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arXiv:0806.0154

Superlinear Amplitude Amplification

Lov K. Grover

Published Jun 2, 2008• 3 citations
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Abstract

Quantum search/amplitude amplification algorithms are designed to be able to amplify the amplitude in the target state linearly with the number of operations. Since the probability is the square of the amplitude, this results in the success probability rising quadratically with the number of operations. This paper presents a new kind of quantum search algorithm in which the amplitude of the target state, itself increases quadratically with the number of operations. However, the domain of applications of this is much more limited than standard amplitude amplification.

DOI
10.48550/arxiv.0806.0154

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Quantum search/amplitude amplification algorithms are designed to be able to amplify the amplitude in the target state linearly with the number of operations. Since the probability is the square of the amplitude, this results in the success probability rising quadratically with the number of operations. This paper presents a new kind of quantum search algorithm in which the amplitude of the target state, itself increases quadratically with the number of operations. However, the domain of applications of this is much more limited than standard amplitude amplification.
Grover's quantum search and its generalization, quantum amplitude amplification, provide a quadratic advantage over classical algorithms for a diverse set of tasks but are tricky to use without knowing beforehand what fraction λ of the initial state is comprised of the target states. In contrast, fixed-point search algorithms need only a reliable lower bound on this fraction but, as a consequence, lose the very quadratic advantage that makes Grover's algorithm so appealing. Here we provide the first version of amplitude amplification that achieves fixed-point behavior without sacrificing the quantum speedup. Our result incorporates an adjustable bound on the failure probability and, for a given number of oracle queries, guarantees that this bound is satisfied over the broadest possible range of λ.
Grover's quantum search and its generalization, quantum amplitude amplification, provide quadratic advantage over classical algorithms for a diverse set of tasks, but are tricky to use without knowing beforehand what fraction λ\lambdaλ of the initial state is comprised of the target states. In contrast, fixed-point search algorithms need only a reliable lower bound on this fraction, but, as a consequence, lose the very quadratic advantage that makes Grover's algorithm so appealing. Here we provide the first version of amplitude amplification that achieves fixed-point behavior without sacrificing the quantum speedup. Our result incorporates an adjustable bound on the failure probability, and, for a given number of oracle queries, guarantees that this bound is satisfied over the broadest possible range of λ\lambdaλ.
The quantum search takes advantage of quantum parallelism to construct superposition of all possible states and then increase the probability amplitude of the solution state. This is the distinguishing characteristics of quantum search strategy. The objective of a classical search algorithm is to diminish the amplitude of target state where as a quantum search algorithm tries to amplify the amplitude of the target state. The term amplification indicates to increase the probability of the target state. This paper attempts to explain two central ideas of Grover’s quantum search algorithm that amplify the probability of the target state, the inversion about the mean and phase inversion, in a simplified way with a concrete example.
Search-base algorithms have widespread applications in different scenarios. Grover's quantum search algorithms and its generalization, amplitude amplification, provide a quadratic speedup over classical search algorithms for unstructured search. We consider the problem of searching with prior knowledge. More preciously, search for the solution among N items with a prior probability distribution. This letter proposes a new generalization of Grover's search algorithm which performs better than the standard Grover algorithm in average under this setting. We prove that our new algorithm achieves the optimal expected success probability of finding the solution if the number of queries is fixed.
In Chs. 2 and 3 we described several quantum algorithms that are exponentially faster than classical algorithms for the same problem, under some measure of complexity. Unfortunately, the algorithms of Ch. 2 apply to rather contrived problems that do not have known direct applications. We now describe a quantum algorithm that gives only a polynomial—more specifically, quadratic—speedup with respect to classical algorithms, but it applies to a large class of problems and finds many useful applications, both directly and as an algorithmic design tool. The algorithm is known as Grover’s search [89], and its generalization is known as amplitude amplification [43]. Amplitude amplification is widely used as part of many quantum algorithms. It also serves as the basis for amplitude estimation, a way of estimating probabilities quadratically faster than with classical Monte Carlo, and for quantum minimum finding. All these topics are discussed in this chapter.
Quantum amplitude amplification is a method of increasing a success probability of an algorithm from a small epsilon>0 to Theta(1) with less repetitions than classically. In this paper, we generalize quantum amplitude amplification to the case when parts of the algorithm that is being amplified stop at different times. We then apply the new variable time amplitude amplification to give two new quantum algorithms for linear algebra problems. Our first algorithm is an improvement of Harrow et al. algorithm for solving systems of linear equations. We improve the running time of the algorithm from O(k^2 log N) to O(k log^3 k log N) where k is the condition number of the system of equations. Our second algorithm tests whether a matrix A is singular or far from singular, faster then the previously known algorithms.