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Paper 1
Quantum Operating System Support for Quantum Trusted Execution Environments
Theodoros Trochatos, Jakub Szefer
- Year
- 2024
- Journal
- arXiv preprint
- DOI
- arXiv:2410.08486
- arXiv
- 2410.08486
With the growing reliance on cloud-based quantum computing, ensuring the confidentiality and integrity of quantum computations is paramount. Quantum Trusted Execution Environments (QTEEs) have been proposed to protect users' quantum circuits when they are submitted to remote cloud-based quantum computers. However, deployment of QTEEs necessitates a Quantum Operating Systems (QOS) that can support QTEEs hardware and operation. This work introduces the first architecture for a QOS to support and enable essential steps required for secure quantum task execution on cloud platforms.
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Measurement-Free Ancilla Recycling via Blind Reset: A Cross-Platform Study on Superconducting and Trapped-Ion Processors
Sangkeum Lee
- Year
- 2026
- Journal
- arXiv preprint
- DOI
- arXiv:2603.08733
- arXiv
- 2603.08733
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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