- We consider the problem of building an arbitrary $N\times N$ real orthogonal operator using a finite set, $S$, of elementary quantum optics gates operating on $m\leq N$ modes - the problem of universality of $S$ on $N$ modes. In particular, we focus on the universality problem of an $m$-mode beamsplitter. Using methods of control theory and some properties of rotations in three dimensions, we prove that any nontrivial real 2-mode and "almost" any nontrivial real $3$-mode beamsplitter is universal on $m\geq3$ modes.
- We introduce and study the task of assisted coherence distillation. This task arises naturally in bipartite systems where both parties work together to generate the maximal possible coherence on one of the subsystems. Only incoherent operations are allowed on the target system while general local quantum operations are permitted on the other, an operational paradigm that we call local quantum-incoherent operations and classical communication (LQICC). We show that the asymptotic rate of assisted coherence distillation for pure states is equal to the coherence of assistance, a direct analog of the entanglement of assistance, whose properties we characterize. Our findings imply a novel interpretation of the von Neumann entropy: it quantifies the maximum amount of extra quantum coherence a system can gain when receiving assistance from a collaborative party. Our results are generalized to coherence localization in a multipartite setting and possible applications are discussed.
- The performance of automatic speech recognition (ASR) has improved tremendously due to the application of deep neural networks (DNNs). Despite this progress, building a new ASR system remains a challenging task, requiring various resources, multiple training stages and significant expertise. This paper presents our Eesen framework which drastically simplifies the existing pipeline to build state-of-the-art ASR systems. Acoustic modeling in Eesen involves learning a single recurrent neural network (RNN) predicting context-independent targets (phonemes or characters). To remove the need for pre-generated frame labels, we adopt the connectionist temporal classification (CTC) objective function to infer the alignments between speech and label sequences. A distinctive feature of Eesen is a generalized decoding approach based on weighted finite-state transducers (WFSTs), which enables the efficient incorporation of lexicons and language models into CTC decoding. Experiments show that compared with the standard hybrid DNN systems, Eesen achieves comparable word error rates (WERs), while at the same time speeding up decoding significantly.
- Jul 30 2015 cs.CV arXiv:1507.08173v1Mining useful clusters from high dimensional data has received significant attention of the computer vision and pattern recognition community in the recent years. Linear and non-linear dimensionality reduction has played an important role to overcome the curse of dimensionality. However, often such methods are accompanied with three different problems: high computational complexity (usually associated with the nuclear norm minimization), non-convexity (for matrix factorization methods) and susceptibility to gross corruptions in the data. In this paper we propose a principal component analysis (PCA) based solution that overcomes these three issues and approximates a low-rank recovery method for high dimensional datasets. We target the low-rank recovery by enforcing two types of graph smoothness assumptions, one on the data samples and the other on the features by designing a convex optimization problem. The resulting algorithm is fast, efficient and scalable for huge datasets with $\mathcal{O}(n \log(n))$ computational complexity in the number of data samples. It is also robust to gross corruptions in the dataset as well as to the model parameters. Clustering experiments on 7 benchmark datasets with different types of corruptions and background separation experiments on 3 video datasets show that our proposed model outperforms 10 state-of-the-art dimensionality reduction models.
- Jul 30 2015 cs.CR arXiv:1507.08094v1We propose a public key encryption scheme based on the Boolean Satisfiability Problem (SAT). The public key is given by a SAT formula and the private key is the satisfying assignment. Encryption is a probabilistic algorithm that takes the bits of the message to randomly generated Boolean functions, represented in algebraic normal form. Those are implied to be true or false by the public key, hence bit-wise decryption is done by applying each function to the private key. Our scheme does not provide signatures.
- Jul 30 2015 stat.CO arXiv:1507.08050v1Probabilistic programming (PP) allows flexible specification of Bayesian statistical models in code. PyMC3 is a new, open-source PP framework with an intutive and readable, yet powerful, syntax that is close to the natural syntax statisticians use to describe models. It features next-generation Markov chain Monte Carlo (MCMC) sampling algorithms such as the No-U-Turn Sampler (NUTS; Hoffman, 2014), a self-tuning variant of Hamiltonian Monte Carlo (HMC; Duane, 1987). Probabilistic programming in Python confers a number of advantages including multi-platform compatibility, an expressive yet clean and readable syntax, easy integration with other scientific libraries, and extensibility via C, C++, Fortran or Cython. These features make it relatively straightforward to write and use custom statistical distributions, samplers and transformation functions, as required by Bayesian analysis.
