Quantum interactive proofs with weak error bounds
Bibliographic record
Abstract
This paper proves that the computational power of quantum interactive proof systems, with a double-exponentially small gap in acceptance probability between the completeness and soundness cases, is precisely characterized by EXP, the class of problems solvable in exponential time by deterministic Turing machines. This fact, and our proof of it, has implications concerning quantum and classical interactive proof systems in the setting of unbounded error that include the following: * Quantum interactive proof systems are strictly more powerful than their classical counterparts in the unbounded-error setting unless PSPACE=EXP, as even unbounded error classical interactive proof systems can be simulated in PSPACE. * The recent proof of Jain, Ji, Upadhyay, and Watrous (STOC 2010) establishing QIP=PSPACE relies heavily on the fact that the quantum interactive proof systems defining the class QIP have bounded error. Our result implies that some nontrivial assumption on the error bounds for quantum interactive proofs is unavoidable to establish this result (unless PSPACE=EXP). * To prove our result, we give a quantum interactive proof system for EXP with perfect completeness and soundness error 1-2^{-2^poly}, for which the soundness error bound is provably tight. This establishes another respect in which quantum and classical interactive proof systems differ, because such a bound cannot hold for any classical interactive proof system: distinct acceptance probabilities for classical interactive proof systems must be separated by a gap that is at least (single-)exponentially small. We also study the computational power of a few other related unbounded-error complexity classes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.018 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.002 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".