A universal fault-tolerant quantum computer that can efficiently solve problems such as
integer factorization and unstructured database search requires millions of qubits with low …

As we begin to reach the limits of classical computing, quantum computing has emerged as
a technology that has captured the imagination of the scientific world. While for many years …

Machine learning (ML) has emerged as a formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation of …

We propose a hybrid classical-quantum digitized counterdiabatic algorithm to tackle the
protein-folding problem on a tetrahedral lattice. Digitized counterdiabatic quantum …

Variational quantum algorithms that are used for quantum machine learning rely on the
ability to automatically differentiate parametrized quantum circuits with respect to underlying …

We propose to directly and quantum-coherently couple a superconducting transmon qubit to
magnons—the quanta of the collective spin excitations, in a nearby magnetic particle. The …

Classical simulation of quantum dynamics plays an important role in our understanding of
quantum complexity, and in the development of quantum technologies. Compared to other …

Quantum error mitigation (QEM) is vital for noisy intermediate-scale quantum (NISQ)
devices. While most conventional QEM schemes assume discrete gate-based circuits with …

The central challenge of quantum computing is implementing high-fidelity quantum gates at
scale. However, many existing approaches to qubit control suffer from a scale-performance …

Quantum reservoir computing (QRC) has been proposed as a paradigm for performing
machine learning with quantum processors where the training takes place in the classical …