Entanglement Boosts Machine Learning Of Quantum Systems
Researchers are increasingly focused on accurately approximating complex Hamiltonian dynamics with simplified, effective models, a crucial challenge at the intersection...
quantum
Quantum Control Leap Simplifies Tasks From Machine Learning To Error Correction
Researchers are increasingly focused on extending control from completely positive and trace-preserving (CPTP) maps to Hermitian-preserving and trace-preserving (HPTP) maps,...
Quantum Machine Learning Achieves Scalability With 50 Qubit Shallow-Circuit Supervision
Quantum computing continues to seek practical applications in machine learning, but challenges remain in efficiently loading classical data and training...
Quantum Computing Advances Universal Functional Derivation With Neural Network And Embedding Methods
The challenge of accurately simulating the behaviour of interacting particles lies at the heart of many problems in physics and...
Enhancing Hybrid Methods Optimizes Parameterized Quantum Circuits For Variational Algorithms And Machine Learning
Parameterized quantum circuits underpin many promising applications of near-term quantum computers, and optimising these circuits is a central challenge in...
Data-efficient Quantum Noise Modeling Via Machine Learning Enables Accurate Noise Models From Existing Data
Predicting and mitigating errors represents a major challenge in harnessing the power of quantum computers, and accurate noise models are...
