Experimental realization of multipartite entanglement of 60 modes of a quantum optical frequency comb

We report the experimental realization and characterization of one 60-mode copy, and of two 30-mode copies, of a dual-rail quantum-wire cluster state in the quantum optical frequency comb of a bimodally pumped optical parametric oscillator. This is the largest entangled system ever created whose subsystems are all available simultaneously. The entanglement proceeds from the coherent concatenation of a multitude of EPR pairs by a single beam splitter, a procedure which is also a building block for the realization of hypercubic-lattice cluster states for universal quantum computing.Comment: Accepted by PRL. 5 pages, 5 figures + 14 pages, 9 figures of supplemental material. Ver3: better experimental dat

Weaving quantum optical frequency combs into continuous-variable hypercubic cluster states

Cluster states with higher-dimensional lattices that cannot be physically embedded in three-dimensional space have important theoretical interest in quantum computation and quantum simulation of topologically ordered condensed-matter systems. We present a simple, scalable, top-down method of entangling the quantum optical frequency comb into hypercubic-lattice continuous-variable cluster states of a size of about 10^4 quantum field modes, using existing technology. A hypercubic lattice of dimension D (linear, square, cubic, hypercubic, etc.) requires but D optical parametric oscillators with bichromatic pumps whose frequency splittings alone determine the lattice dimensionality and the number of copies of the state.Comment: 8 pages, 5 figures, submitted for publicatio

Truthful Mechanisms for Agents that Value Privacy

Recent work has constructed economic mechanisms that are both truthful and differentially private. In these mechanisms, privacy is treated separately from the truthfulness; it is not incorporated in players' utility functions (and doing so has been shown to lead to non-truthfulness in some cases). In this work, we propose a new, general way of modelling privacy in players' utility functions. Specifically, we only assume that if an outcome $o$ has the property that any report of player $i$ would have led to $o$ with approximately the same probability, then $o$ has small privacy cost to player $i$. We give three mechanisms that are truthful with respect to our modelling of privacy: for an election between two candidates, for a discrete version of the facility location problem, and for a general social choice problem with discrete utilities (via a VCG-like mechanism). As the number $n$ of players increases, the social welfare achieved by our mechanisms approaches optimal (as a fraction of $n$)

One-way quantum computing with arbitrarily large time-frequency continuous-variable cluster states from a single optical parametric oscillator

One-way quantum computing is experimentally appealing because it requires only local measurements on an entangled resource called a cluster state. Record-size, but non-universal, continuous-variable cluster states were recently demonstrated separately in the time and frequency domains. We propose to combine these approaches into a scalable architecture in which a single optical parametric oscillator and simple interferometer entangle up to ($3\times 10^3$ frequencies) $\times$ (unlimited number of temporal modes) into a new and computationally universal continuous-variable cluster state. We introduce a generalized measurement protocol to enable improved computational performance on this new entanglement resource.Comment: (v4) Consistent with published version; (v3) Fixed typo in arXiv abstract, 14 pages, 8 figures; (v2) Supplemental material incorporated into main text, additional explanations added, results unchanged, 14 pages, 8 figures; (v1) 5 pages (3 figures) + 6 pages (5 figures) of supplemental material; submitted for publicatio

Unconstrained Submodular Maximization with Constant Adaptive Complexity

In this paper, we consider the unconstrained submodular maximization problem. We propose the first algorithm for this problem that achieves a tight $(1/2-\varepsilon)$-approximation guarantee using $\tilde{O}(\varepsilon^{-1})$ adaptive rounds and a linear number of function evaluations. No previously known algorithm for this problem achieves an approximation ratio better than $1/3$ using less than $\Omega(n)$ rounds of adaptivity, where $n$ is the size of the ground set. Moreover, our algorithm easily extends to the maximization of a non-negative continuous DR-submodular function subject to a box constraint and achieves a tight $(1/2-\varepsilon)$-approximation guarantee for this problem while keeping the same adaptive and query complexities.Comment: Authors are listed in alphabetical orde

Sex Differences in Spatial Accuracy Relate to the Neural Activation of Antagonistic Muscles in Young Adults

Sex is an important physiological variable of behavior, but its effect on motor control remains poorly understood. Some evidence suggests that women exhibit greater variability during constant contractions and poorer accuracy during goal-directed tasks. However, it remains unclear whether motor output variability or altered muscle activation impairs accuracy in women. Here, we examine sex differences in endpoint accuracy during ankle goal-directed movements and the activity of the antagonistic muscles. Ten women (23.1 ± 5.1 years) and 10 men (23 ± 3.7 years) aimed to match a target (9° in 180 ms) with ankle dorsiflexion. Participants performed 50 trials and we recorded the endpoint accuracy and the electromyographic (EMG) activity of the primary agonist (Tibialis Anterior; TA) and antagonist (Soleus; SOL) muscles. Women exhibited greater spatial inaccuracy (Position error: t = −2.65, P = 0.016) but not temporal inaccuracy relative to men. The motor output variability was similar for the two sexes (P \u3e 0.2). The spatial inaccuracy in women was related to greater variability in the coordination of the antagonistic muscles (R 2 0.19, P = 0.03). These findings suggest that women are spatially less accurate than men during fast goal-directed movements likely due to an altered activation of the antagonistic muscles

Online Agnostic Boosting via Regret Minimization

Boosting is a widely used machine learning approach based on the idea of aggregating weak learning rules. While in statistical learning numerous boosting methods exist both in the realizable and agnostic settings, in online learning they exist only in the realizable case. In this work we provide the first agnostic online boosting algorithm; that is, given a weak learner with only marginally-better-than-trivial regret guarantees, our algorithm boosts it to a strong learner with sublinear regret. Our algorithm is based on an abstract (and simple) reduction to online convex optimization, which efficiently converts an arbitrary online convex optimizer to an online booster. Moreover, this reduction extends to the statistical as well as the online realizable settings, thus unifying the 4 cases of statistical/online and agnostic/realizable boosting