This project aims to apply various denoising techniques to identify anomalies in various forms of time series, e.g. in vibrations, medical or radio signals.
Current version: V3.00
- Several tests conducted on the full-QAE to determine its performance in response to the size of latent/trash space.
The current work is summarised here:
- qiskit_qae/ts_qiskit_qae_binary_...: TS QAE based on binary encoding of TS windows developed and tested, abandoned.
- qiskit_qae/ts_qiskit_qae_unary_...: TS QAE based on unary encoding of TS windows developed and tested, abandoned.
- qiskit_qae/ts_qiskit_qae_angles_...: TS QAE based on angle encoding of TS windows developed and tested, results are promising.
This repository consists of the following groups of notebooks:
- dataset: samples of data used by the notebooks
- images: images used in the notebooks
- classic_pytorch: classically computed solutions with PyTorch
- classic_tensorflow: classically computed solutions with Tensorflow
- qiskit_qae: quantum autoencoders with Qiskit
- pennylane_qae: quantuym autoencoders with PennyLane
- runs: experimental runs with important results
- tests: small tests of various features
- tutorials: tutorials and demos from external sources
- legacy: prgrams no longer needed or used
To get installation details of Python, Anaconda / Miniconda, Pytorch, Tensorflow, Qiskit and PennyLane please refer to their respective web sites.