Integrated nano-opto-electro-mechanical sensor for spectrometry and nanometrology

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

Learning nitrogen-vacancy electron spin dynamics on a silicon quantum photonic simulator

We present the experimental demonstration of quantum Hamiltonian learning. Using an integrated silicon-photonics quantum simulator with the classical machine learning technique, we successfully learn the Hamiltonian dynamics of a diamond nitrogen-vacancy center's electron ground-state spin

Experimental quantum hamiltonian learning using a silicon photonic chip and a nitrogen-vacancy electron spin in diamond

Summary form only given. The efficient characterization and validation of the underlying model of a quantum physical system is a central challenge in the development of quantum devices and for our understanding of foundational quantum physics. However, the impossibility to efficiently predict the behaviour of complex quantum models on classical machines makes this challenge to be intractable to classical approaches. Quantum Hamiltonian Learning (QHL) [1, 2] combines the capabilities of quantum information processing and classical machine learning to allow the efficient characterisation of the model of quantum systems. In QHL the behaviour of a quantum Hamiltonian model is efficiently predicted by a quantum simulator, and the predictions are contrasted with the data obtained from the quantum system to infer the system Hamiltonian via Bayesian methods

Experimental Quantum Hamiltonian Learning

Efficiently characterising quantum systems, verifying operations of quantum devices and validating underpinning physical models, are central challenges for the development of quantum technologies and for our continued understanding of foundational physics. Machine-learning enhanced by quantum simulators has been proposed as a route to improve the computational cost of performing these studies. 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

Defect engineering in few-layer black phosphorus for tunable and photostable infrared emission

The control of defect states is becoming a powerful approach to tune two-dimensional materials. Black phosphorus (BP) is a layered material that offers opportunities in infrared optoelectronics. Its band gap depends strongly on the number of layers and covers wavelengths from 720 to 4000 nm from monolayer to bulk, but only in discrete steps and suffering from poor photostability. Here, we demonstrate tunable and stable infrared emission from defect states in few-layer BP. First, we demonstrate a continuous blue shift of the main photoluminescence peak under laser exposure in air due to the creation of crystal defects during photo-oxidation. The tunable emission spectrum continuously bridges the discrete near-infrared energies of few-layer BP for a decreasing number of layers. Second, using plasma-enhanced encapsulation, we report the creation and protection of defects with peak emission energy between bilayer and trilayer BP. The emission is photostable and has an efficiency comparable to that of pristine layers while retaining the strong polarization anisotropy characteristic of BP. Our results put forward defect engineering in few-layer BP as a flexible strategy for stable and widely tunable infrared sources and detectors in integrated spectrometers and hyperspectral sensors

Multimode photonic molecules for advanced force sensing

\u3cp\u3eWe propose a force sensor, with optical detection, based on a reconfigurable multicavity photonic molecule distributed over two parallel photonic crystal membranes. The system spectral behaviour is described with an analytical model based on coupled mode theory and validated by finite difference time domain simulations. The deformation of the upper photonic crystal membrane, due to a localized vertical force, is monitored by the relative spectral positions of the photonic molecule resonances. The proposed system can act both as force sensor, with pico-newton sensitivity, able to identify the position where the force is applied, and as torque sensor able to measure the torsion of the membrane along two perpendicular directions.\u3c/p\u3

Integrated multi-pixel near-infrared spectral sensor

An integrated semiconductor spectral sensor is demonstrated. It is based on an array of resonant- cavity-enhanced photodetectors covering the short-wavelength infrared region. Its robustness and small footprint make this device suitable for on-site spectral sensing applications

Mechanical and Electric Control of Photonic Modes in Random Dielectrics

Random dielectrics defines a class of non‐absorbing materials where the index of refraction is randomly arranged in space. Whenever the transport mean free path is sufficiently small, light can be confined in modes with very small volume. Random photonic modes have been investigated for their basic physical insights, such as Anderson localization, and recently several applications have been envisioned in the field of renewable energies, telecommunications, and quantum electrodynamics. An advantage for optoelectronics and quantum source integration offered by random systems is their high density of photonic modes, which span a large range of spectral resonances and spatial distributions, thus increasing the probability to match randomly distributed emitters. Conversely, the main disadvantage is the lack of deterministic engineering of one or more of the many random photonic modes achieved. This issue is solved by demonstrating the capability to electrically and mechanically control the random modes at telecom wavelengths in a 2D double membrane system. Very large and reversible mode tuning (up to 50 nm), both toward shorter or longer wavelength, is obtained for random modes with modal volumes of the order of few tens of (λ/n)3