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Measurement-Free Ancilla Recycling via Blind Reset: A Cross-Platform Study on Superconducting and Trapped-Ion Processors

arXiv cs.AI / 3/11/2026

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Key Points

  • The paper evaluates blind reset, a unitary-only ancilla recycling method, on superconducting and trapped-ion quantum processors to reduce logical-cycle latency during syndrome extraction.
  • Platform-calibrated simulations and hardware experiments show blind reset can reduce cycle latency by up to 38x while maintaining ancilla cleanliness above 0.84 for various circuit lengths.
  • Significant differences in performance crossover lengths across quantum computing platforms (IQM, Rigetti, IonQ) are identified, affecting where blind reset is most beneficial.
  • Additional analyses include coherence-time sensitivity mapping and error-bound validation to ensure implementation reliability and inform deployment decisions.
  • The results enable a backend-specific policy decision matrix for optimizing ancilla reuse strategies, balancing latency reduction and error rates in quantum error correction cycles.

Abstract

Ancilla reuse in repeated syndrome extraction couples reset quality to logical-cycle latency. We evaluate blind reset -- unitary-only recycling via scaled sequence replay -- on IQM Garnet, Rigetti Ankaa-3, and IonQ under matched seeds, sequence lengths, and shot budgets. Using ancilla cleanliness F_clean=P(|0>), per-cycle latency, and a distance-3 repetition-code logical-error proxy, platform-calibrated simulation identifies candidate regions where blind reset cuts cycle latency by up to 38x under NVQLink-class feedback overhead while maintaining F_clean >= 0.86 for L <= 6. Hardware experiments on IQM Garnet confirm blind-reset cleanliness >= 0.84 at L=8 (1024 shots, seed 42); platform-calibrated simulation for Rigetti Ankaa-3 predicts comparable performance. Architecture-dependent crossover lengths are L* ~ 12 (IQM), ~ 11 (Rigetti), ~ 1 (IonQ), and ~ 78 with GPU-linked external feedback. Two added analyses tighten deployment boundaries: a T1/T2 sensitivity map identifies coherence-ratio regimes, and error-bound validation confirms measured cleanliness remains consistent with the predicted diagnostic envelope. A deployment decision matrix translates these results into backend-specific policy selection.

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