We assess the resources needed for quantum computation examples that serve as building blocks of proposed applications, quantifying the architectural bottlenecks and trade-offs that remain to be addre

The gap

Consensus across the literature

Supporting evidence — 4 representative gaps

• Quantum error correction below the surface code threshold (2024) · doi

  In this work, we have demonstrated surface code memory below the threshold in our new Willow architecture. Each time the code distance increases by two, the logical error per cycle is reduced by more than half, culminating in a distance-7 logical lifetime of more than double its best constituent physical qubit lifetime. This signature of exponential logical error suppression with code distance forms the foundation of running large-scale quantum algorithms with error correction. Our error-corrected processors also demonstrate other key advances towards fault-tolerant quantum computing. We achieve repeatable performance for more than several hours and run experiments up to 106 cycles without deteriorating performance, both of which are necessary for future large-scale fault-tolerant algorithms. Furthermore, we have ArticleabcdProcessingGraph bufferCompletePastBlossomFuseFuseTaskgraphNowNot started0012345FutureShot latencySubshot latency0, 12, 31, 23, 44, 5RTNumber of cyclesNNEns.12345..., 05, ...10–210–3Logical errorper cycle, d30010030103104105106Latency (μs)d = 3d = 5engineered a real-time decoding system with only a modest reduction in accuracy compared with our offline decoders. Even so, many challenges remain ahead of us. Although we might, in principle, achieve low logical error rates by scaling up our current processors, it would be resource intensive in practice. Extrapolating the projections shown in Fig. 1d, achieving a 10−6 error rate would require a distance-27 logical qubit using 1,457 physical qubits. Scaling up will bring additional challenges in real-time decoding as the syndrome measurements per cycle increase quadratically with the code distance. Our repetition code experiments also identify a noise floor at an error rate of 10−10 caused by correlated bursts of errors. Identifying and miti- gating this error mechanism will be integral to running larger quantum algorithms. However, quantum error correction also provides us exponential leverage in reducing logical errors with processor improvements. For example, reducing physical error rates by a factor of two would improve the distance-27 logical performance by four orders of magnitude, well into algorithmically relevant error rates11,12. We further expect these overheads to reduce with advances in error correction protocols47–53 and decoding54–56. The purpose of quantum error correction is to enable large-scale quantum algorithms. Although this work focuses on building a robust memory, additional challenges will arise in logical computation57,58. On the classical side, we must ensure that software elements including our calibration protocols, real-time decoders and logical compilers can scale to the sizes and complexities needed to run multiple sur- face code operations59. With below-threshold surface codes, we have demonstrated processor performance that can scale in principle, but which we must now scale in practice.

  Keywords: error logical code distance scale quantum time algorithms correction performance cycle physical large real decoding
• A practical architecture for reliable quantum computers (2002) · doi

  Elementary architectural concepts are still lacking: How do we provide quantum storage, data paths, classical control circuits, parallelism, and system integration? And, crucially, how can we design architectures to reduce error-correction overhead? The authors describe a proposed architecture that uses code teleportation, quantum memory refresh units, dynamic compilation of quantum programs, and scalable error correction to achieve system-level efficiencies.

  Keywords: quantum system error correction elementary architectural concepts still lacking provide storage paths classical control circuits
• Space and time cost of continuous rotations in surface codes (2026) · doi

  We also highlight that future research should focus on the development of quantum compilers capable of incor- porating different resource optimization priorities – such as spacetime tradeoffs – and of efficiently mapping ab- stract quantum circuits onto surface code architectures.

  Keywords: quantum highlight future focus development compilers capable incor porating different resource optimization priorities spacetime tradeoffs
• Superconducting qubits in the millions: The potential and limitations of modularity (2026) · doi

  We assess the resources needed for quantum computation examples that serve as building blocks of proposed applications, quantifying the architectural bottlenecks and trade-offs that remain to be addressed to deliver utility.

  Keywords: assess resources needed quantum computation examples serve building blocks proposed applications quantifying architectural bottlenecks trade