Revisiting Online Quantum State Learning
Authors: Feidiao Yang, Jiaqing Jiang, Jialin Zhang, Xiaoming Sun6607-6614
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition to the theoretical analysis, we evaluate the algorithms with a series of simulation experiments. The experimental results show that our FTPL method and OGD method outperform the existing RFTL approach proposed by Aaronson et al. (2018) in almost all settings. |
| Researcher Affiliation | Academia | Feidiao Yang, Jiaqing Jiang, Jialin Zhang, Xiaoming Sun Institute of Computing Technology, Chinese Academy of Sciences, Bejing, China University of Chinese Academy of Sciences, Beijing, China {yangfeidiao, jiangjiaqing, zhangjialin, sunxiaoming}@ict.ac.cn |
| Pseudocode | Yes | Algorithm 1 FTPL algorithm for pure state prediction; Algorithm 2 Projection onto the set of density matrices; Algorithm 3 OGD method for online quantum state learning |
| Open Source Code | No | The paper does not provide any statements about releasing source code or links to a code repository. |
| Open Datasets | No | We implement and compare the three algorithms we discussed, the FTPL method, the OGD method, and the RFTL method with our closed-form solution. In this experiment, we consider a typical and reasonable setting described as follows. First, it is a realizable setting, that is, there is an underlying unknown quantum state ρ, pure or mixed, to be learned. The loss functions ℓt are determined by the measurements applied to ρ, although the measurements could be chosen randomly or adversarially. Specifically, we consider the absolute loss ℓt(z) = |z bt|, in which bt is a Bernoulli random variable with expectation E[bt] = Tr(Etρ), corresponding to the result of measuring ρ with Et. [...] For this purpose, we propose an adaptively adversarial data generation policy to select Et. |
| Dataset Splits | No | The paper describes sequential data generation for experiments rather than using predefined dataset splits. No specific validation split percentages or methods are mentioned. |
| Hardware Specification | No | The paper does not explicitly mention any hardware specifications (e.g., CPU, GPU models) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | Regarding other experimental parameters, we take the number of qubits n = 4, so the dimension of the density matrices is 16 16. For each experiment, we run for 100 trials with randomly generated target states ρ and report the average curves. |