Expressibility-induced concentration of quantum neural tangent kernels
Quantum tangent kernel methods provide an efficient approach to analyzing the
performance of quantum machine learning models in the infinite-width limit, which is of …
performance of quantum machine learning models in the infinite-width limit, which is of …
Generating multipartite nonlocality to benchmark quantum computers
We show that quantum computers can be used for producing large n-partite nonlocality,
thereby providing a method to benchmark them. The main challenges to overcome are as …
thereby providing a method to benchmark them. The main challenges to overcome are as …
Entanglement-induced provable and robust quantum learning advantages
Quantum computing holds the unparalleled potentials to enhance, speed up or innovate
machine learning. However, an unambiguous demonstration of quantum learning …
machine learning. However, an unambiguous demonstration of quantum learning …
Benchmarking Variational Quantum Eigensolvers for Entanglement Detection in Many-Body Hamiltonian Ground States
Variational quantum algorithms (VQAs) have emerged in recent years as a promise to obtain
quantum advantage. These task-oriented algorithms work in a hybrid loop combining a …
quantum advantage. These task-oriented algorithms work in a hybrid loop combining a …
Improved Nonlocality Certification via Bouncing between Bell Operators and Inequalities
Bell nonlocality is an intrinsic feature of quantum mechanics, which can be certified via the
violation of Bell inequalities. It is therefore a fundamental question to certify Bell nonlocality …
violation of Bell inequalities. It is therefore a fundamental question to certify Bell nonlocality …