International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)
HetEC: Architectures for Heterogeneous Quantum Error Correction Codes
Samuel Stein, Shifan Xu, Andrew W. Cross, Theodore J. Yoder, Ali Javadi-Abhari, Chenxu Liu, Kun Liu, Zeyuan Zhou, Charles R. Guinn, Yufei Ding, Yongshan Ding, Ang Li
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 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.
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 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.
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.
Fault-tolerant quantum computing (FTQC) is required to execute quantum algorithms capable of tackling computationally intractable problems in chemistry, material science, and cryptography. Noisy Intermediate-Scale Quantum (NISQ) devices remain constrained by physical error rates that degrade quantum coherence over short circuit depths. Quantum error correction (QEC) protects fragile logical states by encoding them into non-local degrees of freedom across topological physical qubit arrays. This research presents a comprehensive architectural and theoretical evaluation of three primary QEC paradigms: planar surface codes, topological color codes, and asymptotic quantum low-density parity-check (qLDPC) codes. We examine error thresholds, transversal gate constraints governed by the Eastin-Knill theorem, magic state distillation (MSD) overheads, lattice surgery, and classical decoding latencies across superconducting transmons, trapped ions, and neutral atom platforms.
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.
Identifying the best families of quantum error correction (QEC) codes for near-term experiments is key to enabling fault-tolerant quantum computing. Ideally, such codes should have low overhead in qubit number, high physical error thresholds, and moderate requirements on qubit connectivity to simplify experiments, while allowing for high logical error suppression. Quantum low-density parity-check (LDPC) codes have been recently shown to provide a path towards QEC with low qubit overhead and small logical error probabilities. Here, we demonstrate that when the dominant errors are erasures, as can be engineered in different quantum computing architectures, quantum LDPC codes additionally provide high thresholds and even stronger logical error suppression in parameter regimes that are accessible to current experiments. Using large-scale QEC numerical simulations, we benchmark the performance of two families of high-rate quantum LDPC codes, namely the so-called Clifford-deformed La-cross codes and the bivariate bicycle codes, under a noise model strongly biased towards erasure errors. Both codes outperform the surface code by offering up to orders of magnitude lower logical error probabilities. Interestingly, we find that this decrease in the logical error probability may not be accompanied by an increase in the code threshold, as different QEC codes benefit differently from large erasure fractions. While here we focus on neutral atom qubits, the results also hold for other quantum platforms, such as trapped ions and superconducting qubits, for which erasure conversion has been demonstrated.
Quantum error correction (QEC) [1,2] is essential for the realization of large-scale quantum computers [3,4]. However, due to the complexity of operating on the encoded ‘logical’ qubits [5,6], understanding the physical principles for building fault-tolerant quantum devices and combining them into efficient architectures is an outstanding scientific challenge. Here we utilize reconfigurable arrays of up to 448 neutral atoms to implement the key elements of a universal, fault-tolerant quantum processing architecture and experimentally explore their underlying working mechanisms. We first employ surface codes to study how repeated QEC suppresses errors [6,7], demonstrating 2.14(13)x below-threshold performance in a four-round characterization circuit by leveraging atom loss detection and machine learning decoding [8,9]. We then investigate logical entanglement using transversal gates and lattice surgery [10–12], and extend it to universal logic through transversal teleportation with 3D [[15,1,3]] codes [13,14], enabling arbitrary-angle synthesis with polylogarithmic overhead [5,15]. Finally, we develop mid-circuit qubit re-use [16], increasing experimental cycle rates by two orders of magnitude and enabling deep-circuit protocols with dozens of logical qubits and hundreds of logical teleportations [17–20] with [[7,1,3]] and high-rate [[16,6,4]] codes while maintaining constant internal entropy. Our experiments reveal key principles for efficient architecture design, involving the interplay between quantum logic & entropy removal, judiciously using physical entanglement in logic gates & magic state generation, and leveraging teleportations for universality & physical qubit reset. These results establish foundations for scalable, universal error-corrected processing and its practical implementation with neutral atom systems.
Quantum Error Correction (QEC) is essential for overcoming the fragility of qubits, enabling reliable and scalable quantum computing by mitigating the effects of noise, decoherence, and operational errors. This chapter explores QEC's foundational principles, including error types, noise impact, and syndrome measurement techniques for identifying and correcting errors. It covers key codes such as Shor and Steane, which provide basic error protection, and advanced codes like concatenated codes, surface codes, and quantum Low-Density Parity-Check (LDPC) codes. Concatenated codes employ hierarchical encoding to exponentially reduce logical error rates, while surface codes, including Toric codes, utilize topological properties of qubits arranged on a 2D lattice to detect and correct errors with high fault tolerance and scalability. Quantum LDPC codes leverage sparse parity-check matrices to offer efficient and scalable error correction, closely approaching theoretical performance limits. The chapter concludes by exploring current research efforts to optimize QEC codes.