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arXiv:quant-ph/9711035

Quantum Mechanical Square Root Speedup in a Structured Search Problem

Edward Farhi, Sam Gutmann

Published Nov 18, 1997• 12 citations
arXivHTMLPDF

Abstract

An unstructured search for one item out of N can be performed quantum mechanically in time of order square root of N whereas classically this requires of order N steps. This raises the question of whether square root speedup persists in problems with more structure. In this note we focus on one example of a structured problem and find a quantum algorithm which takes time of order the square root of the classical time.

DOI
10.48550/arxiv.quant-ph/9711035

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An unstructured search for one item out of N can be performed quantum mechanically in time of order square root of N whereas classically this requires of order N steps. This raises the question of whether square root speedup persists in problems with more structure. In this note we focus on one example of a structured problem and find a quantum algorithm which takes time of order the square root of the classical time.
A quantum algorithm is known that solves an unstructured search problem in a number of iterations of order d,\sqrt{d},d​, where d is the dimension of the search space, whereas any classical algorithm necessarily scales as O(d).O(d).O(d). It is shown here that an improved quantum search algorithm can be devised that exploits the structure of a tree search problem by nesting one quantum search within another. The average number of iterations required to find the solution of a typical hard instance of a constraint satisfaction problem is found to scale as $\sqrt{{d}^{\ensuremath{\alpha}}},$ with the constant $\ensuremath{\alpha}<1$ depending on the nesting depth and the type of problem considered. This corresponds to a square-root speedup over a classical nested search algorithm, of which our algorithm is the quantum counterpart. When applying a single nesting level to a problem with constraints of size 2 such as the graph coloring problem, $\ensuremath{\alpha}$ is estimated to be around 0.62.
Given an item and a list of values of size NNN. It is required to decide if such item exists in the list. Classical computer can search for the item in O(N). The best known quantum algorithm can do the job in O(N)O(\sqrt{N})O(N​). In this paper, a quantum algorithm will be proposed that can search an unstructured list in O(1) to get the YES/NO answer with certainty.
Quantum computing is a young but intriguing field of science. It combines quantum mechanics with information theory and computer science to potentially solve certain formerly computationally expensive tasks more efficiently. Classical computers are based on bits that can take on the value zero or one. The values are distinguished by voltage differences in transistors. Quantum computers are instead based on quantum bits, or qubits, that are represented physically by something that exhibits quantum properties, like for example electrons. Qubits also take on the value zero or one, which could correspond to spin up and spin down of an electron. However, qubits can also be in a superposition state between the quantum states corresponding to the value zero and one. This property is what causes quantum computers to be able to outperform classical computers at certain tasks. One of these tasks is searching through an unstructured database. Whereas a classical computer in the worst case has to search through the whole database in order to find the sought element, i.e. the computation time is proportional to the size of the problem, it can be shown that a quantum computer can find the solution in a time proportional to the square root of the size of the problem. This report aims to illustrate the advantages of quantum computing by explicitly solving the classical Windows game Minesweeper, which can be reduced to a problem resembling the unstructured database search problem. It is shown that solving Minesweeper with a quantum algorithm gives a quadratic speedup compared to solving it with a classical algorithm. The report also covers introductory material to quantum mechanics, quantum gates, the particular quantum algorithm Grover's algorithm and complexity classes, which is necessary to grasp in order to understand how Minesweeper can be solved on a quantum computer.
The study of quantum computation has been motivated by the hope of finding efficient quantum algorithms for solving classically hard problems. In this context, quantum algorithms by local adiabatic evolution have been shown to solve an unstructured search problem with a quadratic speedup over a classical search, just as Grover's algorithm. In this paper, we study how the structure of the search problem may be exploited to further improve the efficiency of these quantum adiabatic algorithms. We show that by nesting a partial search over a reduced set of variables into a global search, it is possible to devise quantum adiabatic algorithms with a complexity that, although still exponential, grows with a reduced order in the problem size.