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A nonnegative-only adversary estimate need not capture the general bound
Statement refuted
The assertion that nonnegative adversary weights always attain the general signed adversary bound is false. The following family supplies a strict separation.
For the partial iterates and of the sorted four-bit function, Thus signed adversary weights give a strict separation already at k=1 and an exponentially increasing ratio under iteration.
Facts & Assumptions
Given: The sorted four-bit function, its base certificates, and its partial Boolean iterates.
The four-bit Ambainis function separates nonnegative and signed adversary bounds: the sorted four-bit f has Adv(f)=5/2 and Adv±(f)>2.511486, with explicit dual distributions and a signed matrix.
Nonnegative and signed adversary bounds compose from below: both signed and nonnegative adversary values multiply as lower bounds when the composed block domain is nonempty.
Input-distribution certificates upper-bound the nonnegative adversary value: for a nonconstant Boolean function, coordinate distributions with cross-output mismatch overlap η>0 give Adv≤1/η.
Counterexample
Let f be the sorted four-bit function and define and on their natural block domains. Since is total on and nonconstant, induction shows every is total on and nonconstant: each outer bit string is realized by independently choosing a preimage of each bit under , and outer strings with either -value lift in the same way. Thus every composition domain is nonempty and both adversary bounds in [F2] apply. The signed matrix in [F1] and repeated composition in [F2] give .
The base coordinate distributions in [F1] have mismatch overlap at least . If is a distribution over the input coordinates with cross-output overlap at least , define for an input to . For opposite outputs of , the outer strings have opposite -values. For each with , the inner pair has opposite -values and contributes overlap at least . Summing those gives total overlap at least ; normalization follows by multiplying two probability distributions.
By induction and [F3], . Since , the signed lower bound grows strictly faster than this nonnegative upper bound for every . Thus negative weights produce a strict adversary separation, and repeated composition amplifies it.
Depends on
- General adversary matrix and spectral bound
- Nonnegative-weight adversary bound
- Signed weights extend the nonnegative adversary certificate
- The four-bit Ambainis function separates nonnegative and signed adversary bounds
- Input-distribution certificates upper-bound the nonnegative adversary value
- Nonnegative and signed adversary bounds compose from below
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
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Sources
- Ronald de Wolf, Quantum Computing: Lecture Notes, arXiv:1907.09415v6 (standard reference, not scraped)
- Høyer, Lee, and Špalek, Negative Weights Make Adversaries Stronger, Theory of Computing 3 (2007), §§6–7 (standard reference, not scraped)