Context Switching for Secure Multi-programming of Near-Term Quantum Computers
Bibliographic record
Abstract
Multi-programming quantum computers improve device utilization and throughput. However, crosstalk from concurrent two-qubit CNOT gates poses security risks, compromising the fidelity and output of co-running victim programs. We design Zero Knowledge Tampering Attacks (ZKTAs), using which attackers can exploit crosstalk without knowledge of the hardware error profile. ZKTAs can alter victim program outputs in 40% of cases on commercial systems. We identify that ZKTAs succeed because the attacker's program consistently runs with the same victim program in a fixed context. To mitigate this, we propose QONTEXTS: a context-switching technique that defends against ZKTAs by running programs across multiple contexts, each handling only a subset of trials. QONTEXTS uses multi-programming with frequent context switching while identifying a unique set of programs for each context. This helps limit only a fraction of execution to ZKTAs. We enhance QONTEXTS with attack detection capabilities that compare the distributions from different contexts against each other to identify noisy contexts executed with ZKTAs. Our evaluations on real IBMQ systems show that QONTEXTS increases program resilience by three orders of magnitude and fidelity by 1.33$\times$ on average. Moreover, QONTEXTS improves throughput by 2$\times$, advancing security in multi-programmed environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".