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An ensemble variational quantum algorithm for non-Markovian quantum dynamics.

Peter L Walters1, Joachim Tsakanikas2,3, Fei Wang1,4

  • 1Department of Chemistry and Biochemistry, George Mason University, Fairfax, Virginia 22030, USA. fwang22@gmu.edu.

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|July 22, 2024
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Summary
This summary is machine-generated.

We developed a quantum algorithm to simulate complex non-Markovian quantum dynamics on current quantum computers. This approach tackles simulations challenging for classical systems, enabling new insights into condensed phase environments.

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Area of Science:

  • Quantum physics
  • Computational chemistry
  • Condensed matter physics

Background:

  • Non-Markovian quantum dynamics are crucial for understanding physical and chemical processes in condensed phases.
  • Simulating these dynamics is computationally intensive for classical computers.
  • Noisy Intermediate-Scale Quantum (NISQ) devices offer potential for tackling such complex problems.

Purpose of the Study:

  • To develop a variational quantum algorithm for simulating non-Markovian quantum dynamics on NISQ devices.
  • To enable efficient simulation of systems where quantum effects are significant and memory-dependent.

Main Methods:

  • A variational quantum algorithm was designed using a model Hamiltonian of a quantum system coupled to a harmonic bath.
  • Auxiliary variables from bath trajectories were introduced to capture non-Markovian characteristics.
  • Monte Carlo sampling of bath degrees of freedom was employed to simulate finite temperature dynamics.

Main Results:

  • The algorithm was successfully validated on a quantum simulator.
  • The algorithm demonstrated practical performance on an IBM quantum device.
  • The simulation framework accurately captured non-Markovian dynamics.

Conclusions:

  • The developed variational quantum algorithm is effective for simulating non-Markovian quantum dynamics on NISQ hardware.
  • The framework is adaptable to various systems, including those with anharmonic baths and non-linear couplings.
  • This approach is promising for simulating spin chain dynamics in dissipative environments.