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We give a short proof of Gaos Quantum Union Bound and Gentle Sequential Measurement theorems.
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Linials seminal result shows that any deterministic distributed algorithm that finds a $3$-colouring of an $n$-cycle requires at least $log^*(n)/2 - 1$ communication rounds. We give a new simpler proof of this theorem.
It has recently been shown that it is possible to represent the complete quantum state of any system as a phase-space quasi-probability distribution (Wigner function) [Phys Rev Lett 117, 180401]. Such functions take the form of expectation values of an observable that has a direct analogy to displaced parity operators. In this work we give a procedure for the measurement of the Wigner function that should be applicable to any quantum system. We have applied our procedure to IBMs Quantum Experience five-qubit quantum processor to demonstrate that we can measure and generate the Wigner functions of two different Bell states as well as the five-qubit Greenberger-Horne-Zeilinger (GHZ) state. As Wigner functions for spin systems are not unique, we define, compare, and contrast two distinct examples. We show how using these Wigner functions leads to an optimal method for quantum state analysis especially in the situation where specific characteristic features are of particular interest (such as for spin Schrodinger cat states). Furthermore we show that this analysis leads to straightforward, and potentially very efficient, entanglement test and state characterisation methods.
We use a modified version of the Peak Patch excursion set formalism to compute the mass and size distribution of QCD axion miniclusters from a fully non-Gaussian initial density field obtained from numerical simulations of axion string decay. We find strong agreement with N-Body simulations at a significantly lower computational cost. We employ a spherical collapse model and provide fitting functions for the modified barrier in the radiation era. The halo mass function at $z=629$ has a power-law distribution $M^{-0.6}$ for masses within the range $10^{-15}lesssim Mlesssim 10^{-10}M_{odot}$, with all masses scaling as $(m_a/50mumathrm{eV})^{-0.5}$. We construct merger trees to estimate the collapse redshift and concentration mass relation, $C(M)$, which is well described using analytical results from the initial power spectrum and linear growth. Using the calibrated analytic results to extrapolate to $z=0$, our method predicts a mean concentration $Csim mathcal{O}(text{few})times10^4$. The low computational cost of our method makes future investigation of the statistics of rare, dense miniclusters easy to achieve.
This is a tutorial aimed at illustrating some recent developments in quantum parameter estimation beyond the Cram`er-Rao bound, as well as their applications in quantum metrology. Our starting point is the observation that there are situations in classical and quantum metrology where the unknown parameter of interest, besides determining the state of the probe, is also influencing the operation of the measuring devices, e.g. the range of possible outcomes. In those cases, non-regular statistical models may appear, for which the Cram`er-Rao theorem does not hold. In turn, the achievable precision may exceed the Cram`er-Rao bound, opening new avenues for enhanced metrology. We focus on quantum estimation of Hamiltonian parameters and show that an achievable bound to precision (beyond the Cram`er-Rao) may be obtained in a closed form for the class of so-called controlled energy measurements. Examples of applications of the new bound to various estimation problems in quantum metrology are worked out in some details.
We develop taggers for multi-pronged jets that are simple functions of jet substructure (so-called `subjettiness) variables. These taggers can be approximately decorrelated from the jet mass in a quite simple way. Specifically, we use a Logistic Regression Design (LoRD) which, even being one of the simplest machine learning classifiers, shows a performance which surpasses that of simple variables used by the ATLAS and CMS Collaborations and is not far from more complex models based on neural networks. Contrary to the latter, our method allows for an easy implementation of tagging tasks by providing a simple and interpretable analytical formula with already optimised parameters.
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