Quantum-Inspired Memory Networks with Entangled Representation

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Traditional memory networks (MemNets) face limitations in storing and retrieving vast amounts of sequential data efficiently. This paper proposes a novel architecture, Quantum-Inspired Memory Networks (QIMNs), designed to overcome these challenges by incorporating quantum-inspired entanglement between memory slots. The core idea is to mimic the principles of quantum entanglement to create a more compact and efficient representation of information within the memory network. This allows for faster retrieval and improved performance, particularly when dealing with long sequences. We introduce a mechanism that simulates entanglement, represented mathematically as a correlation function between memory slots. The network's architecture utilizes this correlation to reduce redundancy and accelerate information access. The proposed QIMN architecture is evaluated conceptually, outlining the key components and their interactions, and highlighting the potential benefits compared to conventional MemNets. We believe this approach offers a viable pathway for scaling memory networks to handle increasingly complex sequential data problems.

DOI: 10.5281/zenodo.22292346

Related papers

Quantum-Inspired Distributed Memory Networks

Jincheng Zhang

This paper presents a novel architecture for distributed memory networks inspired by the principles of quantum superposition and entanglement. Traditional distributed memory systems struggle with scalability and efficiency when dealing with large datasets and complex pattern recognition tasks. Our approach addresses these limitations by leveraging probabilistic representations of data nodes, mimicking quantum amplitudes, which enable multiple simultaneous representations and correlations. A key component is the "entanglement protocol," designed to facilitate efficient information sharing and accelerate retrieval processes across the network. The core claim of this work is to demonstrate exponentially faster information retrieval and pattern recognition capabilities compared to conventional distributed memory models. We outline the theoretical framework, detailing the mathematical formulations underlying the network architecture and the entanglement protocol. The proposed system offers a fundamentally different paradigm for data storage and access, moving beyond classical constraints and potentially unlocking significant performance gains in data-intensive applications.

Sep 2, 20260 citations

Quantum-Inspired Memory Networks with Adaptive Decoherence

Jincheng Zhang

Classical Memory Networks (MemNets) grapple with the "forgetting" problem, struggling to maintain relevant information over extended sequences. This paper proposes a novel architecture – Quantum-Inspired Memory Networks with Adaptive Decoherence – designed to mitigate this issue. The core idea is to introduce a decoherence mechanism inspired by quantum physics, allowing the network to dynamically adjust the decay rate of information stored in memory slots. This adaptation is governed by relevance and usage patterns, leading to optimized information retention and retrieval. Unlike existing approaches that rely on fixed decay rates or simple gating mechanisms, our system leverages an adaptive decoherence rate, providing a more nuanced and effective strategy for managing memory in sequence modeling tasks. We demonstrate the potential of this approach through a theoretical analysis and outline a framework for implementation.

Aug 29, 20260 citations

Quantum Entanglement Memory Network (QEMN)

Jincheng Zhang

This paper introduces the Quantum Entanglement Memory Network (QEMN), a novel computational architecture designed to overcome the limitations of conventional storage media. The core concept leverages the inherent properties of multi-body quantum entanglement to achieve unprecedented information density, alongside the potential for ultra-fast access times and significantly reduced energy consumption. We propose a mechanism for encoding multi-dimensional information through the controlled manipulation of entangled superconducting qubits, utilizing a carefully designed entanglement topology for efficient quantum operations. The QEMN architecture represents a fundamental shift in data storage paradigms, offering a pathway towards scalable quantum memory solutions. This work outlines the theoretical framework, key components, and anticipated benefits of the QEMN system, highlighting its potential to revolutionize information processing and storage.

Sep 5, 20260 citations

Quantum-Inspired Entanglement-Based Neural Networks

Jincheng Zhang

This paper proposes a novel neural network architecture, termed Entanglement-Based Neural Networks (EBNNs), inspired by the principles of quantum entanglement. Conventional classical neural networks often face challenges in effectively capturing and representing intricate correlations within data, particularly when dealing with high-dimensional or complex datasets. The core idea behind EBNNs is to construct networks where nodes are interconnected in a manner that mirrors the correlated states observed in quantum entanglement, even across spatial separation. This is achieved through a specifically designed network topology that introduces dependencies between nodes, allowing for a more efficient representation of complex relationships. The network's structure is defined by a set of interconnected nodes, each possessing a state (s_i) represented by a real number. The connections between nodes are characterized by a correlation function (C_{ij}), which dictates the degree to which the state of node *j* is influenced by the state of node *i*. The architecture utilizes a layered approach, where layers are composed of interconnected nodes. The propagation of information through the network is governed by a modified version of the Hebbian learning rule, adapted to incorporate the entanglement-like correlation functions. This approach aims to improve the network's ability to learn and represent complex patterns by leveraging inherent correlations rather than relying solely on independent node activations. The key contributions of this work are the conceptual design of EBNNs and the formulation of the core mathematical framework for their implementation. We present a detailed description of the network architecture, the correlation function definition, and the learning rule, offering a foundation for further research and development in this area. Finally, we demonstrate the potential advantages of EBNNs in terms of improved learning efficiency and representation capacity.

Aug 29, 20260 citations

Quantum Entanglement Memory Network

Jincheng Zhang

This paper proposes a novel information storage and retrieval architecture, the Quantum Entanglement Memory Network (QEMN), leveraging the principles of quantum entanglement. The core claim is that utilizing entangled quantum states for information storage and retrieval can overcome the limitations of classical storage and enable precise simulations of complex systems. The proposed mechanism involves constructing a network of quantum bits (qubits) entangled to create a network where information is encoded and decoded through the manipulation of superposition and collapse of the entangled states. QEMN represents a fundamentally new approach to information processing by integrating quantum entanglement as the central storage mechanism within a neural network architecture. We will explore the theoretical framework, detailing the key equations and mathematical representations involved in the operation of the network. The potential impact of QEMN on computational capabilities and complex system modeling is discussed.

Sep 5, 20260 citations

Quantum-Inspired Neural Networks with Entangled Node Representations

Jincheng Zhang

Quantum-inspired neural networks (QINNs) have emerged as a promising approach to leveraging quantum computational principles for accelerating and enhancing artificial intelligence. However, a significant limitation of existing QINNs lies in their failure to fully capture the essence of quantum entanglement, a cornerstone of quantum mechanics. This paper proposes a novel architecture, termed Entangled Node Representation (ENR) QINNs, which directly incorporates true quantum entanglement into the network structure. The core idea is to represent nodes as entangled qubits, allowing for inherent correlations between nodes that surpass the limitations of classical neural networks. We postulate that this approach will dramatically improve learning efficiency by enabling the network to explore a wider solution space and converge faster. The proposed ENR QINNs introduce a formal mathematical framework for modeling and training these networks, exploring the potential of superposition and interference effects within the network's architecture. The theoretical analysis demonstrates that the ENR QINNs possess the potential to achieve exponential speedups in computation compared to traditional neural networks, particularly in tasks involving complex correlations. We outline key considerations for the design and training of ENR QINNs, including qubit representation choices, entanglement management strategies, and optimization algorithms specifically tailored to exploit the quantum properties of the network. Further research will focus on experimental validation and scaling the architecture for real-world applications.

Aug 28, 20260 citations