rate $p={10}^{\ensuremath{-}3}$ and a distance d=22 code, we find that the bandwidth cost is reduced by a factor of 1000 and that the time taken by a matching decoder is sped up by a factor of 200. To achieve this target failure probability, the predecoding approach requires a 50% increase in the qubit count compared with the optimal decoder.
Quantum error correction (QEC) is essential for scalable quantum computing. However, it requires classical decoders that are fast and accurate enough to keep pace with quantum hardware. While quantum low-density parity-check codes have recently emerged as a promising route to efficient fault tolerance, current decoding algorithms do not allow one to realize the full potential of these codes in practical settings. Here, we introduce a convolutional neural network decoder that exploits the geometric structure of QEC codes, and use it to probe a novel "waterfall" regime of error suppression, demonstrating that the logical error rates required for large-scale fault-tolerant algorithms are attainable with modest code sizes at current physical error rates, and with latencies within the real-time budgets of several leading hardware platforms. For example, for the [144,12,12] Gross code, the decoder achieves logical error rates up to ∼17x below existing decoders - reaching logical error rates ∼10−10 at physical error p=0.1% - with 3-5 orders of magnitude higher throughput. This decoder also produces well-calibrated confidence estimates that can significantly reduce the time overhead of repeat-until-success protocols. Taken together, these results suggest that the space-time costs associated with fault-tolerant quantum computation may be significantly lower than previously anticipated.