Quantum neural network with ensemble learning to mitigate barren plateaus and cost function concentration
This addresses a key bottleneck for quantum machine learning researchers, though it appears incremental as it builds on existing ensemble methods.
The paper tackled the vanishing gradient and cost function concentration problems in quantum neural networks by introducing an ensemble learning approach using multiple shallow quantum circuits, which showed potential advantages over conventional single deep circuits in a classification task.
The rapid development of quantum computers promises transformative impacts across diverse fields of science and technology. Quantum neural networks (QNNs), as a forefront application, hold substantial potential. Despite the multitude of proposed models in the literature, persistent challenges, notably the vanishing gradient (VG) and cost function concentration (CFC) problems, impede their widespread success. In this study, we introduce a novel approach to quantum neural network construction, specifically addressing the issues of VG and CFC. Our methodology employs ensemble learning, advocating for the simultaneous deployment of multiple quantum circuits with a depth equal to \(1\), a departure from the conventional use of a single quantum circuit with depth \(L\). We assess the efficacy of our proposed model through a comparative analysis with a conventionally constructed QNN. The evaluation unfolds in the context of a classification problem, yielding valuable insights into the potential advantages of our innovative approach.