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© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. Storing, proceßing, and learning from data is a central task in both industrial practice and modern science. Recent advances in modern statistical learning, particularly Deep Neural Networks (DNNs), have given record breaking performance on tasks in game playing,1, 2 natural language proceßing,3 computer vision,4 computational biology,5, 6 and many others. The rapid growth of the field has been driven by an increase in the amount of public datasets,7 improvements to algorithms,8 and a substantial growth in computing power.9 In order to perform well on these tasks networks have had to grow in size, learning more complicated statistical features. The training and deployment of these large neural networks has spurred the creation of many neural network accelerators to aid in the computation of these networks.10-12 Existing general purpose computing devices such as CPUs and GPUs are limited both by thermal dißipation per unit area and yield aßociated with large chips.13, 14 The design of Application Specific Integrated circuits (ASICs) has aided in decreasing the energy consumption per workload substantially by limiting the supported operations on chip. An example of this is the first generation tensor proceßing unit (TPU)15 which is able to perform the inference of large convolutional neural networks in datacenter in <10ms with an idle power of 28W and an workload power of 40W. It may seen counterintuitive then that the limiting factor for the implementation of DNNs is not computation, but rather the energy and bandwidth aßociated with reading and writing data from memory as well as the energy cost of moving data inside of the ASIC.15, 16 Several emerging technologies, such as in-memory computing,17 memristive croßbar arrays18 promise increased performance, but these emerging architectures suffer from calibration ißues and limited accuracy.19 Photonics as a field has had tremendous succeß

© 2020 American Chemical Society. The simultaneous imaging of magnetic fields and temperature (MT) is important in a range of applications, including studies of carrier transport and semiconductor device characterization. Techniques exist for separately measuring temperature (e.g., infrared (IR) microscopy, micro-Raman spectroscopy, and thermo-reflectance microscopy) and magnetic fields (e.g., scanning probe magnetic force microscopy and superconducting quantum interference devices). However, these techniques cannot measure magnetic fields and temperature simultaneously. Here, we use the exceptional temperature and magnetic field sensitivity of nitrogen vacancy (NV) spins in conformally coated nanodiamonds to realize simultaneous wide-field MT imaging at the device level. Our "quantum conformally attached thermo-magnetic"(Q-CAT) imaging enables (i) wide-field, high-frame rate imaging (100-1000 Hz); (ii) high sensitivity; and (iii) compatibility with standard microscopes. We apply this technique to study the industrially important problem of characterizing multifinger gallium nitride high-electron mobility transistors (GaN HEMTs). We spatially and temporally resolve the electric current distribution and resulting temperature rise, elucidating functional device behavior at the microscopic level. The general applicability of Q-CAT imaging serves as an important tool for understanding complex MT phenomena in material science, device physics, and related fields.

We propose and optimize a vertically-loaded diamond microdisk resonator (VLDMoRt) coupled to a nitrogen-vacancy (NV) center in diamond for efficient collection of zero-phonon-line emission into low numerical aperture (NA) free-space modes. The VLDMoRt achieves a Purcell enhancement of 172 with 39% of the emitted light collected within a NA of 0.6, leading to a total external spin-photon collection efficiency of 0.33. As the design is compatible with established nanofabrication techniques and couples to low-NA modes accessible by cryogenic free-space optical systems, it is a promising platform for efficient spin-photon interfaces based on diamond quantum emitters.

© 2020, The Author(s), under exclusive licence to Springer Nature Limited. Nonlinear nanophotonics leverages engineered nanostructures to funnel light into small volumes and intensify nonlinear optical processes with spectral and spatial control. Owing to its intrinsically large and electrically tunable nonlinear optical response, graphene is an especially promising nanomaterial for nonlinear optoelectronic applications. Here we report on exceptionally strong optical nonlinearities in graphene–insulator–metal heterostructures, which demonstrate an enhancement by three orders of magnitude in the third-harmonic signal compared with that of bare graphene. Furthermore, by increasing the graphene Fermi energy through an external gate voltage, we find that graphene plasmons mediate the optical nonlinearity and modify the third-harmonic signal. Our findings show that graphene–insulator–metal is a promising heterostructure for optically controlled and electrically tunable nano-optoelectronic components.

The great promise of quantum computers comes with the dual challenges of building them and finding their useful applications. We argue that these two challenges should be considered together, by codesigning full-stack quantum computer systems along with their applications in order to hasten their development and potential for scientific discovery. In this context, we identify scientific and community needs, opportunities, a sampling of a few use case studies, and significant challenges for the development of quantum computers for science over the next 2–10 years.

