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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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Quantum Computational Chemistry is one of the most promising applications for both near-term and large scale fault-tolerant quantum-computers. In this paper, McClean et al. present Open Fermion (www.openfermion.org), an open-source software library written largely in Python, aimed at enabling the simulation of fermionic models and quantum chemistry problems on quantum hardware. Without such a library, developing and studying algorithms for these problems is be difficult due to the prohibitive amount of domain knowledge required in both the area of chemistry and quantum algorithms. Beginning with an interface to common electronic structure packages, it simplifies the translation between a molecular specification and a quantum circuit for solving or studying the electronic structure problem on a quantum computer, minimizing the amount of domain expertise required to enter the field.

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Reinforcement learning6 differs from supervised and unsupervised learning in that it takes into account a scalar parameter (reward) to evaluate the input-output relation in a trial and error way. In this paper, Cardenas-Lopez et al. propose a protocol to perform generalized quantum reinforcement learning. They consider diverse possible scenarios for an agent, an environment, and a register that connects them, involving multi-qubit and multi-level systems, as well as open-system dynamics and they propose possible implementations of this protocol in trapped ions and superconducting circuits. 

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In this paper, Neill et al. (Google/UCSB), present experimental results for their 9 transmon (gmon) qubit device and illustrate that these experiments form a blueprint for demonstrating quantum supremacy on their next-generation (50 qubit) system. By individually tuning the qubit parameters, they are able to generate thousands of unique Hamiltonian evolutions and probe the output probabilities. The measured probabilities obey a universal distribution, consistent with uniformly sampling the full Hilbert-space. As the number of qubits in the algorithm is varied, the system continues to explore the exponentially growing number of states. They also compare the measurement results with the expected behavior and show that the algorithm can be implemented with high fidelity.

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In this paper, Dunjko et al. provide a comprehensive review of the current (Sept 2017) state of quantum machine learning, including quantum providing speed-ups or enhancing classical ML and classical classical ML being used for quantum-control or to design quantum-circuits

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Topological codes, and the surface code in particular, are popular choices for many quantum computing architectures, because of high error thresholds and local stabilizers. In this paper, Tuckett et al. show that a simple modification of the surface code can exhibit a fourfold  gain in the error correction threshold for a noise model in which Pauli Z errors (dephasing) occur more frequently than X or Y errors (which is common in many quantum architectures, including superconducting qubits). For pure dephasing an improved threshold of 43,7% is found (versus 10.9% for the optimal surface code), while 28,2% applies with a noise-bias-ratio of 10 (more realistic regime).

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In recent years many academics and corporates have focus on solving combinatorial optimization problems on quantum-annealing devices like those offered by D-Wave. Now that noisy intermediate scale (NISQ) gate-based quantum-processers (like those of Google, IBM, Rigetti and Intel) are nearing the moment of quantum-supremacy, it is interesting to learn what gate-based quantum-computers can bring to combinatorial optimization problems. In this work, In this paper, Zahedinejad et al. provide a survey of the approaches to solving different types of combinatorial optimization problems, in particular quadratic unconstrained binary optimization (QUBO) problems on a gate model quantum computer. They focus on four different approaches including digitizing the adiabatic quantum computing, global quantum optimization algorithms, the quantum algorithms that approximate the ground state of a general QUBO problem, and quantum sampling. 

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Quantum dot based spin qubits may offer significant advantages due to their potential for high densities, all-electrical operation, and integration onto an industrial platform. However, in quantum-dots, charge and nuclear spin noise are dominant sources of decoherence and gate errors. Silicon naturally has few nuclear spin isotopes, which can be removed through purification. As a host material, Silicon, enables single-qubit gate fidelities above 99%. In this paper, Watson et al. demonstrate a programmable two-qubit quantum processor in silicon by performing both the Deutsch-Josza and the Grover search algorithms.

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Superpositions of bit strings (many-body spin configurations) have been recently proposed as a key to quantum machine learning applications. Adiabatic protocols may serve as an effective method to prepare such states. If the ground state of the final Hamiltonian in an adiabatic protocol is energetically degenerate, the final state of the protocol is a superposition of the configurations in the degenerate manifold. The challenge is to be able to control the dynamics of the protocol such that the amplitudes of the final state can be deterministically programmed. In this paper, Sieberer et al. present a framework to do precisely that. They apply an adiabatic protocol with controlled diabatic transitions to dynamically prepare programmable superpositions, where the control parameters can, even for large systems, be determined efficiently.

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