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Classical criticality via quantum annealing.

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Quantum annealers can simulate complex magnetic materials and statistical physics models, overcoming limitations of classical methods. This research shows their potential for studying phase transitions and critical phenomena accurately.

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Area of Science:

  • Quantum Computing
  • Statistical Physics
  • Condensed Matter Physics

Background:

  • Quantum annealing offers an alternative to classical Monte Carlo methods for simulating physical systems.
  • Classical algorithms often face limitations like critical slowing down when studying phase transitions.

Purpose of the Study:

  • To demonstrate the capability of quantum annealers in simulating statistical physics models and magnetic materials.
  • To investigate the piled-up dominoes model and its phase diagram using quantum annealing.
  • To explore the application of finite-size scaling techniques on quantum hardware.

Main Methods:

  • Utilizing a quantum annealer to simulate the piled-up dominoes model.
  • Employing finite-size scaling and Binder cumulants for analyzing critical exponents.
  • Controlling temperature by tuning the Hamiltonian's energy scale on the quantum annealer.

Main Results:

  • Accurate reproduction of phase diagrams and simulation of critical phenomena.
  • Overcoming the critical slowing down issue inherent in classical algorithms.
  • Successful application of sophisticated finite-size scaling techniques via quantum hardware.

Conclusions:

  • Quantum annealers are robust simulators for statistical physics, offering a new approach to study phase transitions.
  • This method provides systematic temperature control by adjusting Hamiltonian parameters, not physical hardware temperature.
  • The study validates quantum annealers for advanced statistical mechanics simulations and critical phenomena research.