Differentiable Quantum Computing for Large-scale Linear Control
Authors: Connor Clayton, Jiaqi Leng, Gengzhi Yang, Yi-Ling Qiao, Ming Lin, Xiaodi Wu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5.3 Numerical Experiments We conduct a numerical experiment to showcase the correctness of our quantum policy gradient algorithm. |
| Researcher Affiliation | Academia | 1Joint Center for Quantum Information and Computer Science, University of Maryland 2Department of Computer Science, University of Maryland 3Department of Mathematics, University of Maryland 4Center for Machine Learning, University of Maryland 5Department of Mathematics and Simons Institute for the Theory of Computing, UC Berkeley |
| Pseudocode | Yes | Algorithm 1 Quantum policy gradient Inputs: A, B, Q, R (problem data), K0 SK (initial guess), σ > 0 (step size/learning rate), θ (robustness parameter), N (number of iterations) Output: an approximate solution KN |
| Open Source Code | Yes | The code for both methods can be seen at https://github.com/Yiling_Qiao/diff_lqr. |
| Open Datasets | No | The paper describes constructing problem instances from physical systems ('a mass-spring-damper system', 'aircraft control problem') and defines the matrices A, B, Q, R directly, but does not provide access information (link, DOI, citation) to a publicly available or open dataset. |
| Dataset Splits | No | The paper discusses a 'training process' for the policy gradient method, but it does not specify any exact dataset split percentages, sample counts, predefined splits, or cross-validation setup for training, validation, or testing. |
| Hardware Specification | Yes | Both methods run on a classical simulator with Intel i910980XE CPU. |
| Software Dependencies | No | The paper mentions that code is available on GitHub but does not explicitly list specific software dependencies with version numbers (e.g., 'Python 3.8', 'PyTorch 1.9') within the paper's text. |
| Experiment Setup | Yes | Following a similar setup as in [45], a mass-spring-damper system with g = 4 masses is used for constructing our LQR problem. The state x = [p , v ] R2g contains positions and velocities, with dynamic and input matrices, A = 0 I T T , Q = I + 100e1e 1 , R = I + 4e2e 2 , where 0, I are g g zero and identity matrices, ei is the ith unit vector, and matrix T has 2 on the main diagonal and -1 on the first superand sub-diagonal. ... We set A = [[0, 1], [0, 0.5]] , B = [0, 1] , Q = [[10, 0], [0, 1]] , and R = [0.1]. |