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

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.