Abstract: Many of the features of neural networks for machine learning can naturally be mapped into the quantum optical domain by introducing the quantum optical neural network (QONN). A QONN can be performed to perform a range of quantum information processing tasks, including newly developed protocols for quantum optical state compression, reinforcement learning, black-box quantum simulation and one way quantum repeaters. A QONN can generalize from only a small set of training data onto previously unseen inputs. Simulations indicate that QONNs are a powerful design tool for quantum optical systems and, leveraging advances in integrated quantum photonics, a promising architecture for next generation quantum processors.

Abstract: An atom control architecture based on VIS-IR photonic integrated circuit (PIC) technology is characterized by (1) visible (VIS) and near-infrared (IR) wavelength operation, (2) channel counts extensible beyond 1000s of individually addressable atoms, (3) high intensity modulation extinction and (4) repeatability compatible with low gate errors, and (5) fast switching times. A 16-channel SiN-based APIC with (5.8±0.4) ns response times and <?30 dB extinction ratio at a wavelength of 780 nm. Based on a complementary metal-oxide-semiconductor (CMOS) fabrication process, this atom-control PIC (APIC) technology can be used for atomic, molecular, and optical physics and emerging applications, from quantum computers with cold atoms or ions to quantum networks with solid-state color centers. This APIC technology is especially suitable for scalable quantum information processing based on optically programmable atomic systems.

Abstract: A multiplicative analog frequency transform optical neural network (MAFT-ONN) encodes data in the frequency domain, achieves matrix-vector products in a single shot using photoelectric multiplication, and uses a single electro-optic modulator for the nonlinear activation of all neurons in each layer. Photoelectric multiplication between radio frequency (RF)-encoded optical frequency combs allows single-shot matrix-vector multiplication and nonlinear activation, leading to high throughput and ultra-low latency. This frequency-encoding scheme can be implemented with several neurons per hardware spatial mode and allows for an arbitrary number of layers to be cascaded in the analog domain. For example, a three-layer DNN can compute over four million fully analog operations and implement both a convolutional and fully connected layer.

Abstract: NetCast is an optical neural network architecture that circumvents constraints on deep neural network (DNN) inference at the edge. Many DNNs have weight matrices that are too large to run on edge processors, leading to limitations on DNN inference at the edge or bandwidth bottlenecks between the edge and server that hosts the DNN. With NetCast, a weight server stores the DNN weight matrix in local memory, modulates the weights onto different spectral channels of an optical carrier, and distributes the weights to one or more clients via optical links. Each client stores the activations, or layer inputs, for the DNN and computes the matrix-vector product of those activations with the weights from the weight server in the optical domain. This multiplication can be performed coherently by interfering the spectrally multiplexed weights with spectrally multiplexed activations or incoherently by modulating the weight signal from the weight server with the activations.

Abstract: A 1D diamond nanobeam can act as a coherent mechanical interface between spin defect centers in diamond and telecom optical modes. The nanobeam includes embedded mechanical and electric field concentrators with mechanical and optical mode volumes of Vmech/?p3 ˜10?5 and Vopt/?3 ˜10?3, respectively. With a Group IV vacancy in the concentrator, the nanobeam can operate at spin-mechanical coupling rates approaching 40 MHz with high acousto-optical couplings. This nanobeam, used in an entanglement heralding scheme, can provide high-fidelity Bell pairs between quantum repeaters. Using the mechanical interface as an intermediary between the optical and spin subsystems enables addressing the spin defect center with telecom optics, bypassing the native wavelength of the spin. As the spin is never optically excited or addressed, the device can operate at temperatures up to 40 K with no appreciable spectral diffusion, limited by thermal losses.

Abstract: A filter-based color imaging array that resolves N different colors detects only 1/Nth of the incoming light. In the thermal infrared wavelength range, filtering loss is exacerbated by the lower sensor detectivity at infrared wavelengths than at visible wavelengths. To avoid loss due to filtering, most spectral imagers use bulky optics, such as diffraction gratings or Fourier transform interferometers, to resolve different colors. Fortunately, it is possible to avoid filtering loss without bulky optics: detect light with interleaved arrays of sub-wavelength-spaced antennas tuned to different wavelengths. An optically sensitive element inside each antenna absorbs light at the antenna's resonant wavelength. Metallic slot antennas offer high efficiency, intrinsic unidirectionality, and lower cross-talk than dipole or bowtie antennas. Graphene serves at the optically active material inside each antenna because its 2D nature makes it easily adaptable to this imager architecture.

