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Growing interest in quantum computing for practical applications has led to a surge in the availability of programmable machines for executing quantum algorithms1,2. Present-day photonic quantum computers3–7 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 circuit operations using integrated nanophotonics: a programmable chip, operating at room temperature and interfaced with a fully automated control system. The system enables remote users to execute quantum algorithms that require 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 photon number-resolving readout on all outputs. Detection of multi-photon events with photon numbers and rates exceeding any previous programmable quantum optical demonstration is 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 similarity8. These demonstrations validate the platform as a launchpad for scaling photonic technologies for quantum information processing. A system for realizing many-photon quantum circuits is presented, comprising a programmable nanophotonic chip operating at room temperature, interfaced with a fully automated control system.

Identification of a suitable source of single photons via second-order autocorrelation function measurement within an array of thousand possible candidates is a routine, key step of any practical realization in quantum optics. Within this work, we have shown that machine learning algorithms enable high precision classification between “single” and “not single” quantum emitters based on sparse autocorrelation data measurement and require 1 min. Machine learning assisted classification, done on a sparse 1-s dataset, provides ~85% accuracy of “single”/“not single” emitter identification versus only 57% of the conventional Levenberg-Marquardt approach.

Single quantum emitters offer useful functionalities for quantum optics, but measurements of their properties are time-consuming. We demonstrate that machine learning dramatically reduces data collection time (1s), increasing the accuracy of second-order autocorrelation measurements (>90%).

The characterization of single quantum emitters is a time-consuming process. We have demonstrated that machine learning methods can dramatically reduce data collection time(90%).

Deterministic nanoassembly may enable unique integrated on-chip quantum photonic devices. Such integration requires a careful large-scale selection of nanoscale building blocks such as solid-state single-photon emitters by the means of optical characterization. Second-order autocorrelation is a cornerstone measurement that is particularly time-consuming to realize on a large scale. We have implemented supervised machine learning-based classification of quantum emitters as "single" or "not-single" based on their sparse autocorrelation data. Our method yields a classification accuracy of over 90% within an integration time of less than a second, realizing roughly a hundredfold speedup compared to the conventional, Levenberg-Marquardt approach. We anticipate that machine learning-based classification will provide a unique route to enable rapid and scalable assembly of quantum nanophotonic devices and can be directly extended to other quantum optical measurements., Comment: Adv. Quantum Technol. 2020

We report results on multiband observations from radio to gamma-rays of the two radio-loud narrow-line Seyfert 1 (NLSy1) galaxies PKS 2004-447 and J1548+3511. Both sources show a core-jet structure on parsec scale, while they are unresolved at the arcsecond scale. The high core dominance and the high variability brightness temperature make these NLSy1 galaxies good gamma-ray source candidates. Fermi-LAT detected gamma-ray emission only from PKS 2004-447, with a gamma-ray luminosity comparable to that observed in blazars. No gamma-ray emission is observed for J1548+3511. Both sources are variable in X-rays. J1548+3511 shows a hardening of the spectrum during high activity states, while PKS 2004-447 has no spectral variability. A spectral steepening likely related to the soft excess is hinted below 2 keV for J1548+3511, while the X-ray spectra of PKS 2004-447 collected by XMM-Newton in 2012 are described by a single power-law without significant soft excess. No additional absorption above the Galactic column density or the presence of an Fe line is detected in the X-ray spectra of both sources., 16 pages, 8 figures, accepted for publication in MNRAS