Entanglement purification protocols for all graph states

We present multiparty entanglement purification protocols that are capable of purifying arbitrary graph states directly. We develop recurrence and breeding protocols and compare our methods with strategies based on bipartite entanglement purification in static and communication scenarios. We find that direct multiparty purification is of advantage with respect to achievable yields and minimal required fidelity in static scenarios, and with respect to obtainable fidelity in the case of noisy operations in both scenarios.Comment: revtex 10 pages, 6 figure

Universal quantum computer from a quantum magnet

We show that a local Hamiltonian of spin-3/2 particles with only two-body nearest-neighbor Affleck-Kennedy-Lieb-Tasaki and exchange-type interactions has an unique ground state, which can be used to implement universal quantum computation merely with single-spin measurements. We prove that the Hamiltonian is gapped, independent of the system size. Our result provides a further step towards utilizing systems with condensed matter-type interactions for measurement-based quantum computation.Comment: 5 pages, 3 figure

Flexible resources for quantum metrology

Quantum metrology offers a quadratic advantage over classical approaches to parameter estimation problems by utilizing entanglement and nonclassicality. However, the hurdle of actually implementing the necessary quantum probe states and measurements, which vary drastically for different metrological scenarios, is usually not taken into account. We show that for a wide range of tasks in metrology, 2D cluster states (a particular family of states useful for measurement-based quantum computation) can serve as flexible resources that allow one to efficiently prepare any required state for sensing, and perform appropriate (entangled) measurements using only single qubit operations. Crucially, the overhead in the number of qubits is less than quadratic, thus preserving the quantum scaling advantage. This is ensured by using a compression to a logarithmically sized space that contains all relevant information for sensing. We specifically demonstrate how our method can be used to obtain optimal scaling for phase and frequency estimation in local estimation problems, as well as for the Bayesian equivalents with Gaussian priors of varying widths. Furthermore, we show that in the paradigmatic case of local phase estimation 1D cluster states are sufficient for optimal state preparation and measurement.Comment: 9+18 pages, many figure

The U(1) Lattice Gauge Theory Universally Connects All Classical Models with Continuous Variables, Including Background Gravity

We show that the partition function of many classical models with continuous degrees of freedom, e.g. abelian lattice gauge theories and statistical mechanical models, can be written as the partition function of an (enlarged) four-dimensional lattice gauge theory (LGT) with gauge group U(1). This result is very general that it includes models in different dimensions with different symmetries. In particular, we show that a U(1) LGT defined in a curved spacetime can be mapped to a U(1) LGT with a flat background metric. The result is achieved by expressing the U(1) LGT partition function as an inner product between two quantum states.Comment: Published version, 31 pages, 12 figures; references update

Quantum communication cost of preparing multipartite entanglement

We study the preparation and distribution of high-fidelity multi-party entangled states via noisy channels and operations. In the particular case of GHZ and cluster states, we study different strategies using bipartite or multipartite purification protocols. The most efficient strategy depends on the target fidelity one wishes to achieve and on the quality of transmission channel and local operations. We show the existence of a crossing point beyond which the strategy making use of the purification of the state as a whole is more efficient than a strategy in which pairs are purified before they are connected to the final state. We also study the efficiency of intermediate strategies, including sequences of purification and connection. We show that a multipartite strategy is to be used if one wishes to achieve high fidelity, whereas a bipartite strategy gives a better yield for low target fidelity.Comment: 21 pages, 17 figures; accepted for publication in Phys. Rev. A; v2: corrections in figure

Machine Learning for Long-Distance Quantum Communication

Machine learning can help us in solving problems in the context of big-data analysis and classification, as well as in playing complex games such as Go. But can it also be used to find novel protocols and algorithms for applications such as large-scale quantum communication? Here we show that machine learning can be used to identify central quantum protocols, including teleportation, entanglement purification, and the quantum repeater. These schemes are of importance in long-distance quantum communication, and their discovery has shaped the field of quantum information processing. However, the usefulness of learning agents goes beyond the mere reproduction of known protocols; the same approach allows one to find improved solutions to long-distance communication problems, in particular when dealing with asymmetric situations where the channel noise and segment distance are nonuniform. Our findings are based on the use of projective simulation, a model of a learning agent that combines reinforcement learning and decision making in a physically motivated framework. The learning agent is provided with a universal gate set, and the desired task is specified via a reward scheme. From a technical perspective, the learning agent has to deal with stochastic environments and reactions. We utilize an idea reminiscent of hierarchical skill acquisition, where solutions to subproblems are learned and reused in the overall scheme. This is of particular importance in the development of long-distance communication schemes, and opens the way to using machine learning in the design and implementation of quantum networks

Autism spectrum disorders in children and adolescents with Moebius sequence

Moebius sequence is a rare congenital disorder usually defined as a combination of facial weakness with impairment of ocular abduction. A strong association of Moebius sequence with autism spectrum disorders (ASDs) has been suggested in earlier studies with heterogenous age groups. The primary caregivers of all children and adolescents with Moebius sequence aged 6–17 years known to the German Moebius foundation were anonymously asked to complete two screening measures of ASD [Behavior and Communication Questionnaire (VSK); Marburger Asperger’s Syndrome Rating Scale (MBAS)]. For those who reached the cut-off for ASD, well standardized diagnostic instruments (Autism Diagnostic Interview-Revised, Autism Diagnostic Observation Schedule, WISC-III, and Kinder-DIPS) should be administered. Minimal diagnostic criteria for Moebius sequence were congenital facial weakness (uni- or bilateral) and impairment of ocular abduction (uni- or bilateral). Familiar cases should be excluded. The primary caregivers of 35/46 children and adolescents (18 males, 17 females, mean age 11.5 years) sent back completed questionnaires, but only 27 subjects met inclusion criteria. According to the primary caregivers, none of these subjects showed mental retardation. Two probands (both males 9 and 16 years old) reached the cut-off of the MBAS whereas the results of the VSK did not indicate ASDs in any of the patients. The 9 year old boy could be examined personally and did not meet diagnostic criteria of ASD. ASDs might be not as frequent as reported in previous studies on patients with Moebius sequence, at least not in patients without mental retardation