Although some efforts based on syntactic analysis have opened the door to research in Quantum NLP (QNLP), limitations such as heavy syntactic preprocessing and syntax-dependent network architecture make them impracticable on larger and real-world data sets.
In this work, we solve the problem on the minimum size of sufficient quantum datasets for learning a unitary transformation exactly, which reveals the power and limitation of quantum data.
Here we introduce LOCCNet, a machine learning framework facilitating protocol design and optimization for distributed quantum information processing tasks.
Firstly, it decomposes a positive map into a combination of quantum operations implementable on near-term quantum devices.
Quantum Physics Strongly Correlated Electrons
A novel variational algorithm for trace distance estimation is then derived from this technique, with the assistance of a single ancillary qubit.
Quantum Physics Information Theory Mathematical Physics Information Theory Mathematical Physics Optimization and Control