• Home
  • News
  • Technology
  • Research
  • Teaching
  • Business
  • Jobs
  • Home
  • News
  • Technology
  • Research
  • Teaching
  • Business
  • Jobs
Contact
  • Deutsch
  • English

  • Home
  • News
  • Technology
  • Research
  • Teaching
  • Business
  • Jobs
Contact
  • Deutsch
  • English

Analyzing the Parameter Adaption of Transfer Learning in Variational Quantum Eigensolvers

Analyzing the Parameter Adaption of Transfer Learning in Variational Quantum Eigensolvers

Abstract:

With the introduction of publicly available, yet noisy, Quantum Processors, many machine learning (ML) approaches have been proposed to make the most of the new capabilities. Peruzzo et al. proposed a hybrid algorithm, which implements a variational quantum eigensolver (VQE) to find the ground state of a Hamiltonian. While initially used in the field of quantum chemistry, VQEs can be used to solve a variety of optimization problems, such as finding a max-cut in a graph. As training is computationally expensive, transfer learning approaches have been proposed to reduce training time for similar problem instances. Rohe et al. introduce a VQE algorithm utilizing TL to speed up convergence in the max-cut problem. It was shown that TL is able to achieve convergence significantly faster in the early optimization phase, although training without TL yields slightly better results over time. However, when training time is drastically reduced, TL is able to produce good results while drastically reducing the computational cost of training. Furthermore, it was shown that the similarity of the optimal solution correlated positively with the success of TL. The similarity was measured by calculating the minimal hamming distance (HD) between the VQE’s solution of the source graph and the optimal solutions of the target graph. This thesis will be based primarily on the work done by Rohe et al. Specifically, the aim of the thesis is to analyze the quality of the parameter transfer to solve max-cut graph problems. Thus, instead of trying to demonstrate the applicability of TL for the VQE, it will be investigated where TL causes the algorithm to over- or under-adapt and how these results come about. The source-and target-graphs are sampled form the publicly available California street network as well as Facebook social circle data. Here the source graph will be utilized to train parameters which are to be transferred to initialize the training of the target graph. To evaluate the similarity of the source- and target-graph applied to the max-cut problem, the optimal max-cut solutions are calculated via brute force. Afterward the minimal HD between optimal solutions of the source and target-graphs are calculated. As TL might not always find one of the optimal solutions, the HD between the source solution and the target solution found through TL are calculated. This will provide information about whether TL caused the VQE to over- or under-adapt. As the quality of trained solutions appears to deteriorate as the HD between source- and target-graph solutions increase, it is of high interest to find ways to handle these kinds of problems. A possible explanation for over- or under-adaption is that the pre-trained parameters trap the VQE in a local optimum, inhibiting further exploration of the solution landscape. Through the analysis of the influence TL has on the training process of the VQE as well as under- and over-adaption, this thesis aims to better evaluate the role and quality of parameter transfer in the NISQ era.

Author:

Julio Amaru Nicolas Brocca Alvarado

Advisors:

Tobias Rohe, Sebastian Woelkert, Thomas Gabor, Claudia Linnhoff-Popien


Student Thesis | Published April 2025 | Copyright © QAR-Lab
Direct Inquiries to this work to the Advisors



QAR-Lab – Quantum Applications and Research Laboratory
Ludwig-Maximilians-Universität München
Oettingenstraße 67
80538 Munich
Phone: +49 89 2180-9153
E-mail: qar-lab@mobile.ifi.lmu.de

© Copyright 2025

General

Team
Contact
Legal notice

Social Media

Twitter Linkedin Github

Language

  • Deutsch
  • English
Cookie-Zustimmung verwalten
Wir verwenden Cookies, um unsere Website und unseren Service zu optimieren.
Funktional Always active
Die technische Speicherung oder der Zugang ist unbedingt erforderlich für den rechtmäßigen Zweck, die Nutzung eines bestimmten Dienstes zu ermöglichen, der vom Teilnehmer oder Nutzer ausdrücklich gewünscht wird, oder für den alleinigen Zweck, die Übertragung einer Nachricht über ein elektronisches Kommunikationsnetz durchzuführen.
Preferences
The technical storage or access is necessary for the legitimate purpose of storing preferences that are not requested by the subscriber or user.
Statistiken
Die technische Speicherung oder der Zugriff, der ausschließlich zu statistischen Zwecken erfolgt. The technical storage or access that is used exclusively for anonymous statistical purposes. Without a subpoena, voluntary compliance on the part of your Internet Service Provider, or additional records from a third party, information stored or retrieved for this purpose alone cannot usually be used to identify you.
Marketing
Die technische Speicherung oder der Zugriff ist erforderlich, um Nutzerprofile zu erstellen, um Werbung zu versenden oder um den Nutzer auf einer Website oder über mehrere Websites hinweg zu ähnlichen Marketingzwecken zu verfolgen.
Manage options Manage services Manage {vendor_count} vendors Read more about these purposes
Einstellungen anzeigen
{title} {title} {title}