Quantum associative memory

Ventura, Dan; Martinez, Tony

doi:10.1016/s0020-0255(99)00101-2

Cited by 245 publications

(163 citation statements)

References 33 publications

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“…Some methods have also been explored to connect quantum computation and machine learning. For example, the quantum computing version of artificial neural network has been studied from the pure theory to the simple simulated and experimental implementation [33]- [37]. Rigatos and Tzafestas [38] used quantum computation for the parallelization of a fuzzy logic control algorithm to speed up the fuzzy inference.…”

Section: Introductionmentioning

confidence: 99%

Quantum Reinforcement Learning

Dong

Chen

2005

Lecture Notes in Computer Science

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Abstract-The key approaches for machine learning, especially learning in unknown probabilistic environments are new representations and computation mechanisms. In this paper, a novel quantum reinforcement learning (QRL) method is proposed by combining quantum theory and reinforcement learning (RL). Inspired by the state superposition principle and quantum parallelism, a framework of value updating algorithm is introduced. The state (action) in traditional RL is identified as the eigen state (eigen action) in QRL. The state (action) set can be represented with a quantum superposition state and the eigen state (eigen action) can be obtained by randomly observing the simulated quantum state according to the collapse postulate of quantum measurement. The probability of the eigen action is determined by the probability amplitude, which is parallelly updated according to rewards. Some related characteristics of QRL such as convergence, optimality and balancing between exploration and exploitation are also analyzed, which shows that this approach makes a good tradeoff between exploration and exploitation using the probability amplitude and can speed up learning through the quantum parallelism. To evaluate the performance and practicability of QRL, several simulated experiments are given and the results demonstrate the effectiveness and superiority of QRL algorithm for some complex problems. The present work is also an effective exploration on the application of quantum computation to artificial intelligence.Index Terms-quantum reinforcement learning, state superposition, collapse, probability amplitude, Grover iteration.

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Section: Introductionmentioning

confidence: 99%

Quantum Reinforcement Learning

Dong

Chen

2005

Lecture Notes in Computer Science

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“…Another possible future work is the analysis of quantum memories [46,47] for the development of weightless neural networks models. These quantum memories has an exponential gain in memory capacity when compared with classical memories.…”

Section: Resultsmentioning

confidence: 99%

Weightless neural network parameters and architecture selection in a quantum computer

2016

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Training artificial neural networks requires a tedious empirical evaluation to determine a suitable neural network architecture. To avoid this empirical process several techniques have been proposed to automatise the architecture selection process. In this paper, we propose a method to perform parameter and architecture selection for a quantum weightless neural network (qWNN). The architecture selection is performed through the learning procedure of a qWNN with a learning algorithm that uses the principle of quantum superposition and a non-linear quantum operator. The main advantage of the proposed method is that it performs a global search in the space of qWNN architecture and parameters rather than a local search.

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“…A quantum associative memory with a capacity exponential in the number of qubits and based on Grover's algorithm has been proposed by Ventura and Martinez [10][11][12]. This kind of memory solves the problem of pattern completion.…”

Section: Completing Quantum Associative Memorymentioning

confidence: 99%

“…These quantum neural networks have many promising characteristics, both in the case of supervised and unsupervised learning. In particular, an associative memory based on the use of Grover's quantum search algorithm [9] has been proposed by Ventura and Martinez [10][11][12]. This network solves the completion problem; that is, it can restore the full pattern when initially presented with just a part of that pattern.…”

Section: Introductionmentioning

confidence: 99%

Quantum associative memory with distributed queries

Ezhov

Nifanova

Ventura

2000

Information Sciences

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This paper discusses a model of quantum associative memory which generalizes the completing associative memory proposed by Ventura and Martinez. Similar to this model, our system is based on Grover's well known algorithm for searching an unsorted quantum database. However, the model presented in this paper suggests the use of a distributed query of general form. It is demonstrated that spurious memories form an unavoidable part of the quantum associative memory model; however, the very presence of these spurious states provides the possibility of organizing a controlled process of data retrieval using a specially formed initial state of the quantum database and also of the transformation performed upon it. Concrete examples illustrating the properties of the proposed model are also presented.

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Quantum associative memory

Cited by 245 publications

References 33 publications

Quantum Reinforcement Learning

Quantum Reinforcement Learning

Weightless neural network parameters and architecture selection in a quantum computer

Quantum associative memory with distributed queries

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