W e present a short review of the theory of weak measurement. This should serve as a map for the theory and an easy way to get familiar with the main results, problems and paradoxes raised by the theory. Quanta 2013; 2: 7-17.
We prove a new version of the Holevo bound employing the Hilbert-Schmidt norm instead of the Kullback-Leibler divergence. Suppose Alice is sending classical information to Bob using a quantum channel, while Bob is performing some projective measurement. We bound the classical mutual information in terms of the Hilbert-Schmidt norm by its quantum Hilbert-Schmidt counterpart. This constitutes a Holevo-type upper bound on the classical information transmission rate via a quantum channel. The resulting inequality is rather natural and intuitive relating classical and quantum expressions using the same measure.
On May 2011, D-Wave Systems Inc. announced "D-Wave One", as "the world's first commercially available quantum computer". No wonder this adiabatic quantum computer based on 128-qubit chip-set provoked an immediate controversy. Over the last 40 years, quantum computation has been a very promising yet challenging research area, facing major difficulties producing a large scale quantum computer. Today, after Google has purchased "D-Wave Two" containing 512 qubits, criticism has only increased. In this work, we examine the theory underlying the D-Wave, seeking to shed some light on this intriguing quantum computer. Starting from classical algorithms such as Metropolis algorithm, genetic algorithm (GA), hill climbing and simulated annealing, we continue to adiabatic computation and quantum annealing towards better understanding of the D-Wave mechanism. Finally, we outline some applications within the fields of information and image processing. In addition, we suggest a few related theoretical ideas and hypotheses.
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Prediction in natural environments is a challenging task, and there is a lack of clarity around how a myopic organism can make short-term predictions given limited data availability and cognitive resources. In this context, we may ask what kind of resources are available to the organism to help it address the challenge of short-term prediction within its own cognitive limits. We point to one potentially important resource: ordinal patterns , which are extensively used in physics but not in the study of cognitive processes. We explain the potential importance of ordinal patterns for short-term prediction, and how natural constraints imposed through (i) ordinal pattern types, (ii) their transition probabilities and (iii) their irreversibility signature may support short-term prediction. Having tested these ideas on a massive dataset of Bitcoin prices representing a highly fluctuating environment, we provide preliminary empirical support showing how organisms characterized by bounded rationality may generate short-term predictions by relying on ordinal patterns.
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