This repository contains the code to reproduce the results presented in the paper Quantum Splines for Non-Linear Approximations, published in the Proceedings of the 17th ACM International Conference on Computing Frontiers (CF'20) and freely available on the ACM Digital Library.
The idea underpinning the work is to leverage spline functions to overcome the constraint to unitary transformations and being able to approximate non-linear functions on a quantum device. To demonstrate this, we formulated the problem according to a B-Spline parametrisation and we investigated 4 popular non-linear activation functions commonly used for Neural Networks, namely sigmoid, hyperbolic tangent (tanh), rectified linear unit (relu) and exponential linear unit (elu).
The code is organised in four different scripts, one per activation function. Specifically, Sigmoid.py, Tanh.py, Relu.py, Elu.py compute a B-spline approximation of the corrispondent function using three approaches:
- Full quantum: uses the HHL quantum algorithm to estimate B-spline coeffients and a second quantum circuit for function evaluation
- Quantum Hybrid: uses the HHL algorithm to estimates B-Spline coefficients and classical approach for function evaluation
- Classical: coefficient estimates and function evaluation are performed under the classical computing paradigm
The script Utils.py is used to import the needed packages and all of the custom routines for function evaluation.
The script Utils_Spline.py contains the custom routines for B-Spline coefficients estimation.
The script Viz_complexity.py plots the comparison between quantum HHL and classical algorithms for matrix inversion in terms of computational complexity.
The script All_viz.py plots in one plot all the activation functions.
In order to run the code and reproduce the results of the paper, it is recommended to re-create the same testing environment following the procedure below.
Note: tested on linux OS only; it assumes Anaconda is installed
- First, clone the repository:
git clone https://github.com/amacaluso/Quantum_Spline.git
- Second, create a conda environment from scratch using the environment.yml specs:
# enter the repository
cd Quantum_Spline
# create an environment named QSplines with desired dependencies
conda env create -f environment.yml
- Third, install qiskit package:
# activate the environmet
conda activate QSplines
# install qiskit 0.13
pip install qiskit==0.13
For any issues or questions related to the code, open a new git issue or send a mail to [email protected]