ABSTRACT The quest for universal superconducting quantum computing is hindered by noise and errors. It has been proven that Quantum Error Correction (QEC) codes will lay at the foundation of fault‐tolerant quantum computing. However, cosmic‐ray‐induced correlated errors, the most detrimental events that can impact superconducting quantum computers, are yet to be efficiently tackled. In order to reach fault tolerance, we must also develop radiation aware methods to complement QEC. In this paper, the first algorithm to effectively exploit syndrome information for the efficient detection of radiation events in superconducting quantum devices at runtime is proposed. A thorough analysis of simulated Rotated Surface codes injecting over 11 million physics‐modeled radiation‐induced faults is performed. The properties of the X and Z check bases, the impact of code distance, and the decoder's time to solution constraints are considered. The technique detects of injected faults, regardless of the impact's position. Moreover, both the radiation impact center and the area affected are accurately identified, with an overhead lower than the decoding time. Additionally, the fault identification information is used to propose a radiation fault correction technique that improves of up to the output correctness compared to existing decoders.
ABSTRACT The quest for universal superconducting quantum computing is hindered by noise and errors. It has been proven that Quantum Error Correction (QEC) codes will lay at the foundation of fault‐tolerant quantum computing. However, cosmic‐ray‐induced correlated errors, the most detrimental events that can impact superconducting quantum computers, are yet to be efficiently tackled. In order to reach fault tolerance, we must also develop radiation aware methods to complement QEC. In this paper, the first algorithm to effectively exploit syndrome information for the efficient detection of radiation events in superconducting quantum devices at runtime is proposed. A thorough analysis of simulated Rotated Surface codes injecting over 11 million physics‐modeled radiation‐induced faults is performed. The properties of the X and Z check bases, the impact of code distance, and the decoder's time to solution constraints are considered. The technique detects of injected faults, regardless of the impact's position. Moreover, both the radiation impact center and the area affected are accurately identified, with an overhead lower than the decoding time. Additionally, the fault identification information is used to propose a radiation fault correction technique that improves of up to the output correctness compared to existing decoders.
Fault-tolerant quantum computation demands extremely low logical error rates, yet superconducting qubit arrays are subject to radiation-induced correlated noise arising from cosmic-ray muon-generated quasiparticles. The quasiparticle density is unknown and time-varying, resulting in a mismatch between the true noise statistics and the priors assumed by standard decoders, and consequently, degraded logical performance. We formalize joint noise sensing and decoding using syndrome measurements by modeling the QP density as a latent variable, which governs correlation in physical errors and syndrome measurements. Starting from a variational expectation--maximization approach, we derive an iterative algorithm that alternates between QP density estimation and syndrome-based decoding under the updated noise model. Simulations of surface-code and bivariate bicycle quantum memory under radiation-induced correlated noise demonstrate a measurable reduction in logical error probability relative to baseline decoding with a uniform prior. Beyond improved decoding performance, the inferred QP density provides diagnostic information relevant to device characterization, shielding, and chip design. These results indicate that integrating physical noise estimation into decoding can mitigate correlated noise effects and relax effective error-rate requirements for fault-tolerant quantum computation.
Reliability is fundamental for developing large-scale quantum computers. Since the benefit of technological advancements to the qubit’s stability is saturating, algorithmic solutions, such as quantum error correction (QEC) codes, are needed to bridge the gap to reliable computation. Unfortunately, the deployment of the first quantum computers has identified faults induced by natural radiation as an additional threat to qubits reliability. The high sensitivity of qubits to radiation hinders the large-scale adoption of quantum computers, since the persistence and area-of-effect of the fault can potentially undermine the efficacy of the most advanced QEC. In this paper, we investigate the resilience of various implementations of state-of-the-art QEC codes to radiation-induced faults. We report data from over 400 million fault injections and correlate hardware faults with the logical error observed after decoding the code output, extrapolating physical-to-logical error rates. We compare the code’s radiation-induced logical error rate over the code distance, the number and role in the QEC of physical qubits, the underlying quantum computer topology, and particle energy spread in the chip. We show that, by simply selecting and tuning properly the surface code, thus without introducing any overhead, the probability of correcting a radiation-induced fault is increased by up to 10%. Finally, we provide indications and guidelines for the design of future QEC codes to further increase their effectiveness against radiation-induced events.
