Fast parallel circuits for the quantum Fourier transform

TL;DR

This paper presents new bounds on the circuit complexity of the quantum Fourier transform, achieving logarithmic depth approximations and implications for quantum factoring algorithms.

Contribution

It introduces nearly optimal logarithmic-depth quantum circuits for the QFT and demonstrates their application to efficient quantum factoring algorithms.

Findings

Quantum Fourier transform can be approximated with O(log n + log log (1/epsilon)) depth.

Exact QFT circuit size is bounded by O(n (log n)^2 log log n).

Shor's algorithm can be implemented with poly-logarithmic depth circuits.

Abstract

We give new bounds on the circuit complexity of the quantum Fourier transform (QFT). We give an upper bound of O(log n + log log (1/epsilon)) on the circuit depth for computing an approximation of the QFT with respect to the modulus 2^n with error bounded by epsilon. Thus, even for exponentially small error, our circuits have depth O(log n). The best previous depth bound was O(n), even for approximations with constant error. Moreover, our circuits have size O(n log (n/epsilon)). We also give an upper bound of O(n (log n)^2 log log n) on the circuit size of the exact QFT modulo 2^n, for which the best previous bound was O(n^2). As an application of the above depth bound, we show that Shor's factoring algorithm may be based on quantum circuits with depth only O(log n) and polynomial-size, in combination with classical polynomial-time pre- and post-processing. In the language of computational complexity, this implies that factoring is in the complexity class ZPP^BQNC, where BQNC is the class of problems computable with bounded-error probability by quantum circuits with poly-logarithmic depth and polynomial size. Finally, we prove an Omega(log n) lower bound on the depth complexity of approximations of the QFT with constant error. This implies that the above upper bound is asymptotically optimal (for a reasonable range of values of epsilon).