Optically active point defects in crystals have gained widespread attention as photonic systems that can find use in quantum information technologies. However challenges remain in the placing of individual defects at desired locations, an essential element of device fabrication. Here we report the controlled generation of single nitrogen-vacancy (NV) centres in diamond using laser writing. The use of aberration correction in the writing optics allows precise positioning of vacancies within the diamond crystal, and subsequent annealing produces single NV centres with up to 45% success probability, within about 200 nm of the desired position. Selected NV centres fabricated by this method display stable, coherent optical transitions at cryogenic temperatures, a pre-requisite for the creation of distributed quantum networks of solid-state qubits. The results illustrate the potential of laser writing as a new tool for defect engineering in quantum technologies.Comment: 21 pages including Supplementary informatio

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]

Exploring the maximum spatial resolution achievable in far‐field optical imaging, we show that applying solid immersion lenses (SIL) in stimulated emission depletion (STED) microscopy addresses single spins with a resolution down to 2.4 ± 0.3 nm and with a localization precision of 0.09 nm.

Magnonics addresses the physical properties of spin waves and utilizes them for data processing. Scalability down to atomic dimensions, operation in the GHz-to-THz frequency range, utilization of nonlinear and nonreciprocal phenomena, and compatibility with CMOS are just a few of many advantages offered by magnons. Although magnonics is still primarily positioned in the academic domain, the scientific and technological challenges of the field are being extensively investigated, and many proof-of-concept prototypes have already been realized in laboratories. This roadmap is a product of the collective work of many authors that covers versatile spin-wave computing approaches, conceptual building blocks, and underlying physical phenomena. In particular, the roadmap discusses the computation operations with Boolean digital data, unconventional approaches like neuromorphic computing, and the progress towards magnon-based quantum computing. The article is organized as a collection of sub-sections grouped into seven large thematic sections. Each sub-section is prepared by one or a group of authors and concludes with a brief description of current challenges and the outlook of further development for each research direction.