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The Channel-Distance Algorithm Meets Its Lower Bound
The estimator uses order square-root-d over epsilon queries, matching the known lower bound for unknown unitary channels.
Summary
The estimator uses order square-root-d over epsilon queries, matching the known lower bound for unknown unitary channels.
Diamond distance determines the best single-use probability of distinguishing two quantum channels. A new algorithm estimates that distance between unknown d-dimensional unitary channels to additive error epsilon using order square-root-d over epsilon calls to the channels and their inverses. The authors show this query complexity matches an existing lower bound, making it optimal in that model. The result is an algorithmic theorem, not a report of device-level runtime or noise performance.
Why it matters
The estimator uses order square-root-d over epsilon queries, matching the known lower bound for unknown unitary channels.
Limits and context
- The result is an algorithmic theorem, not a report of device-level runtime or noise performance.
Key claims
The estimator uses order square-root-d over epsilon queries, matching the known lower bound for unknown unitary channels.
Qualification: The result is an algorithmic theorem, not a report of device-level runtime or noise performance.
Evidence: source-2026-10-31-013
Sources
- arXiv preprint 2610.40623, original v1arXiv · primary research
Corrections
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