Comprehensive theory of the relative phase in atom-field interactions

We explore the role played by the quantum relative phase in a well-known model of atom-field interaction, namely, the Dicke model. We introduce an appropriate polar decomposition of the atom-field relative amplitudes that leads to a truly Hermitian relative-phase operator, whose eigenstates correctly describe the phase properties, as we demonstrate by studying the positive operator-valued measure derived from it. We find the probability distribution for this relative phase and, by resorting to a numerical procedure, we study its time evolution.Comment: 20 pages, 4 figures, submitted to Phys. Rev.

Unsupervised Stream-Weights Computation in Classification and Recognition Tasks

International audienceIn this paper, we provide theoretical results on the problem of optimal stream weight selection for the multi-stream classi- fication problem. It is shown, that in the presence of estimation or modeling errors using stream weights can decrease the total classification error. Stream weight estimates are computed for various conditions. Then we turn our attention to the problem of unsupervised stream weights computation. Based on the theoretical results we propose to use models and “anti-models” (class- specific background models) to estimate stream weights. A non-linear function of the ratio of the inter- to intra-class distance is used for stream weight estimation. The proposed unsupervised stream weight estimation algorithm is evaluated on both artificial data and on the problem of audio-visual speech classification. Finally the proposed algorithm is extended to the problem of audio- visual speech recognition. It is shown that the proposed algorithms achieve results comparable to the supervised minimum-error training approach under most testing conditions

Lost and found: the radial quantum number of Laguerre-Gauss modes

We introduce an operator linked with the radial index in the Laguerre-Gauss modes of a two-dimensional harmonic oscillator in cylindrical coordinates. We discuss ladder operators for this variable, and confirm that they obey the commutation relations of the su(1,1) algebra. Using this fact, we examine how basic quantum optical concepts can be recast in terms of radial modes.Comment: Some minor typos fixed

Benchmarking quantum tomography completeness and fidelity with machine learning

We train convolutional neural networks to predict whether or not a set of measurements is informationally complete to uniquely reconstruct any given quantum state with no prior information. In addition, we perform fidelity benchmarking based on this measurement set without explicitly carrying out state tomography. The networks are trained to recognize the fidelity and a reliable measure for informational completeness through collective encoding of quantum measurements, data and target states into grayscale images. By gradually accumulating measurements and data, these convolutional networks can efficiently certify a low-measurement-cost quantum-state characterization scheme. We confirm the potential of this machine-learning approach by presenting experimental results for both spatial-mode and multiphoton systems of large dimensions. These predictions are further shown to improve with noise recognition when the networks are trained with additional bootstrapped training sets from real experimental data

Adaptive compressive tomography with no a priori information

Quantum state tomography is both a crucial component in the field of quantum information and computation, and a formidable task that requires an incogitably large number of measurement configurations as the system dimension grows. We propose and experimentally carry out an intuitive adaptive compressive tomography scheme, inspired by the traditional compressed-sensing protocol in signal recovery, that tremendously reduces the number of configurations needed to uniquely reconstruct any given quantum state without any additional a priori assumption whatsoever (such as rank information, purity, etc) about the state, apart from its dimension.Comment: 6 pages, 4 figure

Quantum metrology at the limit with extremal Majorana constellations

Quantum metrology allows for a tremendous boost in the accuracy of measurement of diverse physical parameters. The estimation of a rotation constitutes a remarkable example of this quantum-enhanced precision. The recently introduced Kings of Quantumness are especially germane for this task when the rotation axis is unknown, as they have a sensitivity independent of that axis and they achieve a Heisenberg-limit scaling. Here, we report the experimental realization of these states by generating up to 21-dimensional orbital angular momentum states of single photons, and confirm their high metrological abilities

Mediterranean Sea response to climate change in an ensemble of twenty first century scenarios

The Mediterranean climate is expected to become warmer and drier during the twenty-first century. Mediterranean Sea response to climate change could be modulated by the choice of the socio-economic scenario as well as the choice of the boundary conditions mainly the Atlantic hydrography, the river runoff and the atmospheric fluxes. To assess and quantify the sensitivity of the Mediterranean Sea to the twenty-first century climate change, a set of numerical experiments was carried out with the regional ocean model NEMOMED8 set up for the Mediterranean Sea. The model is forced by air–sea fluxes derived from the regional climate model ARPEGE-Climate at a 50-km horizontal resolution. Historical simulations representing the climate of the period 1961–2000 were run to obtain a reference state. From this baseline, various sensitivity experiments were performed for the period 2001–2099, following different socio-economic scenarios based on the Special Report on Emissions Scenarios. For the A2 scenario, the main three boundary forcings (river runoff, near-Atlantic water hydrography and air–sea fluxes) were changed one by one to better identify the role of each forcing in the way the ocean responds to climate change. In two additional simulations (A1B, B1), the scenario is changed, allowing to quantify the socio-economic uncertainty. Our 6-member scenario simulations display a warming and saltening of the Mediterranean. For the 2070–2099 period compared to 1961–1990, the sea surface temperature anomalies range from +1.73 to +2.97 °C and the SSS anomalies spread from +0.48 to +0.89. In most of the cases, we found that the future Mediterranean thermohaline circulation (MTHC) tends to reach a situation similar to the eastern Mediterranean Transient. However, this response is varying depending on the chosen boundary conditions and socio-economic scenarios. Our numerical experiments suggest that the choice of the near-Atlantic surface water evolution, which is very uncertain in General Circulation Models, has the largest impact on the evolution of the Mediterranean water masses, followed by the choice of the socio-economic scenario. The choice of river runoff and atmospheric forcing both have a smaller impact. The state of the MTHC during the historical period is found to have a large influence on the transfer of surface anomalies toward depth. Besides, subsurface currents are substantially modified in the Ionian Sea and the Balearic region. Finally, the response of thermosteric sea level ranges from +34 to +49 cm (2070–2099 vs. 1961–1990), mainly depending on the Atlantic forcing