How useful can machine learning be in a quantum laboratory? Here we raise the question of the potential of intelligent machines in the context of scientific research. A major motivation for the present work is the unknown reachability of various entanglement classes in quantum experiments. We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence. In our approach, the projective simulation system is challenged to design complex photonic quantum experiments that produce high-dimensional entangled multiphoton states, which are of high interest in modern quantum experiments. The artificial intelligence system learns to create a variety of entangled states, and improves the efficiency of their realization. In the process, the system autonomously (re)discovers experimental techniques which are only now becoming standard in modern quantum optical experiments -a trait which was not explicitly demanded from the system but emerged through the process of learning. Such features highlight the possibility that machines could have a significantly more creative role in future research.

The ability to generalize is an important feature of any intelligent agent. Not only because it may allow the agent to cope with large amounts of data, but also because in some environments, an agent with no generalization capabilities cannot learn. In this work we outline several criteria for generalization, and present a dynamic and autonomous machinery that enables projective simulation agents to meaningfully generalize. Projective simulation, a novel, physical approach to artificial intelligence, was recently shown to perform well in standard reinforcement learning problems, with applications in advanced robotics as well as quantum experiments. Both the basic projective simulation model and the presented generalization machinery are based on very simple principles. This allows us to provide a full analytical analysis of the agent’s performance and to illustrate the benefit the agent gains by generalizing. Specifically, we show that already in basic (but extreme) environments, learning without generalization may be impossible, and demonstrate how the presented generalization machinery enables the projective simulation agent to learn.

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.

Efficient nonlinear conversion requires that interacting optical waves maintain a consistent phase relationship when traveling in a medium despite its dispersion. Birefringent phase-matching, which is often used to compensate for the dispersion, is not applicable to optically isotropic nonlinear materials. Here, we present a one-dimensional photonic crystal structure that allows the propagation of optical surface waves, both at the fundamental and third-harmonic frequencies, as an efficient medium for phase-matched third-harmonic generation. A unique advantage of this structure is that the effective refractive indices for the surface waves are similar to the refractive index of air at both frequencies. This allows phase-matching between the first and third harmonics, and a visible collinear beam of the third harmonic is produced at the prism-coupled output. Moreover, these optical surface waves propagate over long distances even if a lossy nonlinear nanofilm is deposited onto the photonic crystal surface. We provide experimental results for third-harmonic generation at a wavelength of 410 nm for a bare dielectric Ta2O5/SiO2 multilayer structure and for the same structure coated with a 15-nm GaAs film.

Learning models of artificial intelligence can nowadays perform very well on a large variety of tasks. However, in practice different task environments are best handled by different learning models, rather than a single, universal, approach. Most non-trivial models thus require the adjustment of several to many learning parameters, which is often done on a case-by-case basis by an external party. Meta-learning refers to the ability of an agent to autonomously and dynamically adjust its own learning parameters, or meta-parameters. In this work we show how projective simulation, a recently developed model of artificial intelligence, can naturally be extended to account for meta-learning in reinforcement learning settings. The projective simulation approach is based on a random walk process over a network of clips. The suggested meta-learning scheme builds upon the same design and employs clip networks to monitor the agent's performance and to adjust its meta-parameters "on the fly". We distinguish between "reflexive adaptation" and "adaptation through learning", and show the utility of both approaches. In addition, a trade-off between flexibility and learning-time is addressed. The extended model is examined on three different kinds of reinforcement learning tasks, in which the agent has different optimal values of the meta-parameters, and is shown to perform well, reaching near-optimal to optimal success rates in all of them, without ever needing to manually adjust any meta-parameter.Comment: 14 pages, 12 figure

We study the model of projective simulation (PS) which is a novel approach to artificial intelligence (AI). Recently it was shown that the PS agent performs well in a number of simple task environments, also when compared to standard models of reinforcement learning (RL). In this paper we study the performance of the PS agent further in more complicated scenarios. To that end we chose two well-studied benchmarking problems, namely the "grid-world" and the "mountain-car" problem, which challenge the model with large and continuous input space. We compare the performance of the PS agent model with those of existing models and show that the PS agent exhibits competitive performance also in such scenarios.

Coherent controlization, i.e., coherent conditioning of arbitrary single- or multi-qubit operations on the state of one or more control qubits, is an important ingredient for the flexible implementation of many algorithms in quantum computation. This is of particular significance when certain subroutines are changing over time or when they are frequently modified, such as in decision-making algorithms for learning agents. We propose a scheme to realize coherent controlization for any number of superconducting qubits coupled to a microwave resonator. For two and three qubits, we present an explicit construction that is of high relevance for quantum learning agents. We demonstrate the feasibility of our proposal, taking into account loss, dephasing, and the cavity self-Kerr effect.

143ChemInform Abstract The preparations of the (5-tetrazolyl)tri-, -di-and -mononitromethanes and their tetrazolyl-1-and tetrazolyl-2-methyl derivatives -i.e. the compounds (II), (III), (V), (VII), (IX) and (XIX) -are described. The reactions of the derivatives (VII), (IX) and (XIX) with diazomethane according to method A) proceed via methylation of the nitrogen atoms of the heterocycle or the oxygen atoms of the nitro groups to afford the corresponding products. No methylation of the exocyclic carbon atom is observed in any of the reactions. The mixture of the isomeric nitronic acid methyl esters (XII) and (XIII) spontaneously yields the bis(5-tetrazolyl)oxadiazole derivative (XV).