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Computing the Many-Body Green's Function with Adaptive Variational Quantum Dynamics.

Niladri Gomes1, David B Williams-Young1, Wibe A de Jong1

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|May 25, 2023
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This summary is machine-generated.

We developed an adaptive quantum dynamics simulation to compute the real-time Green's function. This method generates compact quantum states and improves spectral feature convergence for noisy quantum hardware.

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Area of Science:

  • Quantum Computing
  • Quantum Many-Body Physics
  • Computational Chemistry

Background:

  • Calculating the real-time Green's function is crucial for understanding quantum many-body systems.
  • Existing methods face challenges with computational complexity and scalability.

Purpose of the Study:

  • To present a novel adaptive variational quantum dynamics simulation approach for computing the many-body real-time Green's function.
  • To demonstrate the feasibility and effectiveness of this method on real quantum hardware.

Main Methods:

  • Utilizing an adaptive variational quantum dynamics simulation.
  • Expressing the quantum state as a linear combination of state vectors.
  • Employing Padé approximants for spectral feature convergence.
  • Implementing an error mitigation strategy with a resolution-enhancing method.

Main Results:

  • Successfully computed the many-body real-time Green's function using the adaptive variational quantum dynamics approach.
  • Demonstrated the evaluation on an IBM Q quantum computer.
  • Applied a resolution-enhancing method to noisy quantum hardware data, improving spectral features.

Conclusions:

  • The adaptive variational quantum dynamics simulation is a promising method for calculating real-time Green's functions.
  • The developed error mitigation strategy effectively addresses noise in quantum hardware data.
  • This approach advances the application of quantum computing to complex many-body problems.