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Preparing spin squeezed states via adaptive genetic algorithm.

Y M Zhao1, L B Chen1, W Z Zhang2

  • 1Qingdao University of Technology, School of Physics, Qingdao 0532, Shandong, China.

Physical Review. E
|January 21, 2026
PubMed
Summary
This summary is machine-generated.

We developed an adaptive genetic algorithm (GA) to optimize quantum control sequences for creating nonclassical states. This method effectively prepares spin squeezed states, outperforming other techniques in noisy quantum systems.

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Area of Science:

  • Quantum Control
  • Quantum Information Science
  • Quantum Metrology

Background:

  • Generating nonclassical states is crucial for quantum technologies.
  • Controlling quantum systems in realistic, noisy environments presents significant challenges.

Purpose of the Study:

  • To introduce and validate an adaptive genetic algorithm (GA) for optimizing quantum control sequences.
  • To demonstrate the preparation of spin squeezed states in an open quantum system using the GA.
  • To assess the GA's performance against alternative control strategies.

Main Methods:

  • Employing an adaptive genetic algorithm (GA) with evolutionary strategies (crossover, mutation, elimination).
  • Iterative optimization of control sequences starting from a coherent spin state.
  • Simulating an open collective spin model with dissipation and dephasing.
  • Benchmarking against constant control and reinforcement learning methods.

Main Results:

  • Achieved high state-preparation fidelity (>0.99) for spin squeezed states.
  • Demonstrated robust and scalable performance even with dissipation and thermal noise.
  • Showcased a long time window for maintaining the spin squeezed state.
  • GA performance was competitive with and robust against other methods.

Conclusions:

  • The adaptive GA is a powerful and versatile tool for generating nonclassical quantum states.
  • The strategy is adaptable for optimizing metrologically relevant squeezing in noisy systems.
  • This work provides a foundation for using GA-like strategies in quantum system control.