Quantum Time Dynamics Mediated by the Yang-Baxter Equation and Artificial Neural Networks

Area of Science:

• Quantum Information Science
• Computational Physics

Background:

• Quantum computing promises significant advancements but is hindered by inherent errors.
• Traditional quantum error mitigation techniques are often computationally demanding.

Purpose of the Study:

• To develop a novel, computationally efficient method for quantum error mitigation.
• To enhance the accuracy and fidelity of quantum simulations, especially on NISQ devices.

Main Methods:

• Combining artificial neural networks (ANNs) for noise mitigation with the Yang-Baxter equation (YBE) for controlled noisy data generation.
• Utilizing YBE to preserve quantum correlations and symmetries for circuit compression and scalability.
• Training ANN models on partial quantum simulation data for error correction.

Main Results:

• Successfully reduced noise in quantum simulations, leading to improved accuracy.
• Demonstrated effective error mitigation in time-evolving quantum states using ANNs.
• Validated the approach on real quantum devices through simulations of the Heisenberg XY Hamiltonian.

Conclusions:

• The proposed artificial error mitigation framework offers a scalable solution for enhancing quantum computation fidelity.
• This method is particularly beneficial for noisy intermediate-scale quantum (NISQ) systems.
• The integration of ANNs and YBE provides a powerful tool for advancing practical quantum computing.