Continuous-variable quantum neural networks

We introduce a general method for building neural networks on quantum computers. The quantum neural network is a variational quantum circuit built in the continuous-variable (CV) architecture, which encodes quantum information in continuous degrees of freedom such as the amplitudes of the electromagnetic field. This circuit contains a layered structure of continuously parameterized gates which is universal for CV quantum computation. Affine transformations and nonlinear activation functions, two key elements in neural networks, are enacted in the quantum network using Gaussian and non-Gaussian gates, respectively. The non-Gaussian gates provide both the nonlinearity and the universality of the model. Due to the structure of the CV model, the CV quantum neural network can encode highly nonlinear transformations while remaining completely unitary. We show how a classical network can be embedded into the quantum formalism and propose quantum versions of various specialized model such as convolutional, recurrent, and residual networks. Finally, we present numerous modeling experiments built with the Strawberry Fields software library. These experiments, including a classifier for fraud detection, a network which generates Tetris images, and a hybrid classical-quantum autoencoder, demonstrate the capability and adaptability of CV quantum neural networks

When It Strikes, Are We Ready? Lessons Identified at the 7th Planetary Defense Conference in Preparing for a Near-Earth Object Impact Scenario

Abstract Near-Earth object (NEO) impact is one of the examples of high impact and low probability (HILP) event, same as the Covid-19 pandemic the world faces since the beginning of 2020. The 7th Planetary Defense Conference held by the International Academy of Astronautics (IAA) in April 2021 included an exercise on a hypothetical NEO impact event, allowing the planetary defense community to discuss potential responses. Over the span of the 4-day conference this exercise connected disaster response and management professionals to participate in a series of panels, providing feedback and perspective on the unfolding crisis scenario. The hypothetical but realistic asteroid threat scenario illustrated how such a short-warning threat might evolve. The scenario utilized during the conference indicates a need to prepare now for what might come in the future, because even with advance notice, preparation time might be minimal. This scenario chose Europe for the impact, which may likely cope with such a disaster, through the Union Civil Protection Mechanism (UCPM) and other solidarity and support mechanisms within the European Union (EU), as well as with potential support from international partners. This short article raises concern about other areas in the world on how they may access NEO impact information and cope with such disasters. It also provides an idea on vast scale of such disaster vis-à-vis the current capacity of response systems to cope with a larger event in Europe or elsewhere. This scenario showed that planetary defense is a global endeavor. Constant engagement of the planetary defense and disaster response communities is essential in order to keep the world safe from potential disasters caused by NEO impacts.</jats:p

Quantum circuits with many photons on a programmable nanophotonic chip

Growing interest in quantum computing for practical applications has led to a surge in the availability of programmable machines for executing quantum algorithms. Present day photonic quantum computers have been limited either to non-deterministic operation, low photon numbers and rates, or fixed random gate sequences. Here we introduce a full-stack hardware-software system for executing many-photon quantum circuits using integrated nanophotonics: a programmable chip, operating at room temperature and interfaced with a fully automated control system. It enables remote users to execute quantum algorithms requiring up to eight modes of strongly squeezed vacuum initialized as two-mode squeezed states in single temporal modes, a fully general and programmable four-mode interferometer, and genuine photon number-resolving readout on all outputs. Multi-photon detection events with photon numbers and rates exceeding any previous quantum optical demonstration on a programmable device are made possible by strong squeezing and high sampling rates. We verify the non-classicality of the device output, and use the platform to carry out proof-of-principle demonstrations of three quantum algorithms: Gaussian boson sampling, molecular vibronic spectra, and graph similarity

Convex optimization of programmable quantum computers

A fundamental model of quantum computation is the programmable quantum gate array. This is a quantum processor that is fed by a program state that induces a corresponding quantum operation on input states. While being programmable, any finite-dimensional design of this model is known to be non-universal, meaning that the processor cannot perfectly simulate an arbitrary quantum channel over the input. Characterizing how close the simulation is and finding the optimal program state have been open questions for the past 20 years. Here, we answer these questions by showing that the search for the optimal program state is a convex optimization problem that can be solved via semi-definite programming and gradient-based methods commonly employed for machine learning. We apply this general result to different types of processors, from a shallow design based on quantum teleportation, to deeper schemes relying on port-based teleportation and parametric quantum circuits

Global transpiration data from sap flow measurements: The SAPFLUXNET database

Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land-atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80% of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50% of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56% of the datasets. Many datasets contain data for species that make up 90% or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository (10.5281/zenodo.3971689; Poyatos et al., 2020a). The "sapfluxnetr"R package-designed to access, visualize, and process SAPFLUXNET data-is available from CRAN. © 2021 Rafael Poyatos et al.This research was supported by the Minis-terio de Economía y Competitividad (grant no. CGL2014-55883-JIN), the Ministerio de Ciencia e Innovación (grant no. RTI2018-095297-J-I00), the Ministerio de Ciencia e Innovación (grant no. CAS16/00207), the Agència de Gestió d’Ajuts Universitaris i de Recerca (grant no. SGR1001), the Alexander von Humboldt-Stiftung (Humboldt Research Fellowship for Experienced Researchers (RP)), and the Institució Catalana de Recerca i Estudis Avançats (Academia Award (JMV)). Víctor Flo was supported by the doctoral fellowship FPU15/03939 (MECD, Spain)