Oracle Separation of Quantum Polynomial time and the Polynomial Hierarchy
In their seminal paper, Bennett, Bernstein, Brassard and Vazirani [SICOMP, 1997] showed that relative to an oracle, quantum algorithms are unable to solve NP-complete problems in sub-exponential time (i.e., that Grover's search is optimal in this setting).
In this work, we show a strong converse to their result. Namely, we show that, relative to an oracle, there exist computational tasks that can be solved efficiently by a quantum algorithm, but require exponential time for any algorithm in the polynomial hierarchy. (The polynomial hierarchy is a hierarchy of complexity classes that captures P, NP, coNP, and their generalizations.)
The tasks that exhibit this "quantum advantage" arise from a pseudo-randomness approach initiated by Aaronson [STOC, 2010]. Our core technical result is constructing a distribution over Boolean strings that "look random" to constant-depth circuits of quasi-polynomial size, but can be distinguished from the uniform distribution by very efficient quantum algorithms.
(Joint work with Ran Raz.)
In this work, we show a strong converse to their result. Namely, we show that, relative to an oracle, there exist computational tasks that can be solved efficiently by a quantum algorithm, but require exponential time for any algorithm in the polynomial hierarchy. (The polynomial hierarchy is a hierarchy of complexity classes that captures P, NP, coNP, and their generalizations.)
The tasks that exhibit this "quantum advantage" arise from a pseudo-randomness approach initiated by Aaronson [STOC, 2010]. Our core technical result is constructing a distribution over Boolean strings that "look random" to constant-depth circuits of quasi-polynomial size, but can be distinguished from the uniform distribution by very efficient quantum algorithms.
(Joint work with Ran Raz.)
Date
Affiliation
University of California, Berkeley