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Numerically exact quantum dynamics with tensor networks: Predicting the decoherence of interacting spin systems.
Tianchu Li1, Pranay Venkatesh1, Nanako Shitara1,2
1Department of Chemistry, University of Colorado Boulder, Boulder, Colorado 80309, USA.
The Journal of Chemical Physics
|March 4, 2026
Summary
We developed a new numerical method to accurately predict quantum dynamics in solid-state and molecular systems. This advance is crucial for understanding and controlling decoherence, paving the way for improved quantum technologies like qubits and sensors.
Area of Science:
- Quantum Information Science
- Condensed Matter Physics
- Quantum Computing
Background:
- Predicting quantum dynamics is essential for advancing quantum technologies.
- Understanding decoherence mechanisms is key to designing better qubits, sensors, and memories.
Purpose of the Study:
- To introduce a numerically exact and scalable method for predicting quantum dynamics.
- To accurately model coherence and population dynamics in spin networks.
Main Methods:
- Leveraging a matrix product state representation.
- Developing a scalable numerical approach for quantum dynamics simulation.
Main Results:
- Accurate prediction of coherence and population dynamics across various parameter regimes.
- Successful modeling of spin networks in nuclear spin sensors, solid-state qubits, and molecular magnets.
- Prediction of spin dynamics under pulsed light, relevant for quantum sensing.
Conclusions:
- The developed method provides reliable results for moderately sized spin platforms.
- This approach can guide the development of approximate quantum dynamics methods.
- Enables principled inquiry into decoherence mechanisms for quantum technology development.
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