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, 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 […]
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 […]
Quantum 6, 828 (2022). https://doi.org/10.22331/q-2022-10-06-828 Quantum error correction is believed to be a necessity for large-scale fault-tolerant quantum computation. In the past two decades, various constructions of quantum error-correcting codes (QECCs) have been developed, leading […]
Quantum 6, 827 (2022). https://doi.org/10.22331/q-2022-10-06-827 We characterize the quantum entanglement of the realistic two-qubit signals that are sensitive to charge noises. Our working example is the time response generated from a silicon double quantum dot […]
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