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Error mitigation in brainbox quantum autoencoders.

Joséphine Pazem1,2, Mohammad H Ansari3,4

  • 1Institut für Theoretische Physik, Universität Innsbruck, Technikerstraße 25, A-6020, Innsbruck, Austria.

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Quantum autoencoders with specialized "brainboxes" can efficiently correct errors in noisy entangled states. Entanglement localization is key to this learning-based quantum error denoising.

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Area of Science:

  • Quantum computing
  • Quantum information science

Background:

  • Quantum hardware is susceptible to noise, which degrades multiqubit entangled states.
  • Quantum autoencoder circuits offer a method for error correction in these noisy states.

Purpose of the Study:

  • To investigate the use of enhanced bottleneck structures (brainboxes) in quantum autoencoders for faster and more efficient denoising.
  • To analyze the role of entanglement localization in the learning-based error correction process.

Main Methods:

  • Implementation of quantum autoencoder circuits with single-qubit and complex bottleneck structures ('brainboxes').
  • Introduction of varying noise channels to test denoising efficiency.
  • Analysis of Rényi entropy flow through the quantum networks.

Main Results:

  • Brainbox structures significantly improve the speed and efficiency of denoising under stronger noise conditions.
  • The choice of brainbox involves a trade-off between noise intensity and training complexity.
  • Rényi entropy flow analysis confirmed that entanglement localization is crucial for effective denoising.

Conclusions:

  • Advanced bottleneck designs ('brainboxes') enhance quantum autoencoder performance for error correction.
  • Entanglement localization is a fundamental mechanism enabling learning-based denoising in quantum systems.