Efficiently characterising quantum systems [1-3], verifying operations of quantum devices [4-6] and validating underpinning physical models [7,8], are central challenges for the development of quantum technologies [9-11] and for our continued understanding of foundational physics [12,13]. Machine-learning enhanced by quantum simulators has been proposed as a route to improve the computational cost of performing these studies [14, 15]. Here we interface two different quantum systems through a classical channel -a silicon-photonics quantum simulator and an electron spin in a diamond nitrogen-vacancy centre -and use the former to learn the latter's Hamiltonian via Bayesian inference. We learn the salient Hamiltonian parameter with an uncertainty of approximately 10 −5 . Furthermore, an observed saturation in the learning algorithm suggests deficiencies in the underlying Hamiltonian model, which we exploit to further improve the model itself. We go on to implement an interactive version of the protocol and experimentally show its ability to characterise the operation of the quantum photonic device. This work demonstrates powerful new quantum-enhanced techniques for investigating foundational physical models and characterising quantum technologies.In science and engineering [16,17], physical systems are approximated by simplified models to allow the comprehension of their essential features. The utility of the model hinges upon the fidelity of the approximation to the actual physical system, and can be measured by the consistency of the model predictions with the real experimental data. However, predicting behaviour in the exponentially large configuration space of quantum systems is known to be intractable to classical computing machines [18,19]. A fundamental question therefore naturally arises: How can underpinning theoretical models of quantum systems be validated?To address this question, quantum Hamiltonian learning (QHL) was recently proposed [14,15] as a technique that exploits classical machine learning with quantum simulations to efficiently validate Hamiltonian models and verify the predictions of quantum systems or devices. QHL is tractable in cases in which other known methods fail because quantum simulation is exponentially faster than existing techniques [18][19][20] for simulating broad classes of complex quantum systems [21][22][23][24]. Our experimental demonstration of QHL uses a programmable silicon-photonics quantum simulator, shown in Figs. 1a,b, to learn the electron spin dynamics of a negatively charged nitrogen-vacancy (NV − ) centre in bulk diamond, shown in Figs. 1c,d. We further demonstrate an interactive QHL protocol that allows us to characterise and verify single-qubit gates using other trusted gates on the same quantum photonic device. arXiv:1703.05402v1 [quant-ph]

Spectrometry is widely used for the characterization of materials, tissues, and gases, and the need for size and cost scaling is driving the development of mini and microspectrometers. While nanophotonic devices provide narrowband filtering that can be used for spectrometry, their practical application has been hampered by the difficulty of integrating tuning and read-out structures. Here, a nano-opto-electro-mechanical system is presented where the three functionalities of transduction, actuation, and detection are integrated, resulting in a high-resolution spectrometer with a micrometer-scale footprint. The system consists of an electromechanically tunable double-membrane photonic crystal cavity with an integrated quantum dot photodiode. Using this structure, we demonstrate a resonance modulation spectroscopy technique that provides subpicometer wavelength resolution. We show its application in the measurement of narrow gas absorption lines and in the interrogation of fiber Bragg gratings. We also explore its operation as displacement-to-photocurrent transducer, demonstrating optomechanical displacement sensing with integrated photocurrent read-out.

Spectral sensing is increasingly used in applications ranging from industrial process monitoring to agriculture. Sensing is usually performed by measuring reflected or transmitted light with a spectrometer and processing the resulting spectra. However, realizing compact and mass-manufacturable spectrometers is a major challenge, particularly in the infrared spectral region where chemical information is most prominent. Here we propose a different approach to spectral sensing which dramatically simplifies the requirements on the hardware and allows the monolithic integration of the sensors. We use an array of resonant-cavity-enhanced photodetectors, each featuring a distinct spectral response in the 850-1700 nm wavelength range. We show that prediction models can be built directly using the responses of the photodetectors, despite the presence of multiple broad peaks, releasing the need for spectral reconstruction. The large etendue and responsivity allow us to demonstrate the application of an integrated near-infrared spectral sensor in relevant problems, namely milk and plastic sensing. Our results open the way to spectral sensors with minimal size, cost and complexity for industrial and consumer applications.

Quantum photonic integrated circuits (QPICs) on a GaAs platform allow the generation, manipulation, routing, and detection of non-classical states of light, which could pave the way for quantum information processing based on photons. In this article, the prototype of a multi-functional QPIC is presented together with our recent achievements in terms of nanofabrication and integration of each component of the circuit. Photons are generated by excited InAs quantum dots (QDs) and routed through ridge waveguides towards photonic crystal cavities acting as filters. The filters with a transmission of 20% and free spectral range ≥66 nm are able to select a single excitonic line out of the complex emission spectra of the QDs. The QD luminescence can be measured by on-chip superconducting single photon detectors made of niobium nitride (NbN) nanowires patterned on top of a suspended nanobeam, reaching a device quantum efficiency up to 28%. Moreover, two electrically independent detectors are integrated on top of the same nanobeam, resulting in a very compact autocorrelator for on-chip g (2) (τ) measurements.

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