Competitive Bridge Bidding with Deep Neural Networks

The game of bridge consists of two stages: bidding and playing. While playing is proved to be relatively easy for computer programs, bidding is very challenging. During the bidding stage, each player knowing only his/her own cards needs to exchange information with his/her partner and interfere with opponents at the same time. Existing methods for solving perfect-information games cannot be directly applied to bidding. Most bridge programs are based on human-designed rules, which, however, cannot cover all situations and are usually ambiguous and even conflicting with each other. In this paper, we, for the first time, propose a competitive bidding system based on deep learning techniques, which exhibits two novelties. First, we design a compact representation to encode the private and public information available to a player for bidding. Second, based on the analysis of the impact of other players' unknown cards on one's final rewards, we design two neural networks to deal with imperfect information, the first one inferring the cards of the partner and the second one taking the outputs of the first one as part of its input to select a bid. Experimental results show that our bidding system outperforms the top rule-based program.Comment: This paper was submitted to AAMAS on Nov. 12, 2018, accepted on Jan. 23, 201

Space-filling curves of self-similar sets (II): Edge-to-trail substitution rule

It is well-known that the constructions of space-filling curves depend on certain substitution rules. For a given self-similar set, finding such rules is somehow mysterious, and it is the main concern of the present paper. Our first idea is to introduce the notion of skeleton for a self-similar set. Then, from a skeleton, we construct several graphs, define edge-to-trail substitution rules, and explore conditions ensuring the rules lead to space-filling curves. Thirdly, we summarize the classical constructions of the space-filling curves into two classes: the traveling-trail class and the positive Euler-tour class. Finally, we propose a general Euler-tour method, using which we show that if a self-similar set satisfies the open set condition and possesses a skeleton, then space-filling curves can be constructed. Especially, all connected self-similar sets of finite type fall into this class. Our study actually provides an algorithm to construct space-filling curves of self-similar sets.Comment: 44 pages, 16 figure

Quantum beam splitter for orbital angular momentum of light: quantum correlation by four-wave mixing operated in a nonamplifying regime

Nondegenerate four-wave mixing (FWM) process based on a double-$\Lambda$ scheme in hot alkali metal vapor is a versatile tool in quantum state engineering, quantum imaging, and quantum precision measurements. In this Letter, we investigate the generation of quantum correlated twin beams which carry nonzero orbital angular momentums (OAMs) based on the FWM process in hot cesium vapor. The amplified probe beam and the newly generated conjugate beam in the FWM process have the same and opposite topological charge as the seed beam, respectively. We also explore the FWM process operated in a nonamplifying regime where quantum correlated twin beams carrying OAMs can still be generated. In this regime, the FWM process plays the role of quantum beam splitter for the OAM of light, that is, a device that can split a coherent light beam carrying OAM into quantum-correlated twin beams carrying OAMs. More generally, our setup can be used as a quantum beam splitter of images.Comment: 5 pages, 5 figure

Realization of a quantum walk in phase space using resonator-assisted double quantum dots

We implement a quantum walk in phase space with a new mechanism based on the superconducting resonator-assisted double quantum dots. By analyzing the hybrid system, we obtain the necessary factors of realization of a quantum walk in phase space: the walker, coin, coin flipping and conditional phase shift. In order to implement the coin flipping operator, we add a driving field to the resonator. The interaction between the quantum dots and resonator field is used to implement conditional phase shift. Furthermore, we show with different driving fields the quantum walk in phase space exhibits a ballistic behavior over 25 steps and numerically analyze the factors which influence the spreading of the walker in phase space.Comment: 6 pages, 3 figure

A programmable two-qubit solid-state quantum processor under ambient conditions

Quantum computers, which take advantage of the superposition and entanglement of physical states, could outperform their classical counterparts in solving problems with technological impact, such as factoring large numbers and searching databases. A quantum processor executes algorithms by applying a programmable sequence of gates to an initialized state of qubits, which coherently evolves into a final state containing the result of the computation. Although quantum processors with a few qubits have been demonstrated on multiple quantum computing platforms, realization of solid-state programmable quantum processor under ambient conditions remains elusive. Here we report a programable quantum processor that can be programmed with fifteen parameters to realize arbitrary unitary transformations on two spin-qubits in a nitrogen-vacancy (NV) center in diamond. We implemented the Deutsch-Jozsa and Grover search algorithms with average success rates above 80\%. This programmable two-qubit processor could form a core component of a large-scale quantum processor, and the methods used here are suitable for such a device

