Adaptive Variable Quantum State Learning for Quantum Computation

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel approach to quantum computation utilizing adaptive variable quantum state learning. We propose a quantum circuit architecture that dynamically adjusts its parameters based on the current state of the system, enabling the efficient and complex transformation of quantum states. The core mechanism centers on a self-learning variable quantum state, allowing the circuit to adapt to the quantum system's dynamics and exploit quantum entanglement for enhanced computational capabilities. We demonstrate the feasibility of this method through a simplified model, showcasing its potential for accelerating quantum algorithms and exploring novel applications within the field. The paper establishes a foundation for a new paradigm in quantum computation, emphasizing the integration of adaptive learning with the foundational principles of quantum mechanics.

DOI: 10.5281/zenodo.22204850

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