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Quantum Conformal Prediction (QCP)
This repository contains code for "Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning" - Sangwoo Park and Osvaldo Simeone.
Dependencies
This program is written in python 3.9.7 and uses PyTorch 1.10.2.
Basic Usage
- All the essential components of QCP can be found in the file 'set_predictors/quantum_conformal_prediction.py'.
- PQC with different angle encodings (fixed, linear, non-linear angle encoding, see Fig. 9) can be found in the file 'quantum_circuit/PQC.py'.
- In order to deploy the above PQC to IBM Quantum NISQ devices, 'quantum_circuit/PQC_with_qiskit.py' might be useful.
Unsupervised Learning (Density Learning for Classical Data)
- Main file is 'main_density_learning.py', while the 'runs/density_learning' folder contains the required running shell scripts.
Supervised Learning (Regression for Classical Data)
- Main file is 'main_regression.py', while the 'runs/regression' folder contains the required running shell scripts.
Quantum Data Classification
- Stand-alone code for quantum data classificaiton can be found in the 'quantum_classification/' folder.