- Jul 30 2015 math.OC arXiv:1507.08029v1Sparse principal component analysis (PCA) addresses the problem of finding a linear combination of the variables in a given data set with a sparse coefficients vector that maximizes the variability of the data. This model enhances the ability to interpret the principal components, and is applicable in a wide variety of fields including genetics and finance, just to name a few. We suggest a necessary coordinate-wise-based optimality condition, and show its superiority over the stationarity-based condition that is commonly used in the literature, and which is the basis for many of the algorithms designed to solve the problem. We devise algorithms that are based on the new optimality condition, and provide numerical experiments that support our assertion that algorithms which are guaranteed to converge to stronger optimality condition, perform better than algorithms that converge to points satisfying weaker optimality conditions.
- Jul 30 2015 cond-mat.mes-hall arXiv:1507.07991v1Spin-based electronics or spintronics relies on the ability to store, transport and manipulate electron spin polarization with great precision. In its ultimate limit, information is stored in the spin state of a single electron, at which point also quantum information processing becomes a possibility. Here we demonstrate the manipulation, transport and read-out of individual electron spins in a linear array of three semiconductor quantum dots. First, we demonstrate single-shot read-out of three spins with fidelities of 97% on average, using an approach analogous to the operation of a charge-coupled-device (CCD). Next, we perform site-selective control of the three spins thereby writing the content of each pixel of this "Single-Spin CCD". Finally, we show that shuttling an electron back and forth in the array hundreds of times, covering a cumulative distance of 80 $\mu$m, has negligible influence on its spin projection. Extrapolating these results to the case of much larger arrays, points at a diverse range of potential applications, from quantum information to imaging and sensing.
- This report gives the 2014 self-consistent set of values of the constants and conversion factors of physics and chemistry recommended by the Committee on Data for Science and Technology (CODATA). These values are based on a least-squares adjustment that takes into account all data available up to 31 December 2014. The recommended values may also be found on the World Wide Web at physics.nist.gov/constants.
- Jul 30 2015 cond-mat.supr-con arXiv:1507.08275v1
- Jul 30 2015 stat.ML arXiv:1507.08272v1
- Jul 30 2015 math.OA arXiv:1507.08270v1
- Jul 30 2015 physics.plasm-ph arXiv:1507.08265v1
- Jul 30 2015 math.OC arXiv:1507.08263v1
- Jul 30 2015 physics.plasm-ph arXiv:1507.08259v1
- Jul 30 2015 cs.CR arXiv:1507.08258v1
- Jul 30 2015 cs.DB arXiv:1507.08257v1
- Jul 30 2015 astro-ph.EP arXiv:1507.08256v1
- Jul 30 2015 math.DS arXiv:1507.08253v1
- Jul 30 2015 hep-ph arXiv:1507.08252v1
- Jul 30 2015 math.OC arXiv:1507.08251v1
- Jul 30 2015 hep-th arXiv:1507.08250v1
- Jul 30 2015 cs.GT arXiv:1507.08249v1
- Jul 30 2015 cond-mat.quant-gas arXiv:1507.08248v1
- Jul 30 2015 math.NA arXiv:1507.08247v1
- Jul 30 2015 math.DG arXiv:1507.08246v1
- Jul 30 2015 q-bio.PE arXiv:1507.08245v1
- Jul 30 2015 physics.chem-ph arXiv:1507.08244v1
- Jul 30 2015 math.NA arXiv:1507.08243v1
- Jul 30 2015 math.OC arXiv:1507.08241v1
- Jul 30 2015 physics.flu-dyn arXiv:1507.08239v1
- Jul 30 2015 cs.IR arXiv:1507.08234v1
- Jul 30 2015 cs.NI arXiv:1507.08233v1
- Jul 30 2015 hep-ph arXiv:1507.08231v1