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Quantum Annealing with Longitudinal Bias Fields.

Tobias Graß1

  • 1Joint Quantum Institute, NIST and University of Maryland, College Park, Maryland, 20742, USA and ICFO - Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, Av. Carl Friedrich Gauss 3, 08860 Castelldefels (Barcelona), Spain.

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Summary
This summary is machine-generated.

Inhomogeneous longitudinal magnetic fields can improve quantum annealing efficiency by guiding the process toward desired solutions or filtering unwanted states. This method enhances performance for difficult computational problems.

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Area of Science:

  • Quantum computing
  • Computational physics
  • Optimization algorithms

Background:

  • Quantum annealing is a metaheuristic optimization algorithm that uses quantum fluctuations to find the global minimum of a given objective function.
  • Adiabatic state preparation is a key mechanism in quantum annealing, aiming to evolve a quantum system into its ground state, which encodes the solution.
  • Standard quantum annealing can struggle with certain complex computational problems, necessitating enhanced strategies.

Purpose of the Study:

  • To investigate the use of inhomogeneous longitudinal magnetic fields to enhance the efficiency of quantum annealing.
  • To explore strategies for biasing annealing dynamics towards desired solutions or filtering unwanted states.
  • To assess the performance of these novel strategies for computationally challenging instances.

Main Methods:

  • Proposal and numerical simulation of inhomogeneous longitudinal magnetic fields applied during quantum annealing.
  • Iterative configuration of field strengths to bias annealing dynamics.
  • Application of fields as an 'antibias' to filter out undesired contributions to the final state.
  • Simulation of small instances of the exact cover problem to evaluate performance.

Main Results:

  • Inhomogeneous longitudinal magnetic fields can effectively bias annealing dynamics towards the target solution.
  • Suitable field configurations can be found iteratively, offering a practical approach to implementation.
  • The 'antibias' strategy demonstrates potential for filtering unwanted states, improving solution quality.
  • Simulations on exact cover problem instances show performance improvements compared to standard quantum annealing.

Conclusions:

  • Inhomogeneous longitudinal magnetic fields represent a promising technique for enhancing quantum annealing efficiency.
  • This strategy offers a flexible way to address difficult computational problems that are intractable for standard quantum annealing.
  • The proposed methods provide a valuable addition to the toolkit for quantum optimization.