Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Efficient Quantum Algorithms for Quantum Optimal Control
Authors: Xiantao Li, Chunhao Wang
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we present efficient quantum algorithms that are exponentially faster than classical algorithms for solving the quantum optimal control problem. This problem involves finding the control variable that maximizes a physical quantity at time T, where the system is governed by a time-dependent Schr odinger equation. This type of control problem also has an intricate relation with machine learning. Our algorithms are based on a time-dependent Hamiltonian simulation method and a fast gradient-estimation algorithm. We also provide a comprehensive error analysis to quantify the total error from various steps, such as the finite-dimensional representation of the control function, the discretization of the Schr odinger equation, the numerical quadrature, and optimization. Our quantum algorithms require fault-tolerant quantum computers. |
| Researcher Affiliation | Academia | 1Department of Mathematics, Pennsylvania State University, University Park, USA 2Department of Computer Science and Engineering, Pennsylvania State University, University Park, USA. |
| Pseudocode | Yes | Algorithm 1 Quantum algorithm for solving QOC |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The numerical example uses |
| Dataset Splits | No | The paper is primarily theoretical and does not conduct typical machine learning experiments with train/validation/test splits. The numerical example does not specify any dataset splits. |
| Hardware Specification | No | The |
| Software Dependencies | No | The numerical example section does not specify any software names with version numbers, such as programming languages, libraries, or solvers used for the implementation. |
| Experiment Setup | Yes | The step size is set to δ = 0.02. In the optimization, we choose the learning rate to be 0.04. Using u(t) = 0 as the initial guess, we apply Equation (11) for 2000 iterations. |