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Whether solid, liquid, or gas, a substance's state depends on the order and arrangement of its particles (atoms, molecules, or ions). Particles in the solid pack closely together, generally in a pattern. The particles vibrate about their fixed positions but do not move or squeeze past their neighbors. In liquids, although the particles are closely spaced, they are randomly arranged. The position of the particles are not fixed—that is, they are free to move past their neighbors to...
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Heating a crystalline solid increases the average energy of its atoms, molecules, or ions, and the solid gets hotter. At some point, the added energy becomes large enough to partially overcome the forces holding the molecules or ions of the solid in their fixed positions, and the solid begins the process of transitioning to the liquid state or melting. At this point, the temperature of the solid stops rising, despite the continual input of heat, and it remains constant until all of the solid is...
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Dynamical Phase Transitions in Quantum Reservoir Computing.

Rodrigo Martínez-Peña1, Gian Luca Giorgi1, Johannes Nokkala1

  • 1Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC, UIB-CSIC), Campus Universitat de les Illes Balears, E-07122 Palma de Mallorca, Spain.

Physical Review Letters
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Summary
This summary is machine-generated.

Quantum reservoir computing performance improves in the thermal phase, especially at the thermalization transition. This finding highlights the role of dynamical phases in quantum information processing.

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Area of Science:

  • Quantum physics
  • Quantum computing
  • Information theory

Background:

  • Closed quantum systems display diverse dynamical regimes, including many-body localization and thermalization.
  • These regimes influence how information spreads and is processed within quantum systems.
  • Quantum reservoir computing (QRC) is an emerging paradigm that uses dynamical systems for complex computational tasks.

Purpose of the Study:

  • To investigate the impact of different dynamical phases on quantum reservoir computing performance.
  • To determine if specific dynamical regimes are better suited for QRC tasks.
  • To identify optimal conditions for information processing in quantum spin networks.

Main Methods:

  • Studied the behavior of quantum spin networks under various dynamical conditions.
  • Analyzed the performance of quantum reservoir computing tasks within different dynamical phases.
  • Focused on the thermalization transition as a key point of interest.

Main Results:

  • The thermal phase is well-suited for the demands of quantum reservoir computing.
  • Performance in QRC tasks showed a notable increase at the thermalization transition.
  • Identified specific dynamical properties that enhance information processing capabilities.

Conclusions:

  • The thermal dynamical phase is advantageous for quantum reservoir computing.
  • Optimizing systems near the thermalization transition can boost computational performance.
  • Understanding these physical mechanisms is crucial for advancing quantum computing hardware and theory.