Quantum Encoding

Quantum encoding focuses on efficiently representing classical data within quantum systems to leverage the power of quantum computation for various tasks. Current research emphasizes developing optimal encoding strategies for diverse applications, including machine learning (using quantum autoencoders and neural networks), quantum error correction, and statistical inference, often exploring the interplay between encoding methods and algorithm performance. These advancements are crucial for realizing the potential of quantum computing in fields like anomaly detection, genomics, and enhancing the robustness of quantum machine learning models against adversarial attacks.