Qiskit Fall Fest Kolkata Chapter Hackathon 2022
Goal of the Hackathon Challenge: Finding the best test accuracy using one or more quantum machine learning algorithms, with or without the influence of noise.
Task Description:
- The dataset is first be trained using traditional machine learning algorithms to figure out the best test accuracy.
- Then two QML algorithms VQC and QSVM are used to obtain best accuracy without the effect of noise.
- And finally the target is to improve the test accuracy under noisy scenario.
Team Qbosons
Team Members: Vismay Joshi, Tanjin Adnan Abir, Abhishek Rawat, Rajat Lakhera, Suprabhat Sinha
Framework: Qiskit SDK
Dataset: Iris Dataset
Classical ML Algorithms: SVM, KNN, Decision Tree, Logistic Regression, Random Forest
QML Algorithms: Variational Quantum Classifier (VQC), Quantum SVM (QSVM)
Reference:
- Characterizing the Reproducibility of Noisy Quantum Circuits. S. Dasgupta, T. Humble, https://www.mdpi.com/1099-4300/24/2/244
- Maria Schuld and Francesco Petruccione, Supervised Learning with Quantum Computers, Springer 2018, doi:10.1007/978-3-319-96424-9
- Vojtech Havlicek, Antonio D. Córcoles, Kristan Temme, Aram W. Harrow, Abhinav Kandala, Jerry M. Chow and Jay M. Gambetta, Supervised learning with quantum enhanced feature spaces, Nature 567, 209-212 (2019), doi.org:10.1038/s41586-019-0980-2, arXiv:1804.11326
- Maria Schuld and Nathan Killoran, Quantum machine learning in feature Hilbert spaces, Phys. Rev. Lett. 122, 040504 (2019), doi.org:10.1103/PhysRevLett.122.040504, arXiv:1803.07128.
- Vedran Dunjko, Jacob M. Taylor and Hans J. Briegel, Quantum-Enhanced Machine Learning, Physical Review Letters 117 (13), 130501 (2016) doi:10.1103/PhysRevLett.117.130501 arXiv:1610.08251
- Alberto Peruzzo, Jarrod McClean, Peter Shadbolt, Man-Hong Yung, Xiao-Qi Zhou, Peter J. Love, Alán Aspuru-Guzik and Jeremy L. O'Brien, A variational eigenvalue solver on a quantum processor, Nature Communications, 5:4213 (2014), doi.org:10.1038/ncomms5213, arXiv:1304.3061.