Speaker
Description
Variational quantum eigensolvers (VQEs) provide a near-term approach to ground-state energy estimation, but their performance is hindered by nonconvex loss landscapes with local minima and saddle points. Although noise is usually viewed as detrimental, recent studies show that structured Pauli noise can instead regularize loss landscapes by suppressing high-frequency components. Motivated by this idea, we apply Pauli-noise annealing to VQE training. Numerical simulations show improved convergence reliability over noiseless and finite-shot baselines. We further study annealing schedules and extended noise-factor regimes, showing that stronger landscape deformation can enhance saddle-point escape. These results support adaptive noise annealing for reliable noise-assisted VQE.
| Topic | Hybrid quantum-classical approaches for learning and optimization |
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| Additional Topic | Quantum variational circuits and their applications |