The nature of ZbZ_b states from a combined analysis of Υ(5S)→hb(mP)π+π−\Upsilon(5S)\rightarrow h_b(mP) \pi^+ \pi^- and Υ(5S)→B(∗)Bˉ(∗)π\Upsilon(5S)\rightarrow B^{(\ast)}\bar B^{(\ast)}\pi

With a combined analysis of data on $\Upsilon(5S)\rightarrow h_b(1P,2P)\pi^+\pi^-$ and $\Upsilon(5S)\rightarrow B^{(\ast)}\bar B^{(\ast)}\pi$ in an effective field theory approach, we determine resonance parameters of $Z_b$ states in two scenarios. In one scenario we assume that $Z_b$ states are pure molecular states, while in the other one we assume that $Z_b$ states contain compact components. We find that the present data favor that there should be some compact components inside $Z_b^{(\prime)}$ associated with the molecular components. By fitting the invariant mass spectra of $\Upsilon(5S)\rightarrow h_b(1P,2P)\pi^+\pi^-$ and $\Upsilon(5S)\rightarrow B^{(\ast)}\bar B^{\ast}\pi$, we determine that the probability of finding the compact components in $Z_b$ states may be as large as about $40\%$.Comment: Discussions added, version published in EPJ

A double-slit proposal for quantum annealing

We formulate and analyze a double-slit proposal for quantum annealing, which involves observing the probability of finding a two-level system (TLS) undergoing evolution from a transverse to a longitudinal field in the ground state at the final time $t_f$. We demonstrate that for annealing schedules involving two consecutive diabatic transitions, an interference effect is generated akin to a double-slit experiment. The observation of oscillations in the ground state probability as a function of $t_f$ (before the adiabatic limit sets in) then constitutes a sensitive test of coherence between energy eigenstates. This is further illustrated by analyzing the effect of coupling the TLS to a thermal bath: increasing either the bath temperature or the coupling strength results in a damping of these oscillations. The theoretical tools we introduce significantly simplify the analysis of the generalized Landau-Zener problem. Furthermore, our analysis connects quantum annealing algorithms exhibiting speedups via the mechanism of coherent diabatic transitions to near-term experiments with quantum annealing hardware.Comment: 19 pages, 3 figure

IPC: A Benchmark Data Set for Learning with Graph-Structured Data

Benchmark data sets are an indispensable ingredient of the evaluation of graph-based machine learning methods. We release a new data set, compiled from International Planning Competitions (IPC), for benchmarking graph classification, regression, and related tasks. Apart from the graph construction (based on AI planning problems) that is interesting in its own right, the data set possesses distinctly different characteristics from popularly used benchmarks. The data set, named IPC, consists of two self-contained versions, grounded and lifted, both including graphs of large and skewedly distributed sizes, posing substantial challenges for the computation of graph models such as graph kernels and graph neural networks. The graphs in this data set are directed and the lifted version is acyclic, offering the opportunity of benchmarking specialized models for directed (acyclic) structures. Moreover, the graph generator and the labeling are computer programmed; thus, the data set may be extended easily if a larger scale is desired. The data set is accessible from \url{https://github.com/IBM/IPC-graph-data}.Comment: ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Data. The data set is accessible from https://github.com/IBM/IPC-graph-dat

Online Planner Selection with Graph Neural Networks and Adaptive Scheduling

Automated planning is one of the foundational areas of AI. Since no single planner can work well for all tasks and domains, portfolio-based techniques have become increasingly popular in recent years. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent development of structural graph representations of planning tasks, we propose a graph neural network (GNN) approach to selecting candidate planners. GNNs are advantageous over a straightforward alternative, the convolutional neural networks, in that they are invariant to node permutations and that they incorporate node labels for better inference. Additionally, for cost-optimal planning, we propose a two-stage adaptive scheduling method to further improve the likelihood that a given task is solved in time. The scheduler may switch at halftime to a different planner, conditioned on the observed performance of the first one. Experimental results validate the effectiveness of the proposed method against strong baselines, both deep learning and non-deep learning based. The code is available at \url{https://github.com/matenure/GNN_planner}.Comment: AAAI 2020. Code is released at https://github.com/matenure/GNN_planner. Data set is released at https://github.com/IBM/IPC-graph-dat