PLoS ONE

The Quantum Binding Problem in the Context of Associative Memory

Andreas Wichert

Published Sep 7, 2016• 0 citations

Abstract

We present a method to solve the binding problem by using a quantum algorithm for the retrieval of associations from associative memory during visual scene analysis. The problem is solved by mapping the information representing different objects into superposition by using entanglement and Grover's amplification algorithm.

DOI: 10.1371/journal.pone.0162312

Related papers

Quantum set intersection and its application to associative memory

Tamer Salman, Yoram Baram A

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.

Journal of Machine Learning ResearchJan 1, 20125 citations

Implementing associative memory with quantum neural networks

Jieyu Zhao

In this paper an attempt is made to implement associative memory with quantum neural networks. The quantum algorithms for the storage of pattern and the retrieval of information are presented. It is shown that the capacity and the processing speed of the quantum neural network increase exponentially compared to the conventional model. The neural computing method for the calculation of quantum entanglement is also discussed.

International Conference on Machine Learning and CyberneticsApr 12, 20052 citations

AN ASSOCIATIVE MODEL BASED ON QUANTUM SEARCH ALGORITHMS

Hiromi Miyajima, Noritaka Shigei Masataka Fujisaki

In this paper, an algorithm of quantum associative memory is proposed using quantum search algorithms. In the associative memory, the problem is to find the data or the nearest data in the meaning of hamming distance from designated data. If the search data in memorized dataset is contained, the problem becomes data search one. In associative memory, it is difficult to find the nearest data in hamming distance. For searching any item in an unsorted database with N items, a classical takes O(N) steps but Grover’s quantum searching algorithm takes only O( p N) steps on perfect graph. By generalizing the idea, Ventura has proposed the associative memory model, but it does not solve the problem of finding the nearest data. On the other hand, quantum search algorithm with O( p N) steps on hypercube is also proposed. Therefore, the idea in this paper is formalizing the problem as quantum search algorithm in hypercube but not in the conventional perfect graph. First, we perform quantum search algorithm by considering quantum walk on hypercube. Further, it is observed search data or the nearest data by using the conventional algorithm with high probability.

Digital Enterprise and Information SystemsJan 1, 20130 citations

IMPLEMENTING ASSOCIATIVE MEMORY WITH QUANTUM NEURAL NETWORKS

Jie-W Zhao

a Abstract: 1 In this paper an attempt is made to implement associative memory with quantum neural networks. The quantum algorithms for the storage of pattern and the retrieval of information are presented. It is shown that the capacity and the processing speed of the quantum neural network increase exponentially compared to the conventional model. The neural computing method for the calculation of quantum entanglement is also discussed.

Jan 1, 20040 citations

Quantum associative memory based on entanglement

YU Xiao-min

An approach to constructing an artificial quantum associative memory based on entanglement (QuEAM) is discussed. The QuEAM is an exponential increase in the capacity of the memory when compared to classical associative memories such as the Hopfield network. According to the characteristics of amount of entanglement, the study algorithm based on Grover's well-known algorithm is locally magnified the probability amplitude for the qubit. The basic principle of entangled states is discussed. Concrete examples illustrating the properties of the proposed model are also presented.

Chinese Journal of Quantum ElectronicsJan 1, 20051 citation

Quantum Image Processing

Anand, Alok, Meizhong Lyu, Prabh Simran Singh Baweja +1 more

Image processing is popular in our daily life because of the need to extract essential information from our 3D world, including a variety of applications in widely separated fields like bio-medicine, economics, entertainment, and industry. The nature of visual information, algorithm complexity, and the representation of 3D scenes in 2D spaces are all popular research topics. In particular, the rapidly increasing volume of image data as well as increasingly challenging computational tasks have become important driving forces for further improving the efficiency of image processing and analysis. Since the concept of quantum computing was proposed by Feynman in 1982, many achievements have shown that quantum computing has dramatically improved computational efficiency [1]. Quantum information processing exploit quantum mechanical properties, such as quantum superposition, entanglement and parallelism, and effectively accelerate many classical problems like factoring large numbers, searching an unsorted database, Boson sampling, quantum simulation, solving linear systems of equations, and machine learning. These unique quantum properties may also be used to speed up signal and data processing. In quantum image processing, quantum image representation plays a key role, which substantively determines the kinds of processing tasks and how well they can be performed.

arXiv:2203.01831Mar 1, 20226 citations