For quantum computers to successfully solve real-world problems, it is necessary to tackle
the challenge of noise: the errors that occur in elementary physical components due to …

Abstract Machine learning has achieved dramatic success in a broad spectrum of
applications. Its interplay with quantum physics may lead to unprecedented perspectives for …

Reducing measurement errors in multiqubit quantum devices is critical for performing any
quantum algorithm. Here we show how to mitigate measurement errors by a classical …

Measurements on current quantum processors are subject to hardware imperfections that
lead to readout errors. These errors manifest themselves as a bias in quantum expectation …

The past decade has witnessed significant advancements in quantum hardware,
encompassing improvements in speed, qubit quantity, and quantum volume—a metric …

Variational quantum algorithms (VQAs) are promising methods to demonstrate quantum
advantage on near-term devices as the required resources are divided between a quantum …

A frequent starting point of quantum computation platforms is the two-state quantum system,
ie, the qubit. However, in the context of integer optimization problems, relevant to scheduling …

Several tasks in quantum-information processing involve quantum learning. For example,
quantum sensing, quantum machine learning, and quantum-computer calibration involve …

Recent progress in noisy intermediate-scale quantum (NISQ) hardware shows that quantum
devices may be able to tackle complex problems even without error correction. However …

Ab initio molecular dynamics (AIMD) is a powerful tool to predict properties of molecular and
condensed matter systems. However, the quality of this procedure relies on the availability of …