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Quantum Entanglement Encoding of a Continual Learning Model

Jincheng Zhang

Published Sep 5, 2026• 0 citations
Publisher

Abstract

This paper proposes a novel continual learning model leveraging the unique properties of quantum entanglement to mitigate the catastrophic forgetting problem prevalent in classical machine learning. The core idea involves constructing a "quantum memory" based on entangled states, enabling the encoding, updating, and retrieval of knowledge through controlled manipulation of these states and subsequent quantum measurements. We present a theoretical framework outlining this approach, focusing on the mathematical representation of the entanglement-based learning process. Specifically, we define the state vectors (s1, s2, ..., sK) representing the entangled quantum memory, where K denotes the number of learned concepts. The learning algorithm utilizes unitary transformations (Ui) applied to the state vectors, updating the representation of each concept. Quantum measurement (Mi) is employed to extract information and assess the accuracy of the learned concepts. The central challenge lies in designing the appropriate unitary transformations and measurement operators to effectively capture new information while preserving previously learned knowledge. We explore the mathematical relationships between the state vectors, unitary transformations, and measurement operators, demonstrating how these elements contribute to the continual learning process. This research contributes to the emerging field of quantum machine learning, offering a potential pathway to overcoming the limitations of traditional models in scenarios requiring long-term memory and adaptive learning. The proposed model's ability to maintain and update knowledge without forgetting represents a significant advancement in the field.

DOI
10.5281/zenodo.22339807

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