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东华大学学报：英文版

Using Quantum Associative Memory to Simulate the Brain Functions

Changle Zhou

Published Jan 1, 2010• 1 citation
OpenAlex

Abstract

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.

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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.
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.
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.
A quantum associative memory, much more natural than those of “quantum computers”, is presented. Neural-net-like processing with real-valued variables is transformed into processing with quantum waves. Successful computer simulations of image storage and retrieval are reported. Our Hopfield-like algorithm allows quantum implementation with holographic procedure using present-day quantum-optics techniques. This brings many advantages over classical Hopfield neural nets and quantum computers with logic gates.
This paper presents the first attempt of designing a physical quantum-like `brain' for humanoid and experimentally proves a 2D invariant object recognition model inspired by the image processing along the human visual pathway: retina, lateral geniculate nucleus (LGN) and primary visual cortex (V1). It also describes storing the output of the model in quantum holographic memory (a hologram) and the method to reconstruct the visual representation. The work is implemented as system-on-a-chip in field-programmable gate array (FPGA).