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.