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Related Experiment Video

Updated: Sep 13, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

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Introducing the kernel descent optimizer for variational quantum algorithms.

Lars Simon1, Holger Eble1, Manuel Radons2

  • 1Bundesdruckerei GmbH, Kommandantenstraße 18, 10969, Berlin, Germany.

Scientific Reports
|August 2, 2025
PubMed
Summary
This summary is machine-generated.

Kernel descent is a new algorithm for optimizing variational quantum algorithms on near-term quantum devices. It outperforms gradient descent and quantum analytic descent in key scenarios.

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Area of Science:

  • Quantum Computing
  • Optimization Algorithms

Background:

  • Variational quantum algorithms are a promising approach for achieving quantum advantage on current noisy intermediate-scale quantum (NISQ) devices.
  • Efficiently minimizing the objective functions of these algorithms is crucial for their practical application.

Purpose of the Study:

  • Introduce kernel descent, a novel algorithm for minimizing functions in variational quantum algorithms.
  • Compare the performance of kernel descent against existing optimization methods.
  • Demonstrate the effectiveness of kernel descent through extensive experiments.

Main Methods:

  • Kernel descent iteratively computes classical local approximations of the objective function.
  • It utilizes reproducing kernel Hilbert space techniques to construct these local approximations.
  • Classical optimization steps are performed on these approximations.

Main Results:

  • Kernel descent demonstrates superior performance compared to gradient descent.
  • Kernel descent outperforms quantum analytic descent in specific scenarios.
  • Extensive experiments validate the effectiveness of the kernel descent algorithm.

Conclusions:

  • Kernel descent offers an effective new method for optimizing variational quantum algorithms.
  • The use of reproducing kernel Hilbert space techniques provides a distinct advantage.
  • This algorithm shows promise for advancing the application of NISQ devices.