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This study demonstrates an experimental quantum autoencoder that efficiently compresses quantum data, reducing qutrits to qubits with low error. This machine learning approach optimizes quantum resource usage with minimal prior information.

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Area of Science:

  • Quantum computing
  • Machine learning
  • Quantum information science

Background:

  • Efficient utilization of quantum resources is crucial due to their scarcity.
  • Quantum autoencoders offer a potential solution for reducing quantum memory requirements.
  • Autoencoders use machine learning to compress data into a lower-dimensional space.

Purpose of the Study:

  • To experimentally realize a quantum autoencoder for compressing quantum data.
  • To investigate the efficiency and accuracy of quantum data compression.
  • To assess the autoencoder's performance with minimal prior information and its robustness.

Main Methods:

  • Experimental realization of a quantum autoencoder.
  • Utilizing a classical optimization routine for learning data compression.
  • Testing compression of qutrits to qubits.

Main Results:

  • Achieved low error levels when compressing qutrits to qubits for datasets allowing lossless compression.
  • Demonstrated effective performance with minimal prior knowledge of the quantum data or system.
  • Showcased robustness against perturbations during the optimization process.

Conclusions:

  • The developed quantum autoencoder efficiently compresses quantum data, reducing memory needs.
  • The approach is practical, requiring minimal prior information and exhibiting resilience.
  • This work advances the efficient use of precious quantum resources.