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October 2024

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Seminar: Adiabatic training for Variational Quantum Algorithms
by Daniel Manzano 22 Oct '24

22 Oct '24

The Quantum Thermodynamics and Quantum Computing Group (QTCG) of the University of Granada invites all the university members to attend the following seminar. Date and time: Friday October 25, 11am. Place: Facultad de Ciencias. Laboratorio de Física Computacional (Edificio de Física, planta baja, junto al péndulo). Title: Adiabatic training for Variational Quantum Algorithms. Speaker: Ernesto Acosta. PhD Student UGR. Supervisors: Carlos Cano Gutiérrez, Guillermo Botella Juan 25-10-2024 Abstract: On this talk we present a new hybrid Quantum Machine Learning model (QML) composed of three elements: a classical computer in charge of the data preparation and interpretation; a Gate-based Quantum Computer running the Variational Quantum Algorithm (VQA) representing a Quantum Neural Network (QNN); and an adiabatic Quantum Computer where the optimization function is executed to find the best parameters for the VQA. As of the present moment the majority of VQAs are being trained using gradient-based classical optimizers having to deal with the barren-plateau effect. Some gradient-free classical approaches such as Evolutionary Algorithms have also been proposed to overcome this effect. However, adiabatic quantum models have not been defined to train VQAs. A quick review of Artificial Neural Networks and Variational Quantum Algorithms concepts is presented, along with the description of barren-plateau effect in relation to vanishing gradients. Then we will describe the proposed adiabatic training model comparing the obtained results against the classical gradient-based algorithms, showing the feasibility of integration for gate-based and adiabatic quantum computing models. We will end up with some highlights on the current phase of research towards iterative and multithreaded adiabatic training. Keywords: Quantum Machine Learning · Variational Quantum Algorithms · Quantum Annealing References: Acampora, G., Chiatto, A., Vitiello, A., A Comparison of Evolutionary Algorithms for Training Variational Quantum Classifiers. IEEE Congress on Evolutionary Computation (CEC) (2023) Cerezo, M., Arrasmith, A., Babbush, R., Benjamin, S., Endo, S., Fujii, K., McClean, J., Mitarai, K., Yuan, X., Cincio, L., Coles, P., Variational quantum algorithms. Nature Reviews Physics, 3(9), 625–644 (2021) With the best regards, DMJ. --------------------------------------------------------------------------------- Daniel Manzano Quantum Thermodynamics and Quantum Computation Group University of Granada Facultad de Ciencias, Av. Fuentenueva s/n Granada 18071, Spain Phone: +34 958241000 Ext: 20569 https://ic1.ugr.es/members/dmanzano/

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