Quantum Hardware

Quantum hardware research focuses on developing and improving physical quantum computers, primarily to enable faster computation than is possible classically. Current efforts concentrate on enhancing qubit coherence times, mitigating noise through error correction techniques (like measurement-free local error correction), and optimizing quantum circuit design using AI-driven methods (such as quantum architecture search and reinforcement learning). This field is crucial for advancing quantum machine learning, where hybrid classical-quantum models (incorporating variational quantum circuits and quantum neural networks) are being explored for applications in diverse areas like image processing, financial forecasting, and drug discovery.