2013 IEEE 54th Annual Symposium on Foundations of Computer Science (2000)
Redondo Beach, California
Nov. 12, 2000 to Nov. 14, 2000
R. Cleve , Calgary Univ., Alta., Canada
J. Watrous , Calgary Univ., Alta., Canada
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//spl epsiv/)) on the circuit depth for computing an approximation of the QFT with respect to the modulus 2/sup n/ with error bounded by /spl epsiv/. 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//spl epsiv/)). As an application of this depth bound, we show that P. Shor's (1997) 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 postprocessing. Next, we prove an /spl 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 tight (for a reasonable range of values of /spl epsiv/). We also give an upper bound of O(n(log n)/sup 2/ log log n) on the circuit size of the exact QFT modulo 2/sup n/, for which the best previous bound was O(n/sup 2/). Finally, based on our circuits for the QFT with power-of-2 moduli, we show that the QFT with respect to an arbitrary modulus m can be approximated with accuracy /spl epsiv/ with circuits of depth O((log log m)(log log 1//spl epsiv/)) and size polynomial in log m+log(1//spl epsiv/).
circuit complexity; quantum computing; theorem proving; Fourier transforms; fast parallel circuits; quantum Fourier transform; circuit complexity; QFT; upper bound; circuit depth; constant error; depth bound; factoring algorithm; quantum circuits; polynomial size; classical polynomial-time processing; lower bound; depth complexity; arbitrary modulus
R. Cleve, J. Watrous, "Fast parallel circuits for the quantum Fourier transform", 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, vol. 00, no. , pp. 526, 2000, doi:10.1109/SFCS.2000.892140