Ansatz-Agnostic Exponential Resource Saving in Variational Quantum Algorithms Using Shallow Shadows
Authors: Afrad Basheer, Yuan Feng, Christopher Ferrie, Sanjiang Li
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also experimentally demonstrate orders of magnitude improvement in comparison to the standard VQA model. Here we elaborate on the experimental results by comparing the sample complexity of AISO and the standard VQA in the two use cases discussed above. |
| Researcher Affiliation | Academia | 1Centre for Quantum Software and Information, University of Technology Sydney, NSW 2007, Australia 2Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China |
| Pseudocode | No | The paper describes the AISO protocol in numbered steps in Section 4, but these steps are presented as descriptive text within a paragraph rather than a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not include any explicit statement about releasing source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper uses '8-qubit states' for VQSP and '4-qubit quantum gates' for VQCS, which are generated for the experiments. There is no concrete access information (link, DOI, repository, or formal citation to a public dataset) provided for these specific states or gates. |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits. It discusses the number of iterations and function evaluations for optimization algorithms (SPSA, Powell's method) and the total number of copies used, but not data partitioning details. |
| Hardware Specification | No | The paper discusses quantum devices and NISQ era challenges but does not provide any specific hardware details (like exact GPU/CPU models, processors, or memory) used for running the classical simulations or experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers, that would be needed to replicate the experiment. |
| Experiment Setup | Yes | For VQSP, we have used the Simultaneous Perturbation Stochastic Approximation [Spall, 1992] (SPSA), where the converging sequences used are, respectively, cr = ar = r 0.4 and the total number of iterations is 5000. On the other hand, the results of VQCS have used Powell s method [Powell, 1964] with a maximum of 103 function evaluations allowed. The depth d of the shallow shadow ensemble (cf. Figure 2) is set to 3 throughout the experiments. |