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QuaN: Noisy Dataset For Quantum Machine Learning
- Citation Author(s):
- Submitted by:
- Himanshu Sahu
- Last updated:
- Mon, 04/29/2024 - 08:01
- DOI:
- 10.21227/aqpc-1832
- Data Format:
- License:
- Categories:
- Keywords:
Abstract
QuaN is a collection of specially designed datasets for exploring the impact of noise quantum machine learning and other applications. The presented work focuses on the transformation of clean datasets into noisy counterparts across diverse domains, including MNIST-handwritten digits datasets, Medical MNIST, IRIS datasets and Mobile Health datasets. The dataset is created using noise from classical and quantum domains. The classical noise includes Gaussian distribution, Salt and Pepper method, Random Perturbation, Class Imbalance, and Missing values whereas the quantum noise includes bitflip, phase flip, amplitude damping etc. The dataset is stored in encoded as NumPy array for classical noise and quantum circuit for quantum noise which can be directly loaded and utilized for a QML application. PennyLane is used to create the dataset for tasks such as data encoding and Qasm circuit creation.
Dataset Files
- Pnuemonia (MedMNIST) dataset with classical noise and quantum noise. MedMNIST.zip (810.36 MB)
- IRIS dataset with classical noise. IRIS_CN.zip (55.57 kB)
- IRIS dataset with quantum noise. IRIS_QN.zip (225.42 kB)
- MNIST dataset with classical noise. MNIST_CN.zip (5.37 GB)
- MNIST dataset with quantum noise. MNIST_QN.zip (1.14 GB)
- Fashion MNIST dataset with classical noise. FashionMNIST_CN.zip (5.37 GB)
- Fashion MNIST dataset with quantum noise. FashionMNIST_QN.zip (1.29 GB)