Quantum 6, 837 (2022). https://doi.org/10.22331/q-2022-10-13-837 A critical engineering challenge in quantum technology is the accurate control of quantum dynamics. Model-based methods for optimal control have been shown to be highly effective when theory and experiment […]
Quantum 6, 836 (2022). https://doi.org/10.22331/q-2022-10-13-836 Logic Artificial Intelligence (AI) is a subfield of AI where variables can take two defined arguments, True or False, and are arranged in clauses that follow the rules of formal […]
Quantum 6, 835 (2022). https://doi.org/10.22331/q-2022-10-13-835 Every open-system dynamics can be associated to infinitely many stochastic pictures, called unravelings, which have proved to be extremely useful in several contexts, both from the conceptual and the practical […]
Quantum 6, 834 (2022). https://doi.org/10.22331/q-2022-10-13-834 We initiate the systematic study of QMA algorithms in the setting of property testing, to which we refer as QMA $textit{proofs of proximity}$ (QMAPs). These are quantum query algorithms that […]
Quantum 6, 833 (2022). https://doi.org/10.22331/q-2022-10-13-833 Through the introduction of auxiliary fermions, or an enlarged spin space, one can map local fermion Hamiltonians onto local spin Hamiltonians, at the expense of introducing a set of additional […]
DeepMind has done it again. After solving a fundamental challenge in biology—predicting protein structure—and untangling the mathematics of knot theory, it’s taken aim at a fundamental computing process embedded inside thousands of everyday applications. From […]
Quantum 6, 832 (2022). https://doi.org/10.22331/q-2022-10-11-832 We investigate the quantum nature of gravity in terms of the coherence of quantum objects. As a basic setting, we consider two gravitating objects each in a superposition state of […]
Quantum 6, 831 (2022). https://doi.org/10.22331/q-2022-10-06-831 We study bosonic quantum computations using the Segal-Bargmann representation of quantum states. We argue that this holomorphic representation is a natural one which not only gives a canonical description of […]
Quantum 6, 830 (2022). https://doi.org/10.22331/q-2022-10-06-830 Quantum phase estimation is a cornerstone in quantum algorithm design, allowing for the inference of eigenvalues of exponentially-large sparse matrices.The maximum rate at which these eigenvalues may be learned, –known […]
Quantum 6, 829 (2022). https://doi.org/10.22331/q-2022-10-06-829 Quantum computing is believed to be particularly useful for the simulation of chemistry and materials, among the various applications. In recent years, there have been significant advancements in the development […]
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