Strong connections between quantum encodings, non-locality and quantum cryptography

Encoding information in quantum systems can offer surprising advantages but at the same time there are limitations that arise from the fact that measuring an observable may disturb the state of the quantum system. In our work, we provide an in-depth analysis of a simple question: What happens when we perform two measurements sequentially on the same quantum system? This question touches upon some fundamental properties of quantum mechanics, namely the uncertainty principle and the complementarity of quantum measurements. Our results have interesting consequences, for example they can provide a simple proof of the optimal quantum strategy in the famous Clauser-Horne-Shimony-Holt game. Moreover, we show that the way information is encoded in quantum systems can provide a different perspective in understanding other fundamental aspects of quantum information, like non-locality and quantum cryptography. We prove some strong equivalences between these notions and provide a number of applications in all areas.Comment: Version 3. Previous title: "Oblivious transfer, the CHSH game, and quantum encodings

Provable Bounds for Learning Some Deep Representations

We give algorithms with provable guarantees that learn a class of deep nets in the generative model view popularized by Hinton and others. Our generative model is an $n$ node multilayer neural net that has degree at most $n^{\gamma}$ for some $\gamma <1$ and each edge has a random edge weight in $[-1,1]$. Our algorithm learns {\em almost all} networks in this class with polynomial running time. The sample complexity is quadratic or cubic depending upon the details of the model. The algorithm uses layerwise learning. It is based upon a novel idea of observing correlations among features and using these to infer the underlying edge structure via a global graph recovery procedure. The analysis of the algorithm reveals interesting structure of neural networks with random edge weights.Comment: The first 18 pages serve as an extended abstract and a 36 pages long technical appendix follow