SciPost Physics Lecture Notes

SciPost Phys. Lect. Notes 61 (2022) · published 17 August 2022

• doi: 10.21468/SciPostPhysLectNotes.61
• pdf
• BiBTeX
• RIS
• Submissions/Reports
• 

Abstract

Machine learning has achieved dramatic success over the past decade, with applications ranging from face recognition to natural language processing. Meanwhile, rapid progress has been made in the field of quantum computation including developing both powerful quantum algorithms and advanced quantum devices. The interplay between machine learning and quantum physics holds the intriguing potential for bringing practical applications to the modern society. Here, we focus on quantum neural networks in the form of parameterized quantum circuits. We will mainly discuss different structures and encoding strategies of quantum neural networks for supervised learning tasks, and benchmark their performance utilizing Yao.jl, a quantum simulation package written in Julia Language. The codes are efficient, aiming to provide convenience for beginners in scientific works such as developing powerful variational quantum learning models and assisting the corresponding experimental demonstrations.

• Fauzi et al.,
  , 152 (2022) [Crossref]
• Chen et al., Quantum approximation of normalized Schatten norms and applications to learning
  Phys. Rev. A 106, 052409 (2022) [Crossref]
• Tian et al., Recent Advances for Quantum Neural Networks in Generative Learning
  IEEE Trans. Pattern Anal. Mach. Intell., 1 (2023) [Crossref]
• Khemapatapan et al.,
  , 01 (2023) [Crossref]
• West et al., Towards quantum enhanced adversarial robustness in machine learning
  Nat Mach Intell 5, 581 (2023) [Crossref]
• Song et al., A quantum federated learning framework for classical clients
  Sci. China Phys. Mech. Astron. 67, 250311 (2024) [Crossref]
• Khemapatapan et al., Forecasting the Weather behind Pa Sak Jolasid Dam using Quantum Machine Learning
  Adv. sci. technol. eng. syst. j. 8, 54 (2023) [Crossref]
• Qiu, Universal adversarial perturbations for multiple classification tasks with quantum classifiers
  Mach. Learn.: Sci. Technol. 4, 045009 (2023) [Crossref]
• Kukliansky et al., Network Anomaly Detection Using Quantum Neural Networks on Noisy Quantum Computers
  IEEE Trans. Quantum Eng. 5, 1 (2024) [Crossref]
• Saxena et al.,
  , 1 (2022) [Crossref]