State Preparation
Quantum state preparation (QSP) aims to efficiently create specific quantum states on a quantum computer, a crucial prerequisite for many quantum algorithms. Current research focuses on developing scalable and robust QSP methods, employing techniques like parameterized quantum circuits (PQCs) optimized via genetic algorithms or reinforcement learning, and generative models such as diffusion models that inherently respect quantum state properties. These advancements address the exponential scaling challenges of exact QSP, improving fidelity and reducing resource requirements, thereby paving the way for more practical and error-resistant quantum computations.
Papers
A Reinforcement Learning Environment for Directed Quantum Circuit Synthesis
Michael Kölle, Tom Schubert, Philipp Altmann, Maximilian Zorn, Jonas Stein, Claudia Linnhoff-Popien
Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemble
Chuangtao Chen, Qinglin Zhao, MengChu Zhou, Zhimin He, Zhili Sun, Haozhen Situ