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Journal of Sichuan Normal University

A Simple Quantum Computation to Calculate Intersection of Data Sets

Ben-Qiong Hu

Published Jan 1, 2013• 0 citations
OpenAlex

Abstract

The computation of intersection of two data sets is an essential calculation in mathematics field.The computational efficiency is very low when the data sets consist of high dimensional vectors.In this paper,a unitary operation named general Grover iteration(GGI) is proposed and used to design quantum algorithm to calculate the intersection,and the algorithm is simple and fast than electronic computation significantly.

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The computation of intersection of two data sets is an essential calculation in mathematics field.The computational efficiency is very low when the data sets consist of high dimensional vectors.In this paper,a unitary operation named general Grover iteration(GGI) is proposed and used to design quantum algorithm to calculate the intersection,and the algorithm is simple and fast than electronic computation significantly.
Recent advances in the quantum computing field of computer science that apply the power of quantum mechanics to perform calculations have led many companies, such as IBM, Google, Microsoft, D-Wave Systems, and Xanadu Quantum Technologies, to invest in the field of quantum computing. The main goal of this paper is to apply the Elgendy et al. algorithm, which proposes quantum algorithms for set operations such as true intersection, false intersection, difference, and union, on the current viability of available IBM quantum computers. Moreover, this paper includes a new 5-qubit implementation of Elgendy et al., tests results showing the current capabilities of quantum computers, extends to greater scales, and evaluates algorithm performance on available noisy devices. Elgendy et al. algorithms use a two-stage quantum amplitude amplification technique: The first stage involves preparing the truth set of the first Boolean function using incomplete superpositions, which is done using the Younes et al. technique which performs quantum searching via entanglement and partial diffusion. In the second stage, set operations are handled using an oracle that represents the second Boolean function and a modified version of Arima's algorithm. Among the potential uses are machine learning, cryptography, pattern recognition, and database querying.
Grover's algorithm achieves a quadratic speedup for unstructured search when a global oracle for the target set is available. However, its success probability is generally below 100%100\%100%. To overcome this issue, Long presented a modified version of Grover's search algorithm by introducing a phase-matching condition, which can search for the target set with zero theoretical failure rate. Nevertheless, in many applications, the target set is determined by the intersection of multiple constrained sets. Most existing Grover-based methods are limited to the intersection of only two sets and are not always successful in searching for the target set. In this work, based on Long's method, we propose an algorithm for exactly searching the intersection of multiple sets. We derive and prove a phase-matching condition for searching the intersection exactly. Furthermore, we present a quantum circuit that implements exact intersection search. Finally, we support our theoretical results with numerical simulations.
We describe a quantum algorithm for computing the intersection of two sets and its application to associative memory. The algorithm is based on a modification of Grover's quantum search algorithm (Grover, 1996). We present algorithms for pattern retrieval, pattern completion, and pattern correction. We show that the quantum associative memory can store an exponential number of memories and retrieve them in sub-exponential time. We prove that this model has advantages over known classical associative memories as well as previously proposed quantum models.
Recent research on the topic of quantum computation provides us with some quantum algorithms with higher efficiency and speedup compared to their classical counterparts. In this paper, it is our intent to provide the results of our investigation of several applications of such quantum algorithms - especially the Grover's Search algorithm - in the analysis of Hyperspectral Data. We found many parallels with Grover's method in existing data processing work that make use of classical spectral matching algorithms. Our efforts also included the study of several methods dealing with hyperspectral image analysis work where classical computation methods involving large data sets could be replaced with quantum computation methods. The crux of the problem in computation involving a hyperspectral image data cube is to convert the large amount of data in high dimensional space to real information. Currently, using the classical model, different time consuming methods and steps are necessary to analyze these data including: Animation, Minimum Noise Fraction Transform, Pixel Purity Index algorithm, N-dimensional scatter plot, Identification of Endmember spectra - are such steps. If a quantum model of computation involving hyperspectral image data can be developed and formalized - it is highly likely that information retrieval from hyperspectral image data cubes would be a much easier process and the final information content would be much more meaningful and timely. In this case, dimensionality would not be a curse, but a blessing.