Classically Approximating Variational Quantum Machine Learning with Random Fourier Features
Authors: Jonas Landman, Slimane Thabet, Constantin Dalyac, Hela Mhiri, Elham Kashefi
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | 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. |