Radiation impacts are a current challenge with computing on superconducting-based quantum devices because they can lead to widespread correlated errors across the device. Such errors can be problematic for quantum error correction (QEC) codes, which are generally designed to correct independent errors. To address this, we have developed a computational model to simulate the effects of radiation impacts on QEC performance. This is achieved by building from recently developed models of quasiparticle density, mapping radiation-induced qubit error rates onto a quantum error channel and simulation of a simple surface code. We also provide a performance metric to quantify the resilience of a QEC code to radiation impacts. Additionally, we sweep various parameters of chip design to test mitigation strategies for improved QEC performance. Our model approach is holistic, allowing for modular performance testing of error mitigation strategies and chip and code designs.
The struggle of the hour in quantum computing research is achieving effective suppression of the error mechanisms induced by the interaction of external radiation with superconducting quantum devices. Despite the rapid advancements in quantum error correction (QEC) of recent years, radiation-induced faults are yet to be fully addressed. These events are known to be the cause of simultaneous correlated defects in qubits that lie onto a single substrate, ultimately jeopardising QEC code effectiveness.
In this paper, we propose to selectively combine substrate-level phonon barriers and QEC interleaving via a planar-mesh tiling algorithm, TETRIS-Q, reaching efficient and effective suppression of radiation events. Our cross-layer solution comes at no extra cost in terms of QEC code execution or decoding time. We model and simulate radiation-induced transient faults over a plethora of barrier and QEC interleaving configurations. Through more than 51 million quantum circuit simulations, we show peak logical error reductions of more than 99.8%, together with an 80% reduction of the observable transient duration with permeable barriers. We find that sparser tiling can reach comparable performance to single qubit tiling, prompting cost reductions of upwards of 87% in barrier tracing. By leveraging independent QEC code interleaving, we measure up to one order of magnitude average logical error rate reductions without the use of permeable barriers, and up to three orders of magnitude with the joint usage of barriers.
Quantum error mitigation (QEM) is typically viewed as a suite of practical techniques for today's noisy intermediate-scale quantum devices, with limited relevance once fault-tolerant quantum computers become available. In this work, we challenge this conventional wisdom by showing that QEM can continue to provide substantial benefits in the era of quantum error correction (QEC), and in an even more efficient manner than it does on current devices. We introduce a framework for logical-level QEM that leverages soft information naturally produced by QEC decoders, requiring no additional data, hardware modifications, or runtime overhead beyond what QEC protocols already provide. Within this framework, we develop and analyze three logical-level QEM techniques: post-selection and runtime abort policies, probabilistic error cancellation, and zero-noise extrapolation. Our techniques reduce logical error rates by more than 100x while discarding fewer than 0.1% of shots; they also provide in situ characterization of logical channels for QEM protocols. As a proof of principle, we benchmark our approach using a surface-code architecture and two state-of-the-art decoders based on tensor-network contraction and minimum-weight perfect matching. We evaluate logical-level QEM on random Clifford circuits and molecular simulation algorithms and find that, compared to previous approaches relying on QEC only or QEC combined with QEM, we can achieve up to 87.4% spacetime overhead savings. Our results demonstrate that logical-level QEM with QEC decoder soft information can reliably improve logical performance, underscoring the efficiency and usefulness of QEM techniques for fault-tolerant quantum computers.
Quantum error correction (QEC), the lynchpin of fault-tolerant quantum computing (FTQC), is designed and validated against well-behaved Pauli stochastic error models. But in real-world deployment, QEC protocols encounter a vast array of other errors -- coherent and non-Pauli errors -- whose impacts on quantum circuits are vastly different than those of stochastic Pauli errors. The impacts of these errors on QEC and FTQC protocols have been largely unpredictable to date due to exponential classical simulation cost. Here, we show how to accurately and efficiently model the effects of coherent and non-Pauli errors on FTQC, and we study the effects of such errors on syndrome extraction for surface and bivariate bicycle codes, and on magic state cultivation. Our analysis suggests that coherent error can shift fault-tolerance thresholds, increase the space-time cost of magic state cultivation, and can increase logical error rates by an order of magnitude compared to equivalent stochastic errors. These analyses are enabled by a new technique for mapping any Markovian circuit-level error model with sufficiently small error rates onto a detector error model (DEM) for an FTQC circuit. The resulting DEM enables Monte Carlo estimation of logical error rates and noise-adapted decoding, and its parameters can be analytically related to the underlying physical noise parameters to enable approximate strong simulation.