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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Classical Surrogates for Quantum Learning Models.

Franz J Schreiber1, Jens Eisert1,2,3, Johannes Jakob Meyer1

  • 1Dahlem Center for Complex Quantum Systems, Freie Universität Berlin, 14195 Berlin, Germany.

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|September 22, 2023
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This summary is machine-generated.

Researchers explored quantum machine learning models, finding that classical surrogates can replicate their performance. This suggests current quantum models may not offer advantages in speed or training, highlighting the need for further research into quantum inductive biases.

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Area of Science:

  • Quantum Information Science
  • Quantum Machine Learning

Background:

  • Noisy intermediate-scale quantum (NISQ) computers are driving the search for practical applications.
  • Quantum machine learning, particularly variational quantum learning models, is a promising area for NISQ advantage.

Purpose of the Study:

  • To introduce and analyze the concept of a classical surrogate for trained quantum learning models.
  • To evaluate the performance and trainability of quantum models against their classical surrogates.

Main Methods:

  • Developed a method to derive classical surrogate models from trained quantum learning models.
  • Conducted numerical experiments to compare quantum models (specifically reuploading models) with their classical surrogates.
  • Optimized the Ansatz of classical surrogates to serve as a benchmark for quantum models.

Main Results:

  • Demonstrated that large classes of reuploading quantum models possess a classical surrogate.
  • Numerical experiments showed no performance or trainability advantage for these quantum models over their classical counterparts.
  • Classical surrogates can be efficiently obtained and perform inference classically, enhancing applicability but challenging quantum advantage claims.

Conclusions:

  • The existence of classical surrogates for many quantum learning models raises questions about inherent quantum advantages.
  • Current quantum models analyzed do not outperform classical surrogates in performance or trainability.
  • Generalization capability remains a potential area for quantum advantage, underscoring the need to understand quantum inductive biases.