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Commercially available hardware exists for Quantum Key Distribution (QKD) up-to a range of ~100 km. Range extension is being targeted using different approaches based on ground and satellite based trusted-nodes or quantum-repeaters. In this paper Liao et al. show experimental results of quantum key distribution between a low-Earth-orbit satellite and multiple ground stations located in China and Europe. A secret key is created between China and Europe at locations separated by 7600 km on Earth with ~kHz rate per passage of the satellite Micius over a ground station.

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Quantum states that violate Bell's inequality are all non-local states (entangled), but there are other states that do not violate a particular bell inequality but are still non-local. In this paper, Das et al. work with an inequality called I3322 (discovered by Collins et al.), which is inequivalent to the Bell-CHSH inequality (there are states that don't violate Bell-CHSH but do violate I3322), and construct a Bayesian game where a mixed entangled state provides higher individual payoffs than the classical equilibria and where the social welfare payoff is also increased beyond the upper limit for the classical scenario.

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Multi-qubit entanglement is a critical prerequisite for quantum supremacy (quantum computers offering tractability or speed-up compared to classical computing). So far, verified multipartite entanglement has been reported in qubit platforms up to 14 trapped ions, 10 photons and 10 superconducting qubits. In this paper, Wang et al. experimentally demonstrate an 18-qubit maximal (GHZ) entanglement by simultaneous exploiting three different degrees-of-freedom of six photons, including their paths, polarization, and orbital angular momentum.

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In this paper, Wang et al. present experimental results showing genuine multipartite entanglement of up to 16 qubits on the ibmqx5 device, a 16 transmon-qubit universal quantum computing device developed by IBM. Prior to these results entanglement had been reported up to 10 superconducting qubits.

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Quantum computers promise to reduce the computational complexity of simulating quantum many-body systems from exponential to polynomial. Much effort is being put in reducing the complexity of the necessary algorithms, to allow them to be run on noisy intermediate scale quantum computers. In this paper, Dumitrescu et al. report a quantum simulation of the deuteron binding energy on 2 such small-scale noisy cloud accessible quantum processors (the IBM QX5 and Rigetti 19Q).

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Understanding and modeling the behavior of large numbers of interacting fermions is key to understanding the macroscopic properties of matter. However, the memory required to represent such a many-body state scales exponentially with the number of fermions, which makes simulation of many interesting cases intractable on classical computers. Algorithms leveraging the advantages of quantum computers for quantum simulations have steadily been developed in the past two decades. Variational quantum eigensolvers (VQE) have recently appeared as a promising class of quantum algorithms designed to prepare states for such quantum simulations. Low-depth circuits for such state preparation and quantum simulation are needed for practical quantum chemistry applications on near-term quantum devices with limited coherence. In this paper, Dallaire-Demers et al. present a new type of low-depth VQE ansatz, which should be in reach of near-term quantum devices and which can accurately prepare the ground state of correlated fermionic systems.

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In this article, John Preskill provides his view on the near-term (5-10 years ahead) societal and commercial impact of quantum-computers. Specifically, the author focuses on what he calls Noisy Intermediate-Scale Quantum (NISQ) technology: quantum computers with 50-100 qubits for which noise in quantum gates will limit the size of quantum circuits that can be executed reliably. Such NISQ devices may be able to perform tasks which surpass the capabilities of today’s classical digital computers and will be useful tools for exploring e.g. many-body quantum physics, and may have other useful applications, but, the author states, the 100-qubit quantum computer will not change the world right away and we should regard them as a significant step toward the more powerful quantum technologies of the future.

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Quantum annealers can be used to solve optimization and sampling problems. However,  they can also solve certain combinational logic problems on the basis of an Ising-model implementation of Boolean logic. In this paper, Maezawa et al. propose a prime factoring machine operated in a frame work of quantum annealing (QA). The idea is inverse operation of a quantum-mechanically reversible multiplier implemented with QA-based Boolean logic circuits. They discuss their plan toward a practical-scale factoring machine from concept to technology.

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Practical applications for current noise and small quantum-computing hardware, has focused mostly on short-depth parameterized quantum circuits used as a subroutine embedded in a larger classical optimization loop. In this paper, Otterbach et al. describe experiments with unsupervised machine learning (specifically clustering), which they translate into a combinatorial optimization problem solved by the quantum approximate optimization algorithm (QAOA) running on the Rigetti 19Q (a 19 qubit gate-based processor). They show that their implementation finds optimal solution for this task even with relatively noisy gates.

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For (un-)supervised learning, with applications in data-mining, prediction and classification, already quite a few quantum algorithms have been developed showing potential for (super-) polynomial speed-ups. Less is known about the benefits quantum can bring to reinforcement learning (RL), which has applications in a.o. AI and autonomous driving. In RL  a learning-agent perceives (aspects of) the states of a task environment, and influences subsequent states by performing actions. Certain state-action-state transitions are rewarding, and successful learning agents learn optimal behavior. In this paper, Dunjko et al. construct quantum-enhanced reinforcement-learners, which learn super-polynomially, and even exponentially faster than any classical reinforcement learning model.

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So-called holonomic quantum gates based on geometric phases are robust against control-errors. Zanardi and Rasetti, first proposed the adiabatic holonomic quantum computation (AHQC), which has the unavoidable challenge of long run-time needed for adiabatic evolution increasing the vulnerability to decoherence. Therefore non-adiabatic HQC schemes, with much shorter gate-times, were proposed and realized in platforms based on trapped ions, NMR, superconducting circuits and nitrogen-vacancy centers in diamond. In this paper, Zhao et al. propose a non-adiabatic HQC scheme based on Rydberg atoms, which combines robustness to control-errors, short gate times and long coherence times.

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Majorana bound states are quasi-particles, which obey non-Abelian braiding statistics (meaning they are neither bosons nor fermions). Topological quantum computation uses multiple such quasiparticles to store quantum information, where the non-local encoding provides high fault-tolerance (immunity to local perturbations). Unitary gates can be created by braiding. A semiconductor nanowire coupled to a superconductor can be tuned into a topological superconductor with two Majorana zero-modes localized at the wire ends. Tunneling into a Majorana mode will show a robustly quantized zero-bias peak (ZBP) in the differential conductance. In this paper, Zhang et al. are the first to experimentally show the exact theoretically predicted ZBP quantization, which strongly supports the existence of non-Abelian Majorana zero-modes in their system, paving the way for their next challenge: braiding experiments.

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