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Dynamic Programming for Quantum State Preparation

Jincheng Zhang

Published Sep 6, 2026• 0 citations
Publisher

Abstract

Quantum state preparation is a critical component in many quantum algorithms, yet its complexity scales exponentially with the number of qubits and the desired state's complexity. This work presents a novel approach to quantum state preparation utilizing dynamic programming. We demonstrate that dynamic programming can efficiently determine optimal sequences of quantum gates to realize a target quantum state, significantly reducing the computational burden compared to exhaustive search methods. The core of our method lies in formulating the problem as a state-space optimization task, where each state represents a partial quantum state and the transition between states corresponds to applying a quantum gate. We detail the algorithm and provide a theoretical analysis of its performance, highlighting its potential to mitigate the exponential scaling issues inherent in traditional quantum state preparation techniques. This approach offers a practical and scalable solution for preparing complex quantum states, representing a significant advancement in the field.

DOI
10.5281/zenodo.22459598

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