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Production and Targeting of Monovalent Quantum Dots
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Predicting Open Quantum Dynamics with Data-Informed Quantum-Classical Dynamics.

Pinchen Xie1, Ke Wang2, Anupam Mitra1

  • 1Lawrence Berkeley National Laboratory, Applied Mathematics and Computational Research Division, Berkeley, California 94720, USA.

Physical Review Letters
|January 26, 2026
PubMed
Summary
This summary is machine-generated.

We developed a new data-informed quantum-classical dynamics (DIQCD) method to predict open quantum system evolution. DIQCD accurately models complex quantum behaviors using experimental data, showing promise for quantum devices and materials science.

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Area of Science:

  • Quantum Physics
  • Computational Chemistry
  • Materials Science

Background:

  • Predicting the dynamics of open quantum systems is challenging due to environmental interactions.
  • Existing methods often struggle with sparse, noisy data from real-world quantum experiments.

Purpose of the Study:

  • Introduce a novel data-informed quantum-classical dynamics (DIQCD) approach.
  • Develop a flexible, time-dependent Hamiltonian for accurate quantum system modeling.
  • Validate DIQCD's performance on experimental and simulated quantum devices.

Main Methods:

  • Utilized a Lindblad equation framework for the equation of motion.
  • Optimized a time-dependent Hamiltonian to fit sparse and noisy observational data.
  • Applied DIQCD to predict entanglement dynamics and carrier mobility.

Main Results:

  • DIQCD accurately predicted entanglement dynamics of ultracold calcium fluoride molecules.
  • The method successfully forecasted carrier mobility in rubrene organic semiconductors.
  • Achieved accuracy comparable to established numerical techniques for quantum system simulation.

Conclusions:

  • DIQCD offers a powerful, data-driven approach for understanding open quantum systems.
  • The method demonstrates broad applicability across quantum computing and materials science.
  • DIQCD enhances the predictive power of quantum simulations using experimental data.