Abstract: Optical microcavity resonance measurements can have readout noise matching the fundamental limit set by thermal fluctuations in the cavity. Small-heat-capacity, wavelength-scale microcavities can be used as bolometers that bypass the limitations of other bolometer technologies. The microcavities can be implemented as photonic crystal cavities or micro-disks that are thermally coupled to strong mid-IR or LWIR absorbers, such as pyrolytic carbon columns. Each microcavity and the associated absorber(s) rest on hollow pillars that extend from a substrate and thermally isolate the cavity and the absorber(s) from the rest of the bolometer. This ensures that thermal transfer to the absorbers is predominantly from radiation as opposed to from conduction. As the absorbers absorb thermal radiation, they shift the resonance wavelength of the cavity.

Abstract: Using the Meissner effect in superconductors, demonstrated here is the capability to create an arbitrarily high magnetic flux density (also sometimes referred to as “flux squeezing”). This technique has immediate applications for numerous technologies. For example, it allows the generation of very large magnetic fields (e.g., exceeding 1 Tesla) for nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), the generation of controlled magnetic fields for advanced superconducting quantum computing devices, and/or the like. The magnetic field concentration/increased flux density approaches can be applied to both static magnetic fields (i.e., direct current (DC) magnetic fields) and time-varying magnetic fields (i.e., alternating current (AC) magnetic fields) up to microwave frequencies.

Abstract: A reflective spatial light modulator (SLM) made of an electro-optic material in a one-sided Fabry-Perot resonator can provide phase and/or amplitude modulation with fine spatial resolution at speeds over a Gigahertz. The light is confined laterally within the electro-optic material/resonator layer stack with microlenses, index perturbations, or by patterning the layer stack into a two-dimensional (2D) array of vertically oriented micropillars. Alternatively, a photonic crystal guided mode resonator can vertically and laterally confine the resonant mode. In phase-only modulation mode, each SLM pixel can produce a ? phase shift under a bias voltage below 10 V, while maintaining nearly constant reflection amplitude. This high-speed SLM can be used in a wide range of new applications, from fully tunable metasurfaces to optical computing accelerators, high-speed interconnects, true 2D phased array beam steering, beam forming, or quantum computing with cold atom arrays.

Abstract: Deep learning performance is limited by computing power, which is in turn limited by energy consumption. Optics can make neural networks faster and more efficient, but current schemes suffer from limited connectivity and the large footprint of low-loss nanophotonic devices. Our optical neural network architecture addresses these problems using homodyne detection and optical data fan-out. It is scalable to large networks without sacrificing speed or consuming too much energy. It can perform inference and training and work with both fully connected and convolutional neural-network layers. In our architecture, each neural network layer operates on inputs and weights encoded onto optical pulse amplitudes. A homodyne detector computes the vector product of the inputs and weights. The nonlinear activation function is performed electronically on the output of this linear homodyne detection step.

Abstract: An evaporatively cooled device and a system including the same. In some embodiments, the system includes an oligolayer conductive sheet; a superconductor; a tunneling barrier, between the oligolayer conductive sheet and the superconductor; and a bias circuit, configured to apply a bias voltage across the tunneling barrier, the bias voltage being less than a gap voltage of the superconductor and greater than one-half of the gap voltage of the superconductor.

Abstract: Nitrogen vacancy (NV) centers in diamond combine exceptional sensitivity with nanoscale spatial resolution by optically detected magnetic resonance (ODMR). Infrared (IR)-absorption-based readout of the NV singlet state transition can increase ODMR contrast and collection efficiency. Here, a resonant diamond metallodielectric metasurface amplifies IR absorption by concentrating the optical field near the diamond surface. This plasmonic quantum sensing metasurface (PQSM) supports plasmonic surface lattice resonances and balances field localization and sensing volume to optimize spin readout sensitivity. Combined electromagnetic and rate-equation modeling suggests a near-spin-projection-noise-limited sensitivity below 1 nT Hz?1/2 per ?m2 of sensing area using numbers for contemporary NV diamond samples and fabrication techniques.

Abstract: An ensemble of spin defect centers or other atom-like quantum systems in a solid-state host can be used as a compact alternative for an atomic clock thanks to an architecture that overcomes magnetic and temperature-induced systematics. A polariton-stabilized solid-state spin clock hybridizes a microwave resonator with a magnetic-field-insensitive spin transition within the ground state of a spin defect center (e.g., a nitrogen vacancy center in diamond). Detailed numerical and analytical modeling of this polariton-stabilized solid-state spin clock indicates a potential fractional frequency instability below 10?13 over a 1-second measurement time, assuming present-day experimental parameters. This stability is a significant improvement over the state-of-the-art in miniaturized atomic vapor clocks.

Abstract: A two-photon logic gate introduces a phase shift between two photons using a Q-switched cavity with some nonlinearity. The two-photon logic gate catches photons in and releases photons from de-coupled cavity modes in response to electronic or photonic control signals. This “catch-and-release” two-photon gate can be formed in semiconductor photonic integrated circuit (PIC) that operates at room temperature. When combined with sources, linear circuits, other logic gates, and detectors, it can be used to make a quantum computer with up to 1000 error-corrected logical qubits on a cm2 PIC, with full qubit connectivity to avoid overhead. Two-qubit gate fidelity exceeding 99% is possible with near-term technology, and scaling beyond 99.9% is possible. Two-photon logic gates are also suitable for gate-based quantum digital computing and for analog quantum computing schemes, such as adiabatic quantum computing or quantum annealing.

