Abstract
Training parameterized quantum circuits (PQCs) is a growing research area that has received a boost from the emergence of new hybrid quantum classical algorithms and Quantum Machine Learning (QML) to leverage the power of today’s quantum computers. However, a universal pipeline that guarantees good learning behavior has not yet been found, due to several challenges. These include in particular the low number of qubits and their susceptibility to noise but also the vanishing of gradients during training. In this work, we apply and evaluate Triplet Loss in a QML training pipeline utilizing a PQC for the first time. We perform extensive experiments for the Triplet Loss based setup and training on two common datasets, the MNIST and moon dataset. Without significant fine-tuning of training parameters and circuit layout, our proposed approach achieves competitive results to a regular training. Additionally, the variance and the absolute values of gradients are significantly better compared to training a PQC without Triplet Loss. The usage of metric learning proves to be suitable for QML and its high dimensional space as it is not as restrictive as learning on hard labels. Our results indicate that metric learning provides benefits to mitigate the so-called barren plateaus.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Moon dataset. https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html. Accessed 04 Apr 2022
Benedetti, M., Lloyd, E., Sack, S., Fiorentini, M.: Parameterized quantum circuits as machine learning models. Quant. Sci. Technol. 4(4), 043001 (2019). https://doi.org/10.1088/2058-9565/ab4eb5
Bilkis, M., Cerezo, M., Verdon, G., Coles, P.J., Cincio, L.: A semi-agnostic ansatz with variable structure for quantum machine learning (2021). https://arxiv.org/abs/2103.06712
Cerezo, M., et al.: Variational quantum algorithms. nature reviews. Physics 3(9), 625–644 (2021). https://doi.org/10.1038/s42254-021-00348-9
Cerezo, M., Sone, A., Volkoff, T., Cincio, L., Coles, P.J.: Cost function dependent barren plateaus in shallow parametrized quantum circuits. Nature Commun. 12(1), 1791 (2021). https://doi.org/10.1038/s41467-021-21728-w
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1007/BF00994018
Dunjko, V., Wittek, P.: A non-review of quantum machine learning: trends and explorations. Quantum Views 4, 32 (2020)
Grant, E., et al.: Hierarchical quantum classifiers. NPJ Quantum Inf. 4(1), 65 (2018). https://doi.org/10.1038/s41534-018-0116-9
Grant, E., Wossnig, L., Ostaszewski, M., Benedetti, M.: An initialization strategy for addressing barren plateaus in parametrized quantum circuits. Quantum 3, 214 (2019). https://doi.org/10.22331/q-2019-12-09-214
Hettinger, C., Christensen, T., Ehlert, B., Humpherys, J., Jarvis, T., Wade, S.: Forward thinking: building and training neural networks one layer at a time (2017)
Holmes, Z., Sharma, K., Cerezo, M., Coles, P.J.: Connecting ansatz expressibility to gradient magnitudes and barren plateaus. PRX Quantum 3, 010313, Published 24 January 2022 (2021). https://doi.org/10.1103/PRXQuantum.3.010313, https://arxiv.org/abs/2101.02138
Kaya, M., Bilge, H.Ş.: Deep metric learning: a survey. Symmetry 11(9), 1066 (2019)
Kulis, B., et al.: Metric learning: a survey. Found. Trends® Mach. Learn. 5(4), 287–364 (2013)
LaRose, R., Coyle, B.: Robust data encodings for quantum classifiers. Phys. Rev. A 102, 032420 (2020). https://doi.org/10.1103/PhysRevA.102.032420, https://arxiv.org/abs/2003.01695
LeCun, Y., Cortes, C.: MNIST handwritten digit database (2010). https://yann.lecun.com/exdb/mnist/
Lloyd, S., Schuld, M., Ijaz, A., Izaac, J., Killoran, N.: Quantum embeddings for machine learning. arXiv preprint arXiv:2001.03622 (2020)
McClean, J.R., Boixo, S., Smelyanskiy, V.N., Babbush, R., Neven, H.: Barren plateaus in quantum neural network training landscapes. Nat. Commun. 9(1), 1–6 (2018)
McClean, J.R., Romero, J., Babbush, R., Aspuru-Guzik, A.: The theory of variational hybrid quantum-classical algorithms. New J. Phys. 18(2), 023023 (2016)
Nielsen, M.A., Chuang, I.L.: Quantum Computation and Quantum Information. Cambridge University Press, Cambridge (2000)
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Pesah, A., Cerezo, M., Wang, S., Volkoff, T., Sornborger, A.T., Coles, P.J.: Absence of barren plateaus in quantum convolutional neural networks. Phys. Rev. X 11, 041011 (2021). https://doi.org/10.1103/PhysRevX.11.041011
Preskill, J.: Quantum computing in the NISQ era and beyond. Quantum 2, 79 (2018)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)
Schuld, M.: Effect of data encoding on the expressive power of variational quantum-machine-learning models. Phys. Rev. A 103(3), 032430 (2021). https://doi.org/10.1103/PhysRevA.103.032430
Schuld, M., Bergholm, V., Gogolin, C., Izaac, J., Killoran, N.: Evaluating analytic gradients on quantum hardware. Phys. Rev. A 99(3), 032331 (2019)
Skolik, A., McClean, J.R., Mohseni, M., van der Smagt, P., Leib, M.: Layerwise learning for quantum neural networks. Quantum Mach. Intell. 3(1), 1–11 (2021). https://doi.org/10.1007/s42484-020-00036-4
Thumwanit, N., Lortaraprasert, C., Yano, H., Raymond, R.: Trainable discrete feature embeddings for variational quantum classifier (2021). https://arxiv.org/abs/2106.09415
Wecker, D., Hastings, M.B., Troyer, M.: Progress towards practical quantum variational algorithms. Phys. Rev. A 92(4), 042303 (2015)
Wendenius, C., Kuehn, E.: Quantum-triplet-loss, July 2022. https://doi.org/10.5281/zenodo.6786443
Xuan, H., Stylianou, A., Liu, X., Pless, R.: Hard negative examples are hard, but useful. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 126–142. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58568-6_8
Yu, B., Liu, T., Gong, M., Ding, C., Tao, D.: Correcting the triplet selection bias for triplet loss. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 71–86. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01231-1_5
Acknowledgements
The authors acknowledge support by the state of Baden-Württemberg through bwHPC.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wendenius, C., Kuehn, E., Streit, A. (2023). Training Parameterized Quantum Circuits with Triplet Loss. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13717. Springer, Cham. https://doi.org/10.1007/978-3-031-26419-1_31
Download citation
DOI: https://doi.org/10.1007/978-3-031-26419-1_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-26418-4
Online ISBN: 978-3-031-26419-1
eBook Packages: Computer ScienceComputer Science (R0)