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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Classically Approximating Variational Quantum Machine Learning with Random Fourier Features
Authors: Jonas Landman, Slimane Thabet, Constantin Dalyac, Hela Mhiri, Elham Kashefi
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this Section, we aim to assess the accuracy and efficiency of our classical methods to approximate VQCs in practice. Each VQC was analyzed using ideal simulators of quantum computers, on a classical computer, without taking the noise into account. Important complementary experiments are provided in Appendix G. In particular, we show scaling simulations in Appendix G.5. |
| Researcher Affiliation | Collaboration | Jonas Landman University of Edinburgh QC Ware; Slimane Thabet LIP6, Sorbonne Universit e PASQAL SAS; Constantin Dalyac LIP6, Sorbonne Universit e PASQAL SAS; Hela Mhiri LIP6, Sorbonne Universit e ENSTA Paris; Elham Kashefi University of Edinburgh LIP6, Sorbonne Universit e |
| Pseudocode | Yes | Algorithm 1 RFF with Distinct Sampling; Algorithm 2 RFF with Tree Sampling; Algorithm 3 RFF with Grid Sampling |
| Open Source Code | Yes | CODE AVAILABILITY: All the code that was used in this project is available following the anonymous link https://osf.io/by5dk/?view_only=5688cba7b13d44479f76e13e01d28d75 |
| Open Datasets | Yes | We choose the fashion-MNIST dataset (Xiao et al. (2017)), where we consider a binary image classification task. We also use the California Housing dataset for a regression task. |
| Dataset Splits | No | The paper mentions 'Ntrain = 9600 and Ntest = 2400' for Fashion-MNIST and 'Ntrain = 5000 and Ntest = 1000' for California Housing, but does not provide specific details on a separate validation set split, only mentioning early stopping which typically uses one. |
| Hardware Specification | No | The paper states that experiments were run 'on a classical computer' but does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts. |
| Software Dependencies | No | The paper mentions software like 'Py Torch' and 'Scikit-learn', and optimizers like 'Adam', but it does not specify version numbers for these components, which are necessary for reproducible software dependencies. |
| Experiment Setup | Yes | The final VQC predictions are obtained after 60 epochs using Adam optimizer with learning rate = 0.01 . For Tree sampling RFF training, trained for 2000 epochs with early stopping using Adam optimizer with learning rate = 0.05. The final VQC predictions are obtained after 100 epochs using Adam optimizer with learning rate = 0.01. |