Preprint

PUBDB-2025-00141

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Modified conjugate quantum natural gradient

Halla, M. (Corresponding author)DESY*

2025

This record in other databases:  

Report No.: arXiv:2501.05847

Abstract: The efficient optimization of variational quantum algorithms (VQAs) is critical for their successful application in quantum computing. The Quantum Natural Gradient (QNG) method, which leverages the geometry of quantum state space, has demonstrated improved convergence compared to standard gradient descent (Stokes et al. in Quantum 4:269, 2020). In this work, we introduce the Modified Conjugate Quantum Natural Gradient (CQNG), an optimization algorithm that integrates QNG with principles from the nonlinear conjugate-gradient method. Unlike QNG, which employs a fixed learning rate, CQNG dynamically adjusts its hyperparameters at each step, enhancing both efficiency and flexibility. Numerical simulations show that CQNG achieves faster convergence and reduces quantum-resource requirements compared to QNG across various optimization scenarios, even when strict conjugacy conditions are not fully satisfied—hence the term “Modified Conjugate.” These results highlight CQNG as a promising optimization technique for improving the performance of VQAs.

Keyword(s): Quantum Physics (quant-ph) ; FOS: Physical sciences

---

Contributing Institute(s):

1. Centre f. Quantum Techno. a. Application (CQTA)

Research Program(s):

1. 611 - Fundamental Particles and Forces (POF4-611) (POF4-611)

Experiment(s):

1. No specific instrument

Appears in the scientific report 2025

---

Database coverage:
Published

---

---

Linked articles:

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Journal Article Halla, M. (Corresponding author)DESY*
Modified Conjugate Quantum Natural Gradient
EPJ Quantum Technology 12, 123 (2025) [10.1140/epjqt/s40507-025-00432-4]2025     Files BibTeX | EndNote: XML, Text | RIS

---