One of the most promising suggested applications of quantum computing is solving
classically intractable chemistry problems. This may help to answer unresolved questions …

The power of quantum computing technologies is based on the fundamentals of quantum
mechanics, such as quantum superposition, quantum entanglement, or the no-cloning …

Under suitable assumptions, some recently developed quantum algorithms can estimate the
ground-state energy and prepare the ground state of a quantum Hamiltonian with near …

Many experimental proposals for noisy intermediate scale quantum devices involve training
a parameterized quantum circuit with a classical optimization loop. Such hybrid quantum …

With the increased focus on quantum circuit learning for near-term applications on quantum
devices, in conjunction with unique challenges presented by cost function landscapes of …

We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of
hybrid quantum-classical models for classical or quantum data. This framework offers high …

The calculation of excited state energies of electronic structure Hamiltonians has many
important applications, such as the calculation of optical spectra and reaction rates. While …

We develop a phase-estimation method with a distinct feature: its maximal run time (which
determines the circuit depth) is δ/ϵ, where ϵ is the target precision, and the preconstant δ …

Under suitable assumptions, the quantum-phase-estimation (QPE) algorithm is able to
achieve Heisenberg-limited precision scaling in estimating the ground-state energy …

Recently, increased computational power and data availability, as well as algorithmic
advances, have led machine learning (ML) techniques to impressive results in regression …