Classification of patterns representing Apples and Oranges in three-qubit system

Singh, Manu Pratap; Radhey, Kishori; Saraswat, Vivek; Kumar, S. Mohan

doi:10.1007/s11128-016-1472-z

Cited by 4 publications

(8 citation statements)

References 22 publications

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“…The effects of Grover's and Ventura's algorithms have been studied over three input state types to determine which is more suitable for pattern classification. This study proved that some superpositions are not suitable for use as input datasets for classification because of the limitations of the algorithms used in the classification process [15].…”

Section: Introductionmentioning

confidence: 90%

“…However, it has been shown in [13][14][15]] that Grover's operator is more effective in cases where the number of patterns in the input dataset is large, which obviously contradicted Ventura's claim. In [13,14], the effects of applying Grover's [8] and Ventura's [9] algorithms were tested on a two-qubit system, while, in [15], this was tested on a three-qubit system. The effects of Grover's and Ventura's algorithms have been studied over three input state types to determine which is more suitable for pattern classification.…”

Section: Introductionmentioning

confidence: 98%

“…It was not only one of the first and most important contributions to quantum machine learning, but Ventura also claimed to provide a higher probability of success than Grover's algorithm. In [13][14][15], these quantum search algorithms (Grover's algorithm and Ventura's algorithm) are used to classify a pre-defined pattern in an unstructured dataset in two-and three-qubit systems; the classification probabilities were calculated when the two algorithms were applied to three input superposition types. However, it has been shown in [13][14][15]] that Grover's operator is more effective in cases where the number of patterns in the input dataset is large, which obviously contradicted Ventura's claim.…”

Section: Introductionmentioning

confidence: 99%

“…In [13][14][15], these quantum search algorithms (Grover's algorithm and Ventura's algorithm) are used to classify a pre-defined pattern in an unstructured dataset in two-and three-qubit systems; the classification probabilities were calculated when the two algorithms were applied to three input superposition types. However, it has been shown in [13][14][15]] that Grover's operator is more effective in cases where the number of patterns in the input dataset is large, which obviously contradicted Ventura's claim. In [13,14], the effects of applying Grover's [8] and Ventura's [9] algorithms were tested on a two-qubit system, while, in [15], this was tested on a three-qubit system.…”

Section: Introductionmentioning

confidence: 99%

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Quantum Pattern Classification in a Three-Qubit System

et al. 2023

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The problem of pattern classification in quantum data has been of great importance over the past few years. This study investigates the effect of deploying Grover’s, the partial diffusion, and the fixed-phase algorithms separately to amplify the amplitudes of a desired pattern in an unstructured dataset. These quantum search operators were applied to symmetric and antisymmetric input superpositions on a three-qubit system for 20 iterations each. After each iteration, different probabilities of classification were calculated in order to determine the accuracy of classification for each of the three quantum search operators. The results indicated that, in the case of applying the three quantum search operators to incomplete superposition input states, the partial diffusion operator outperformed the other operators with a probability of correct classification that reached 100% in certain iterations. It also showed that the classification accuracy of the fixed-phase operator exceeded the accuracy of the other two operators by 40% in most cases when the input state was a uniform superposition, and some of the basis states were phase-inverted.

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Section: Introductionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantum Pattern Classification in a Three-Qubit System

et al. 2023

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“…Quantum associative memories have been used to perform classification tasks in several works [27,17,23,4,28,29,30]. In [29] Grover's algorithm and a quantum associative memory based on it are used to perform classification tasks in a toy dataset representing orange and apples with 3-qubit patterns. In [23] the PQM is used to classify digits from the MNIST dataset, the PQM accuracy on the test set is only 50%.…”

Section: Related Workmentioning

confidence: 99%

Parametric Probabilistic Quantum Memory

et al. 2020

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Probabilistic Quantum Memory (PQM) is a data structure that computes the distance from a binary input to all binary patterns stored in superposition on the memory. This data structure allows the development of heuristics to speed up artificial neural networks architecture selection. In this work, we propose an improved parametric version of the PQM to perform pattern classification, and we also present a PQM quantum circuit suitable for Noisy Intermediate Scale Quantum (NISQ) computers. We present a classical evaluation of a parametric PQM network classifier on public benchmark datasets. We also perform experiments to verify the viability of PQM on a 5-qubit quantum computer.

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