Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

Quantum associative memory that processes nonorthogonal patterns

Lingxiang Zheng, Changle Zhou

Published Nov 1, 2008• 1 citation
Publisher

Abstract

A novel quantum associative memory using the quantum holography scheme is presented. The patterns stored in the model are encoded into a set of independent quantum states. It overcomes the harm of nonorthogonal by storing these independent patterns into the memory using an iterative learning algorithm. The positive operation valued measurement is performed on the selectively recalled quantum state when does the information retrieval. Computer simulations show that the model exhibits good associative ability. Benefiting from the quantum implementation, the model not only can improve the performance and storage capacity, it also may be a suitable model to explain the biological associative phenomena because the phase of oscillation is inherent in the model.

DOI
10.1109/iske.2008.4730997

Related papers

Quantum Associative Memory Based on Subspace Projection

Lingxiang Zheng

To study the biological associative phenomena, we propose a novel quantum associative neural network model based on the subspace projection transformation of the quantum system. The patterns are encoded into a set of independent quantum state and iteratively stored into the quantum associative memory. The information retrieval is an action of projection transformation and follows a POVM measurement. Computer simulations show that it exhibits good associative ability. It can associatively recognize the face with different expression from a set of face images. In addition, the model can dramatically improve the storage capacity and process speed compared with the classical neural network model due to the quantum techniques. Compared with previous quantum associative memory models, our model can memorize and retrieval nonorthogonal quantum patterns with good performance.

International Conference on Intelligent Networks and Intelligent Systems•Nov 1, 2008•0 citations

Artificial Associative Memory Using Quantum Processes

Dan A. Ventura

Abstract This paper discusses an approach to constructing an artificial quantum associative memory (QuAM). The QuAM makes use of two quantum computational algorithms, one for pattern storage and the other for pattern recall. The result is an exponential increase in the capacity of the memory when compared to traditional associative memories such as the Hopfield network. Further, the paper argues for considering pattern recall as a non-unitary process and demonstrates the utility of non-unitary operators for improving the pattern recall performance of the QuAM. 1.

•Jan 1, 1998•12 citations

High-Capacity Quantum Associative Memories

M. C. Diamantini, Carlo A. Trugenberger

We review our models of quantum associative memories that represent the “quantization” of fully coupled neural networks like the Hopfield model. The idea is to replace the classical irreversible attractor dynamics driven by an Ising model with pattern-dependent weights by the reversible rotation of an input quantum state onto an output quantum state consisting of a linear superposition with probability amplitudes peaked on the stored pattern closest to the input in Hamming distance, resulting in a high probability of measuring a memory pattern very similar to the input. The unitary operator implementing this transformation can be formulated as a sequence of one-qubit and two-qubit elementary quantum gates and is thus the exponential of an ordered quantum Ising model with sequential operations and with pattern-dependent interactions, exactly as in the classical case. Probabilistic quantum memories, that make use of postselection of the measurement result of control qubits, overcome the famed linear storage limitation of their classical counterparts because they permit to completely eliminate crosstalk and spurious memories. The number of control qubits plays the role of an inverse fictitious temperature. The accuracy of pattern retrieval can be tuned by lowering the fictitious temperature under a critical value for quantum content association while the complexity of the retrieval algorithm remains polynomial for any number of patterns polynomial in the number of qubits. These models thus solve the capacity shortage problem of classical associative memories, providing a polynomial improvement in capacity. The price to pay is the probabilistic nature of information retrieval.

arXiv:1506.01231•Journal of Applied Mathematics and Physics•Jan 1, 2016•8 citations

Using Quantum Associative Memory to Simulate the Brain Functions

Changle Zhou

To simulate the brain functions,a quantum associative memory combined with information preprocessing by a sparse coding model is presented. The sparse coding scheme is used to simulate the information transformation from retina up to primary visual cortex (V1) along the visual path and the quantum associative memory is used to simulate the pattern processing functions of the brain such as the pattern storing,forgetting and retrieving. Experimental results show that the model exhibits good associative ability on face recognition. Considering the huge storage capacity,mass parallel-distributed processing ability and oscillatory phenomena of the quantum system,this model might be a biological plausible implementation.

东华大学学报：英文版•Jan 1, 2010•1 citation

Quantum-Inspired Multidirectional Associative Memory With a Self-Convergent Iterative Learning

Naoki Masuyama, Chu Kiong Loo, Manjeevan Seera +1 more

Quantum-inspired computing is an emerging research area, which has significantly improved the capabilities of conventional algorithms. In general, quantum-inspired hopfield associative memory (QHAM) has demonstrated quantum information processing in neural structures. This has resulted in an exponential increase in storage capacity while explaining the extensive memory, and it has the potential to illustrate the dynamics of neurons in the human brain when viewed from quantum mechanics perspective although the application of QHAM is limited as an autoassociation. We introduce a quantum-inspired multidirectional associative memory (QMAM) with a one-shot learning model, and QMAM with a self-convergent iterative learning model (IQMAM) based on QHAM in this paper. The self-convergent iterative learning enables the network to progressively develop a resonance state, from inputs to outputs. The simulation experiments demonstrate the advantages of QMAM and IQMAM, especially the stability to recall reliability.

