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Discrete-query quantum algorithm for NAND trees. (arXiv:quant-ph/0702160v2 UPDATED)

Andrew M. Childs, Richard Cleve, Stephen P. Jordan, David L. Yonge-Mallo

Published Sep 9, 2019• 0 citations
OpenAlex

Abstract

Recently, Farhi, Goldstone, and Gutmann gave a quantum algorithm for evaluating NAND trees that runs in time O(sqrt(N log N)) in the Hamiltonian query model. In this note, we point out that their algorithm can be converted into an algorithm using O(N^{1/2 + epsilon}) queries in the conventional quantum query model, for any fixed epsilon > 0.

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Discrete-query quantum algorithm for NAND trees

Andrew M. Childs, Richard Cleve, Stephen P. Jordan +1 more

This is a comment on the article “A Quantum Algorithm for the Hamiltonian NAND Tree” by Edward Farhi, Jeffrey Goldstone, and Sam Gutmann, Theory of Computing 4 (2008) 169-190. That paper gave a quantum algorithm for evaluating nand trees with running time $O(\sqrt{N})$ in the Hamiltonian query model. In this note, we point out that their algorithm can be converted into an algorithm using $N^{1/2 + o(1)}$ queries in the conventional (discrete) quantum query model.

arXiv:quant-ph/0702160•Theory of Computing•Jan 1, 2009•33 citations

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Andrew M. Childs, Richard Cleve, Stephen P. Jordan +1 more

Recently, Farhi, Goldstone, and Gutmann gave a quantum algorithm for evaluating nand trees that runs in time O ( √ N log N) in the Hamiltonian query model. In this note, we point out that their algorithm can be converted into an algorithm using O(N 1/2+ǫ) queries in the conventional quantum query model, for any fixed ǫ> 0. A nand tree of depth n is a balanced binary tree whose internal vertices represent nand gates. Placing bits x1,...,x2 n at the leaves, the root of the nand tree evaluates to the function fn(x1,...,x2 n), where fn: {0, 1} 2n → {0, 1} is defined recursively as follows. For n = 0, f0(x) = x, and for n> 0, fn(x1,..., x2 n) = ¬ ( fn−1(x1,..., x 2 n−1) ∧ fn−1(x 2 n−1 +1,...,x2 n)). (1) The goal of the nand tree problem is to evaluate fn(x1,..., x2 n), making as few queries to the bits x1,..., x2 n as possible. The optimal classical randomized algorithm for this problem makes Θ(N 0.753) queries, where N = 2 n [7, 8, 9]. Until now, no better quantum algorithm was known, whereas the best known quantum lower bound is only Ω ( √ N) [1]. Here we show that for any fixed ǫ> 0, the quantum query complexity of evaluating nand trees is O(N 1/2+ǫ).

•Feb 16, 2007•54 citations

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Every NAND formula of size N can be evaluated in time N^{1/2+o(1)} on a quantum computer

Andrew M. Childs, Ben W. Reichardt, Robert Špalek +1 more

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Continuous-time Quantum Algorithms: Searching and Adiabatic Computation

Lawrence M. Ioannou

One of the most important quantum algorithms is Grover's search algorithm [G96]. Quantum searching can be used to speed up the search for solutions to NP-complete problems e. g. 3SAT. Even so, the best known quantum algorithms for 3SAT are considered inefficient. \nSoon after Grover's discovery, Farhi and Gutmann [FG96] devised a "continuous-time analogue" of quantum searching. More recently Farhi et. al. [FGGS00] proposed a continuous-time 3SAT algorithm which invokes the adiabatic approximation [M76]. Their algorithm is difficult to analyze, hence we do not know whether it can solve typical 3SAT instances faster than Grover's search algorithm can. \nI begin with a review of the discrete- and continuous-time models of quantum computation. I then make precise the notion of "efficient quantum algorithms", motivating sufficient conditions for discrete- and continuous-time algorithms to be considered efficient via discussion of standard techniques for discrete-time simulation of continuous-time algorithms. After reviewing three quantum search algorithms [F00,FG96,G96], I develop the adiabatic 3SAT algorithm as a natural extension of Farhi and Gutmann's search algorithm. Along the way, I present the adiabatic search algorithm [vDMV01] and remark on its discrete-time simulation. Finally I devise a generalization of the adiabatic algorithm and prove some lower bounds for various cases of this general framework. \nUPDATE (February 2003): Please see article http://arxiv. org/abs/quant-ph/0302138 for a resolution to the problem of simulating the continuous-time adiabatic search algorithm with a quantum circuit using only O(sqrt(N)) resources.

