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

Sangkeum Lee

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.

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

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.
Paper Structure (58 sections, 7 equations, 12 figures, 8 tables)

This paper contains 58 sections, 7 equations, 12 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Measurement-free QEC ecosystem
  3. Background and Related Work
  4. Ancilla reset in error correction workflows
  5. Blind reset primitive
  6. Cross-platform quantum benchmarking
  7. Latency context: NVQLink and hybrid control stacks
  8. Methods
  9. Blind reset protocol for ancilla recycling
  10. Error propagation.
  11. Assumptions.
  12. Cross-platform experimental design
  13. Simulation framework and latency model
  14. Noise model.
  15. QEC workload.
  16. ...and 43 more sections

Figures (12)

  • Figure 1: Per-cycle ancilla reset decision within repeated syndrome extraction. After each stabilizer check the ancilla enters one of three paths: no-reset (passive reuse), measurement-reset (readout plus conditional preparation), or blind reset (scale-and-double unitary replay). The decision criteria (Eq. \ref{['eq:decision']}) select blind reset when it is both faster and sufficiently clean. The NVQLink annotation illustrates how external feedback overhead expands the blind-favorable region.
  • Figure 2: Distance-3 repetition code simulation over 20 syndrome cycles. Ancilla cleanliness $P(\lvert 0\rangle)$ under three reset policies for IQM (left), Rigetti (center), and IonQ (right) noise models. Measurement-reset (green) is stable near $0.99$; no-reset (red) fluctuates erratically; blind reset (blue) occupies an intermediate region. Each curve averages over 50 seeds with 2048 shots per circuit; shaded bands show 95% CIs.
  • Figure 3: Ancilla cleanliness versus sequence length. Blind reset (solid) versus no-reset (dashed) for IQM (blue), Rigetti (orange), IonQ (green). Shaded bands: 95% CIs. Asterisks: $p<0.05$ at $L=4,6$.
  • Figure 4: Measured ancilla cleanliness versus diagnostic envelope (Eq. \ref{['eq:errorprop']}). Error bars: 95% CIs. Modest violations occur under realistic noise.
  • Figure 5: Reset latency versus sequence length for blind reset (solid) and measurement-reset (dashed) across platform profiles. Vertical dashed lines mark crossover $L^{\star}$. The NVQLink scenario (purple) extends the blind-favorable region by ${\approx}6.5\times$.
  • ...and 7 more figures