IEEE Transactions on Neural Networks and Learning Systems•Feb 7, 2017•21 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 Cybernetics•Apr 12, 2005•2 citations
A novel quantum associative memory using the quantum holography scheme is presented. The patterns stored in the model are encoded into a set of independent quantum states. It overcomes the harm of nonorthogonal by storing these independent patterns into the memory using an iterative learning algorithm. The positive operation valued measurement is performed on the selectively recalled quantum state when does the information retrieval. Computer simulations show that the model exhibits good associative ability. Benefiting from the quantum implementation, the model not only can improve the performance and storage capacity, it also may be a suitable model to explain the biological associative phenomena because the phase of oscillation is inherent in the model.
To study the biological associative phenomena, we propose a novel quantum associative neural network model based on the subspace projection transformation of the quantum system. The patterns are encoded into a set of independent quantum state and iteratively stored into the quantum associative memory. The information retrieval is an action of projection transformation and follows a POVM measurement. Computer simulations show that it exhibits good associative ability. It can associatively recognize the face with different expression from a set of face images. In addition, the model can dramatically improve the storage capacity and process speed compared with the classical neural network model due to the quantum techniques. Compared with previous quantum associative memory models, our model can memorize and retrieval nonorthogonal quantum patterns with good performance.
Abstract This paper discusses an approach to constructing an artificial quantum associative memory (QuAM). The QuAM makes use of two quantum computational algorithms, one for pattern storage and the other for pattern recall. The result is an exponential increase in the capacity of the memory when compared to traditional associative memories such as the Hopfield network. Further, the paper argues for considering pattern recall as a non-unitary process and demonstrates the utility of non-unitary operators for improving the pattern recall performance of the QuAM. 1.
We review our models of quantum associative memories that represent the “quantization” of fully coupled neural networks like the Hopfield model. The idea is to replace the classical irreversible attractor dynamics driven by an Ising model with pattern-dependent weights by the reversible rotation of an input quantum state onto an output quantum state consisting of a linear superposition with probability amplitudes peaked on the stored pattern closest to the input in Hamming distance, resulting in a high probability of measuring a memory pattern very similar to the input. The unitary operator implementing this transformation can be formulated as a sequence of one-qubit and two-qubit elementary quantum gates and is thus the exponential of an ordered quantum Ising model with sequential operations and with pattern-dependent interactions, exactly as in the classical case. Probabilistic quantum memories, that make use of postselection of the measurement result of control qubits, overcome the famed linear storage limitation of their classical counterparts because they permit to completely eliminate crosstalk and spurious memories. The number of control qubits plays the role of an inverse fictitious temperature. The accuracy of pattern retrieval can be tuned by lowering the fictitious temperature under a critical value for quantum content association while the complexity of the retrieval algorithm remains polynomial for any number of patterns polynomial in the number of qubits. These models thus solve the capacity shortage problem of classical associative memories, providing a polynomial improvement in capacity. The price to pay is the probabilistic nature of information retrieval.
To simulate the brain functions,a quantum associative memory combined with information preprocessing by a sparse coding model is presented. The sparse coding scheme is used to simulate the information transformation from retina up to primary visual cortex (V1) along the visual path and the quantum associative memory is used to simulate the pattern processing functions of the brain such as the pattern storing,forgetting and retrieving. Experimental results show that the model exhibits good associative ability on face recognition. Considering the huge storage capacity,mass parallel-distributed processing ability and oscillatory phenomena of the quantum system,this model might be a biological plausible implementation.
Quantum-inspired computing is an emerging research area, which has significantly improved the capabilities of conventional algorithms. In general, quantum-inspired hopfield associative memory (QHAM) has demonstrated quantum information processing in neural structures. This has resulted in an exponential increase in storage capacity while explaining the extensive memory, and it has the potential to illustrate the dynamics of neurons in the human brain when viewed from quantum mechanics perspective although the application of QHAM is limited as an autoassociation. We introduce a quantum-inspired multidirectional associative memory (QMAM) with a one-shot learning model, and QMAM with a self-convergent iterative learning model (IQMAM) based on QHAM in this paper. The self-convergent iterative learning enables the network to progressively develop a resonance state, from inputs to outputs. The simulation experiments demonstrate the advantages of QMAM and IQMAM, especially the stability to recall reliability.