Mitigating Barren Plateaus with Transfer-learning-inspired Parameter Initializations

Abstract

Variational quantum algorithms (VQAs) are widely applied in the noisy intermediate-scale quantum era and are expected to demonstrate quantum advantage. However, training VQAs faces difficulties, one of which is the so-called barren plateaus (BP) phenomenon, where gradients of cost functions vanish exponentially with the number of qubits. In this paper, inspired by transfer learning, where knowledge of pre-solved tasks could be further used in a different but related work with training efficiency improved, we report a parameter initialization method to mitigate BP. In the method, a small-sized task is solved with a VQA. Then the ansatz and its optimum parameters are transferred to tasks with larger sizes. Numerical simulations show that this method could mitigate BP and improve training efficiency. A brief discussion on how this method can work well is also provided. This work provides a reference for mitigating BP, and therefore, VQAs could be applied to more practical problems.

12 pages, 4 figures, 1 tables

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• Quantum algorithms for scientific computing
• Quantum Neural Estimation of Entropies
• Image Classification with Rotation-Invariant Variational Quantum Circuits
• Quantum neural compressive sensing for ghost imaging
• Transfer learning from Hermitian to non-Hermitian quantum many-body physics
• High-fidelity dimer excitations using quantum hardware
• Variational quantum algorithm based on Lagrange polynomial encoding to solve differential equations
• A variational quantum algorithm for entanglement quantification
• HQNET: Harnessing Quantum Noise for Effective Training of Quantum Neural Networks in NISQ Era
• Data-driven Quantum Dynamical Embedding Method for Long-term Prediction on Near-term Quantum Computers