Abstract: Circuit compression is a key requirement for near-term quantum computing, yet the factors that govern stability under gate removal are not fully understood. We study this problem via a large-scale numerical analysis of 300 structurally uniform circuits across 10, 12, and 14 qubits. Despite identical macroscopic resources, each ensemble separates into two stability classes […]
Abstract: Principal component analysis is commonly used for dimensionality reduction, feature extraction, denoising, and visualization. The most commonly used principal component analysis method is based upon optimization of the L2-norm; however, the L2-norm is known to exaggerate the contribution of errors and outliers. When optimizing over the L1-norm, the components generated are known to exhibit […]
Abstract: Federated learning has emerged as a powerful paradigm for decentralized model training, ensuring privacy preservation by allowing clients to collaboratively learn a shared model without exchanging raw data. Quantum federated learning (QFL) extends this approach by leveraging quantum computing to enhance computational efficiency and security. However, existing QFL frameworks face challenges in handling temporal […]