Quantum Machine Learning

Noisy intermediate-scale quantum (NISQ) algorithms

A universal fault-tolerant quantum computer that can solve efficiently problems such as integer factorization and unstructured database search requires millions of qubits with low error rates and long coherence times. While the experimental …

Meta-Variational Quantum Eigensolver (Meta-VQE): Learning energy profiles of parameterized Hamiltonians for quantum simulation

We present the meta-variational quantum eigensolver (VQE), an algorithm capable of learning the ground-state energy profile of a parameterized Hamiltonian. If the meta-VQE is trained with a few data points, it delivers an initial circuit …

Tequila: A platform for rapid development of quantum algorithms

Variational quantum algorithms are currently the most promising class of algorithms on near-term quantum computers. In contrast to classical algorithms, there are almost no standardized methods yet, and the field continues to evolve rapidly. Similar …

Data re-uploading for a universal quantum classifier

A single qubit provides sufficient computational capabilities to construct a universal quantum classifier when assisted with a classical subroutine. This fact may be surprising since a single qubit only offers a simple superposition of two states and …