•Jan 1, 2002•4 citations
Recently, Farhi, Goldstone, and Gutmann gave a quantum algorithm for evaluating NAND trees that runs in time O(sqrt(N log N)) in the Hamiltonian query model. In this note, we point out that their algorithm can be converted into an algorithm using O(N^{1/2 + epsilon}) queries in the conventional quantum query model, for any fixed epsilon > 0.
This is a comment on the article “A Quantum Algorithm for the Hamiltonian NAND Tree” by Edward Farhi, Jeffrey Goldstone, and Sam Gutmann, Theory of Computing 4 (2008) 169-190. That paper gave a quantum algorithm for evaluating nand trees with running time O(N)O(\sqrt{N})O(N​) in the Hamiltonian query model. In this note, we point out that their algorithm can be converted into an algorithm using N1/2+o(1)N^{1/2 + o(1)}N1/2+o(1) queries in the conventional (discrete) quantum query model.
Recently, Farhi, Goldstone, and Gutmann gave a quantum algorithm for evaluating nand trees that runs in time O ( √ N log N) in the Hamiltonian query model. In this note, we point out that their algorithm can be converted into an algorithm using O(N 1/2+ǫ) queries in the conventional quantum query model, for any fixed ǫ> 0. A nand tree of depth n is a balanced binary tree whose internal vertices represent nand gates. Placing bits x1,...,x2 n at the leaves, the root of the nand tree evaluates to the function fn(x1,...,x2 n), where fn: {0, 1} 2n → {0, 1} is defined recursively as follows. For n = 0, f0(x) = x, and for n> 0, fn(x1,..., x2 n) = ¬ ( fn−1(x1,..., x 2 n−1) ∧ fn−1(x 2 n−1 +1,...,x2 n)). (1) The goal of the nand tree problem is to evaluate fn(x1,..., x2 n), making as few queries to the bits x1,..., x2 n as possible. The optimal classical randomized algorithm for this problem makes Θ(N 0.753) queries, where N = 2 n [7, 8, 9]. Until now, no better quantum algorithm was known, whereas the best known quantum lower bound is only Ω ( √ N) [1]. Here we show that for any fixed ǫ> 0, the quantum query complexity of evaluating nand trees is O(N 1/2+ǫ).
One of the most important quantum algorithms is Grover's search algorithm [G96]. Quantum searching can be used to speed up the search for solutions to NP-complete problems e. g. 3SAT. Even so, the best known quantum algorithms for 3SAT are considered inefficient. \nSoon after Grover's discovery, Farhi and Gutmann [FG96] devised a "continuous-time analogue" of quantum searching. More recently Farhi et. al. [FGGS00] proposed a continuous-time 3SAT algorithm which invokes the adiabatic approximation [M76]. Their algorithm is difficult to analyze, hence we do not know whether it can solve typical 3SAT instances faster than Grover's search algorithm can. \nI begin with a review of the discrete- and continuous-time models of quantum computation. I then make precise the notion of "efficient quantum algorithms", motivating sufficient conditions for discrete- and continuous-time algorithms to be considered efficient via discussion of standard techniques for discrete-time simulation of continuous-time algorithms. After reviewing three quantum search algorithms [F00,FG96,G96], I develop the adiabatic 3SAT algorithm as a natural extension of Farhi and Gutmann's search algorithm. Along the way, I present the adiabatic search algorithm [vDMV01] and remark on its discrete-time simulation. Finally I devise a generalization of the adiabatic algorithm and prove some lower bounds for various cases of this general framework. \nUPDATE (February 2003): Please see article http://arxiv. org/abs/quant-ph/0302138 for a resolution to the problem of simulating the continuous-time adiabatic search algorithm with a quantum circuit using only O(sqrt(N)) resources.