LIPIcs.ITCS.2024.92.pdf
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A recent line of work [Bravyi et al., 2018; Watts et al., 2019; Grier and Schaeffer, 2020; Bravyi et al., 2020; Watts and Parham, 2023] has shown the unconditional advantage of constant-depth quantum computation, or QNC⁰, over NC⁰, AC⁰, and related models of classical computation. Problems exhibiting this advantage include search and sampling tasks related to the parity function, and it is natural to ask whether QNC⁰ can be used to help compute parity itself. Namely, we study AC⁰∘QNC⁰ - a hybrid circuit model where AC⁰ operates on measurement outcomes of a QNC⁰ circuit - and we ask whether Par ∈ AC⁰∘QNC⁰. We believe the answer is negative. In fact, we conjecture AC⁰∘QNC⁰ cannot even achieve Ω(1) correlation with parity. As evidence for this conjecture, we prove: - When the QNC⁰ circuit is ancilla-free, this model can achieve only negligible correlation with parity, even when AC⁰ is replaced with any function having LMN-like decay in its Fourier spectrum. - For the general (non-ancilla-free) case, we show via a connection to nonlocal games that the conjecture holds for any class of postprocessing functions that has approximate degree o(n) and is closed under restrictions. Moreover, this is true even when the QNC⁰ circuit is given arbitrary quantum advice. By known results [Bun et al., 2019], this confirms the conjecture for linear-size AC⁰ circuits. - Another approach to proving the conjecture is to show a switching lemma for AC⁰∘QNC⁰. Towards this goal, we study the effect of quantum preprocessing on the decision tree complexity of Boolean functions. We find that from the point of view of decision tree complexity, nonlocal channels are no better than randomness: a Boolean function f precomposed with an n-party nonlocal channel is together equal to a randomized decision tree with worst-case depth at most DT_depth[f]. Taken together, our results suggest that while QNC⁰ is surprisingly powerful for search and sampling tasks, that power is "locked away" in the global correlations of its output, inaccessible to simple classical computation for solving decision problems.
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