Generating quantum correlated twin beams by four-wave mixing in hot cesium vapor

Using a nondegenerate four-wave mixing process based on a double-$\Lambda$ scheme in hot cesium vapor, we generate quantum correlated twin beams with a maximum intensity-difference squeezing of 6.5 dB. The substantially improved squeezing can be mainly attributed to very good frequency and phase-difference stability between the pump and probe beams in our experiment. Intensity-difference squeezing can be observed within a wide experimental parameter range, which guarantees its robust generation. Since this scheme produces multi-spatial-mode twin beams at the Cs $D_{1}$ line, it is of interest for experiments involving quantum imaging and coherent interfaces between atomic and solid-state systems.Comment: 6 pages, 5 figures, comments are welcom

Catalog of narrow C IVC~IV absorption lines in BOSS (I): for quasars with zem≤2.4z_{em} \leq 2.4

We have assembled absorption systems by visually identifying $C~IV\lambda\lambda1548,1551$ absorption doublets in the quasar spectra of the Baryon Oscillation Spectroscopic Survey (BOSS) one by one. This paper is the first of the series work. In this paper, we concern quasars with relatively low redshifts and high signal-to-noise ratios for their spectra, and hence we limit our analysis on quasars with $z_{em}\le2.4$ and on the doublets with $W_r\lambda1548\ge0.2$ \AA. Out of the more than 87,000 quasars in the Data Release 9, we limit our search to 10,121 quasars that have the appropriate redshifts and spectra with high enough signal-to-noise ratios to identify narrow C IV absorption lines. Among them, 5,442 quasars are detected to have at least one $C~IV\lambda\lambda1548,1551$ absorption doublet. We obtain a catalog containing 8,368 $C~IV\lambda\lambda1548,1551$ absorption systems, whose redshifts are within $z_{abs}=1.4544$ - $2.2805$. In this catalog, about $33.7\%$ absorbers have $0.2$ \AA$\le W_r\lambda1548<0.5$ \AA, about $45.9\%$ absorbers have $0.5$ \AA$\le W_r\lambda1548<1.0$ \AA, about $19.2\%$ absorbers have $1.0$ \AA$\le W_r\lambda1548<2.0$ \AA, and about $1.2\%$ absorbers have $W_r\lambda1548\ge2.0$ \AA

ηc\eta_c production associated with light hadrons at the B-factories and the future super B-factories

We present a complete study of the associated production of the $\eta_c$ meson with light hadrons in $e^+e^-$ collisions at the B-factory energy, which is demonstrated to be one of the best laboratories for testing the colour-octet (CO) mechanism. The colour-siglet contributions are evaluated up to $O(\alpha^2\alpha_s^3)$ while the CO ones are evaluated up to $O(\alpha^2\alpha_s^2)$. For the first time, the angular distribution of the $^1S_0^{[8]}$ production is studied at QCD next-to-leading order. We find that the $^1S_0^{[8]}$ channel dominate the total cross section, while the $^1P_1^{[8]}$ one exhibit its importance in the angular distribution, which turns out to be downward going with respect to $\mathrm{cos}\theta$. This can be considered as the most distinct signal for the CO mechanism

Biased decoy-state reference-frame-independent quantum key distribution

Without actively aligning the reference frames, the reference-frame-independent quantum key distribution (RFI-QKD) can generate secure keys even when the reference frames drift slowly. Here, we propose a new scheme on the decoy-state RFI-QKD, where the basis assignments of two legitimate parties are biased, reducing the fraction of mismatched-basis data. When accounting for statistical fluctuations under different misalignments of reference frames, we investigate the performance of biased decoy-state RFI-QKD with full parameter optimization. We also make a clear comparison between the new scheme with other practical schemes, e.g., unbiased decoy-state RFI-QKD and biased decoy-state BB84-QKD. Simulation results demonstrate that the new proposed biased decoy-state RFI-QKD can significantly improve the performance (secret key rate and secure transmission distance) of practical QKD systems.Comment: 9pages, 3 figure

Dual Memory Network Model for Biased Product Review Classification

In sentiment analysis (SA) of product reviews, both user and product information are proven to be useful. Current tasks handle user profile and product information in a unified model which may not be able to learn salient features of users and products effectively. In this work, we propose a dual user and product memory network (DUPMN) model to learn user profiles and product reviews using separate memory networks. Then, the two representations are used jointly for sentiment prediction. The use of separate models aims to capture user profiles and product information more effectively. Compared to state-of-the-art unified prediction models, the evaluations on three benchmark datasets, IMDB, Yelp13, and Yelp14, show that our dual learning model gives performance gain of 0.6%, 1.2%, and 0.9%, respectively. The improvements are also deemed very significant measured by p-values.Comment: To appear in 2018 EMNLP 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysi