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Coupling between superconducting qubits is typically controlled not by changing the qubit-qubit coupling constant, but by suppressing the coupling by detuning their transition frequency. This approach becomes much more difficult with a high number of qubits, due to the ever-more crowded transition-frequency spectrum. In this paper, Casparis et al. demonstrate an alternative coupling scheme, in the form of a voltage controlled quantum-bus with the ability to change the effective qubit-qubit coupling by a factor of 8 between the on- and off-states without causing significant qubit decoherence.

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Change point detection is a vast branch of statistical analysis developing techniques for uncovering abrupt changes in the underlying probability distribution of streaming data. This can be done off-line (using time-series data) or online (processing data sequentially). The latter enables real-time decision making, require less memory and is most relevant in machine learning. In this paper, Sentis et al. discuss online detection strategies for identifying a change point in a stream of quantum particles allegedly prepared in identical states. They show that the identification of the change point can be done without error via sequential local measurements, requiring only one classical bit of memory between subsequent measurements.

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Transition metal dichalcogenide monolayers (TMDC) are atomic-thin two-dimensional materials in which electrostatic quantum dots (QD) can be created. The electrons or holes confined in these QD have not only a spin degree of freedom, but also a valley degree of freedom. This additional degree of freedom can be used to encode a qubit creating a new field of electronics called valleytronics. In this paper Pawlowski et al. show how to create a QD in a MoS2 monolayer material and how to perform the NOT operation on its valley degree of freedom.

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In the 2016 US presidential elections, many of the professional polling groups had overestimated the probability of a Clinton victory. Multiple post-election analyses concluded that a leading cause of error in their forecast models was a lack of correlation between predictions for individual states. Uncorrelated models, though much simpler to build and train, cannot capture the more complex behavior of a fully-connected system. Accurate, reliable sampling from fully-connected graphs with arbitrary correlations quickly becomes classically intractable as the graph size grows. In this paper, Henderson et al. show an initial implementation of quantum-trained Boltzmann machine used for sampling from correlated systems. They show that such a quantum-trained machine is able to generate election forecasts with similar structural properties and outcomes as a best in class modeling group.

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Entanglement-based quantum repeaters aim to extend the range of quantum-communication. Typically, entanglement-based quantum repeaters apply a (nested) combination of entanglement swapping and distillation to create high fidelity entangled pairs over longer distances, with polynomially growing local resources and moderate rates. In this paper, Zwerger et al. introduce an alternative type of quantum repeater employing hashing, a deterministic entanglement distillation protocol with one-way communication, and show that this high-fidelity scheme is scalable  to arbitrary distances, with constant overhead in resources per repeater station, and ultrahigh rates. 

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In this paper, Djidjev et al. evaluate the performance of the D-Wave 2X quantum annealer on two NP-hard graph problems: clique finding and graph partitioning. Overall, they conclude that general problems which allow to be mapped onto the D-Wave architecture are typically still too small to show a quantum speedup (although the D-wave does provide similar quality solutions as the classical solvers). For simple simulated annealing algorithms, D-Wave is considerably faster and selected instances especially designed to fit D-Wave's particular chimera architecture can be solved orders of magnitude faster than with classical techniques.

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Quantum annealers such as the D-Wave 2X allow solving NP-hard optimization problems that can be expressed as quadratic unconstrained binary (QUBO) programs. However, the relatively small number of available qubits poses a severe limitation to the range of problems that can be solved. In this paper, Hahn et al. explore the suitability of preprocessing methods for reducing the sizes of the input programs and thereby the number of qubits required for their solution on quantum computers. Specifically preprocessing reductions are discussed for max. clique and max. cut problems.

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Superconducting qubits are most commonly measured by employing their off-resonant coupling to a readout resonator. Recent experimental results have shown readout times as low as 50ns for single qubits. In this paper, Heinsoo et al. experimentally show how multiple- and selective-readout of any (sub-)set of qubits can be realized via high-fidelity rapid multiplexed readout: probing several readout resonators coupled to a single feedline with a multi-frequency pulse. Applications can be found in quantum error correction, iterative quantum fourier transforms and entanglement-distillation or -swapping.

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In this paper, Gardas et al. propose the use of the quantum fluctuation theorem to benchmark the performance of quantum annealers in respect to computational errors caused by thermal noise. They experimentally test their proposal on 2 quantum annealing devices to illustrate the sensitivity of the fluctuation theorem to the smallest aberrations from ideal annealing.

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Realization of a universal set of quantum gates is a crucial milestone in the development of any qubit-technology aiming to become a platform for large scale quantum computing. In this paper, Gutiérrez et al. assess the capability of current trapped-ion architectures in the implementation of an entangling CNOT gate between encoded logical qubits.

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Topological data analysis (TDA) offers a robust way to extract useful information from noisy, unstructured data by identifying its underlying structure. In this paper, Huang et al. show an experimental proof-of-principle of a recently developed TDA quantum algorithm for calculating Betti numbers of data points (which count the number of topological holes of various dimensions in a scatterplot), using a six-photon quantum processor on a network of three data-points.

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Usually, the interaction of an open quantum system with a noisy environment causes an irreversible loss of quantum coherence, which can be described by the Born-Markov approximation. Contrary, a non-Markovian environment (e.g. strong system-environment coupling) shows a pronounced 'memory effect', which can be used to revive quantum coherence as an alternative from shielding the environment. In this paper, Dong et al. show experimental results for such a non-Markovianity-assisted high-fidelity implementation of a quantum algorithm.