Applications such as simulating complicated quantum systems or solving large-scale linear
algebra problems are very challenging for classical computers, owing to the extremely high …

Quantum state tomography is a daunting challenge of experimental quantum computing,
even in moderate system size. One way to boost the efficiency of state tomography is via …

Quantum combs play a vital role in characterizing and transforming quantum processes, with
wide-ranging applications in quantum information processing. However, obtaining the …

We present a novel, computationally efficient approach to accelerate quantum optimal
control calculations of large multi-qubit systems used in a variety of quantum computing …

Decoupling systems into independently evolving components has a long history of
simplifying seemingly complex systems. They enable a better understanding of the …

Applications such as simulating complicated quantum systems or solving large-scale linear
algebra problems are very challenging for classical computers due to the extremely high …

The gradient descent method is central to numerical optimization and is the key ingredient in
many machine learning algorithms. It promises to find a local minimum of a function by …

People are witnessing quantum computing revolutions nowadays. Progress in the number of
qubits, coherence times and gate fidelities is happening. Although quantum error correction …

We develop an optimal control algorithm for robust quantum gate preparation in open
environments with the state of the quantum system represented using the Lindblad master …

The first generation of small noisy quantum processors have recently become available to
non-specialists who are not required to understand specifics of the physical platforms and, in …