© 2020, The Author(s). The inability of conventional electronic architectures to efficiently solve large combinatorial problems motivates the development of novel computational hardware. There has been much effort toward developing application-specific hardware across many different fields of engineering, such as integrated circuits, memristors, and photonics. However, unleashing the potential of such architectures requires the development of algorithms which optimally exploit their fundamental properties. Here, we present the Photonic Recurrent Ising Sampler (PRIS), a heuristic method tailored for parallel architectures allowing fast and efficient sampling from distributions of arbitrary Ising problems. Since the PRIS relies on vector-to-fixed matrix multiplications, we suggest the implementation of the PRIS in photonic parallel networks, which realize these operations at an unprecedented speed. The PRIS provides sample solutions to the ground state of Ising models, by converging in probability to their associated Gibbs distribution. The PRIS also relies on intrinsic dynamic noise and eigenvalue dropout to find ground states more efficiently. Our work suggests speedups in heuristic methods via photonic implementations of the PRIS.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement Conventional computing architectures have no known efficient algorithms for combinatorial optimization tasks such as the Ising problem, which requires finding the ground state spin configuration of an arbitrary Ising graph. Physical Ising machines have recently been developed as an alternative to conventional exact and heuristic solvers; however, these machines typically suffer from decreased ground state convergence probability or universality for high edge-density graphs or arbitrary graph weights, respectively. We experimentally demonstrate a proof-of-principle integrated nanophotonic recurrent Ising sampler (INPRIS), using a hybrid scheme combining electronics and silicon-on-insulator photonics, that is capable of converging to the ground state of various four-spin graphs with high probability. The INPRIS results indicate that noise may be used as a resource to speed up the ground state search and to explore larger regions of the phase space, thus allowing one to probe noise-dependent physical observables. Since the recurrent photonic transformation that our machine imparts is a fixed function of the graph problem and therefore compatible with optoelectronic architectures that support GHz clock rates (such as passive or non-volatile photonic circuits that do not require reprogramming at each iteration), this work suggests the potential for future systems that could achieve orders-of-magnitude speedups in exploring the solution space of combinatorially hard problems.

Quantum emitters in diamond are leading optically accessible solid-state qubits. Among these, Group IV-vacancy defect centers have attracted great interest as coherent and stable optical interfaces to long-lived spin states. Theory indicates that their inversion symmetry provides first-order insensitivity to stray electric fields, a common limitation for optical coherence in any host material. Here we experimentally quantify this electric field dependence via an external electric field applied to individual tin-vacancy (SnV) centers in diamond. These measurements reveal that the permanent electric dipole moment and polarizability are at least 4 orders of magnitude smaller than for the diamond nitrogen vacancy (NV) centers, representing the first direct measurement of the inversion symmetry protection of a Group IV defect in diamond. Moreover, we show that by modulating the electric-field-induced dipole we can use the SnV as a nanoscale probe of local electric field noise, and we employ this technique to highlight the effect of spectral diffusion on the SnV.

Networking superconducting quantum computers is a longstanding challenge in quantum science. The typical approach has been to cascade transducers: converting to optical frequencies at the transmitter and to microwave frequencies at the receiver. However, the small microwave-optical coupling and added noise have proven formidable obstacles. Instead, we propose optical networking via heralding end-to-end entanglement with one detected photon and teleportation. This new protocol can be implemented on standard transduction hardware while providing significant performance improvements over transduction. In contrast to cascaded direct transduction, our scheme absorbs the low optical-microwave coupling efficiency into the heralding step, thus breaking the rate-fidelity trade-off. Moreover, this technique unifies and simplifies entanglement generation between superconducting devices and other physical modalities in quantum networks.

Overcoming poor readout is an increasingly urgent challenge for devices based on solid-state spin defects, particularly given their rapid adoption in quantum sensing, quantum information, and tests of fundamental physics. However, in spite of experimental progress in specific systems, solid-state spin sensors still lack a universal, high-fidelity readout technique. Here we demonstrate high-fidelity, room-temperature readout of an ensemble of nitrogen-vacancy centers via strong coupling to a dielectric microwave cavity, building on similar techniques commonly applied in cryogenic circuit cavity quantum electrodynamics. This strong collective interaction allows the spin ensemble’s microwave transition to be probed directly, thereby overcoming the optical photon shot noise limitations of conventional fluorescence readout. Applying this technique to magnetometry, we show magnetic sensitivity approaching the Johnson–Nyquist noise limit of the system. Our results pave a clear path to achieve unity readout fidelity of solid-state spin sensors through increased ensemble size, reduced spin-resonance linewidth, or improved cavity quality factor.

© 2019 by the Authors. Physical annealing systems provide heuristic approaches to solving combinatorial optimization problems. Here, we benchmark two types of annealing machines-a quantum annealer built by D-Wave Systems and measurementfeedback coherent Ising machines (CIMs) based on optical parametric oscillators-on two problem classes, the Sherrington-Kirkpatrick (SK) model and MAX-CUT. The D-Wave quantum annealer outperforms the CIMs on MAX-CUT on cubic graphs. On denser problems, however, we observe an exponential penalty for the quantum annealer [exp(-aDWN2)] relative to CIMs [exp(-aCIMN)] for fixed anneal times, both on the SK model and on 50% edge density MAX-CUT. This leads to a several orders of magnitude time-to-solution difference for instances with over 50 vertices. An optimal-annealing time analysis is also consistent with a substantial projected performance difference. The difference in performance between the sparsely connected D-Wave machine and the fully-connected CIMs provides strong experimental support for efforts to increase the connectivity of quantum annealers.