Abstract: Deep neural networks (DNNs) have become very popular in many areas, especially classification and prediction. However, as the number of neurons in the DNN increases to solve more complex problems, the DNN becomes limited by the latency and power consumption of existing hardware. A scalable, ultra-low latency photonic tensor processor can compute DNN layer outputs in a single shot. The processor includes free-space optics that perform passive optical copying and distribution of an input vector and integrated optoelectronics that implement passive weighting and the nonlinearity. An example of this processor classified the MNIST handwritten digit dataset (with an accuracy of 94%, which is close to the 96% ground truth accuracy). The processor can be scaled to perform near-exascale computing before hitting its fundamental throughput limit, which is set by the maximum optical bandwidth before significant loss of classification accuracy (determined experimentally).

Abstract: Component errors prevent linear photonic circuits from being scaled to large sizes. These errors can be compensated by programming the components in an order corresponding to nulling operations on a target matrix X through Givens rotations X?T†X, X?XT†. Nulling is implemented on hardware through measurements with feedback, in a way that builds up the target matrix even in the presence of hardware errors. This programming works with unknown errors and without internal sources or detectors in the circuit. Modifying the photonic circuit architecture can reduce the effect of errors still further, in some cases even rendering the hardware asymptotically perfect in the large-size limit. These modifications include adding a third directional coupler or crossing after each Mach-Zehnder interferometer in the circuit and a photonic implementation of the generalized FFT fractal.

Abstract: A hybrid quantum system performs high-fidelity quantum state transduction between a superconducting (SC) microwave qubit and the ground state spin system of a solid-state artificial atom. This transduction is mediated via an acoustic bus connected by piezoelectric transducers to the SC microwave qubit. For SC circuit qubits and diamond silicon vacancy centers in an optimized phononic cavity, the system can achieve quantum state transduction with fidelity exceeding 99% at a MHz-scale bandwidth. By combining the complementary strengths of SC circuit quantum computing and artificial atoms, the hybrid quantum system provides high-fidelity qubit gates with long-lived quantum memory, high-fidelity measurement, large qubit number, reconfigurable qubit connectivity, and high-fidelity state and gate teleportation through optical quantum networks.

Abstract: Deep neural networks (DNNs) have become very popular in many areas, especially classification and prediction. However, as the number of neurons in the DNN increases to solve more complex problems, the DNN becomes limited by the latency and power consumption of existing hardware. A scalable, ultra-low latency photonic tensor processor can compute DNN layer outputs in a single shot. The processor includes free-space optics that perform passive optical copying and distribution of an input vector and integrated optoelectronics that implement passive weighting and the nonlinearity. An example of this processor classified the MNIST handwritten digit dataset (with an accuracy of 94%, which is close to the 96% ground truth accuracy). The processor can be scaled to perform near-exascale computing before hitting its fundamental throughput limit, which is set by the maximum optical bandwidth before significant loss of classification accuracy (determined experimentally).

Abstract: It remains a challenge to generate coherent radiation in the spectral range of 0.1-10 THz (“the THz gap”), a band for applications ranging from spectroscopy to security and high-speed wireless communications. Here, we disclose how to produce coherent radiation spanning the THz gap using efficient second-harmonic generation (SHG) in low-loss dielectric structures, starting from an electronic oscillator (EO) that generates coherent radiation at frequencies of about 100 GHz. The EO is coupled to cascaded, hybrid THz-band dielectric cavities that combine (1) extreme field concentration in high-quality-factor resonators with (2) nonlinear materials enhanced by phonon resonances. These cavities convert the input radiation into higher-frequency coherent radiation at conversion efficiencies of >103%/W, making it possible to bridge the THz gap with 1 W of input power.

Abstract: Programmable photonic circuits of reconfigurable interferometers can be used to implement arbitrary operations on optical modes, providing a flexible platform for accelerating tasks in quantum simulation, signal processing, and artificial intelligence. A major obstacle to scaling up these systems is static fabrication error, where small component errors within each device accrue to produce significant errors within the circuit computation. Mitigating errors usually involves numerical optimization dependent on real-time feedback from the circuit, which can greatly limit the scalability of the hardware. Here, we present a resource-efficient, deterministic approach to correcting circuit errors by locally correcting hardware errors within individual optical gates. We apply our approach to simulations of large-scale optical neural networks and infinite impulse response filters implemented in programmable photonics, finding that they remain resilient to component error well beyond modern day process tolerances.