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In the last decade, remarkable advances in integrated photonic technologies have enabled table-top experiments and instrumentation to be scaled down to compact chips with significant reduction in size, weight, power consumption, and cost. Here, we demonstrate an integrated continuously tunable laser in a heterogeneous gallium arsenide-on-silicon nitride (GaAs-on-SiN) platform that emits in the far-red radiation spectrum near 780 nm, with 20 nm tuning range, <6 kHz intrinsic linewidth, and a >40 dB side-mode suppression ratio. The GaAs optical gain regions are heterogeneously integrated with low-loss SiN waveguides. The narrow linewidth lasing is achieved with an extended cavity consisting of a resonator-based Vernier mirror and a phase shifter. Utilizing synchronous tuning of the integrated heaters, we show mode-hop-free wavelength tuning over a range larger than 100 GHz (200 pm). To demonstrate the potential of the device, we investigate two illustrative applications: (i) the linear characterization of a silicon nitride microresonator designed for entangled-photon pair generation, and (ii) the absorption spectroscopy and locking to the D1 and D2 transition lines of 87-Rb. The performance of the proposed integrated laser holds promise for a broader spectrum of both classical and quantum applications in the visible range, encompassing communication, control, sensing, and computing.

This work culminates in a demonstration of an alternative Frequency Domain Multiplexing (FDM) scheme for Superconducting Nanowire Single-Photon Detectors (SNSPDs) using the Kinetic inductance Parametric UP-converter (KPUP) made out of NbTiN. There are multiple multiplexing architectures for SNSPDs that are already in use, but FDM could prove superior in applications where the operational bias currents are very low, especially for mid- and far-infrared SNSPDs. Previous FDM schemes integrated the SNSPD within the resonator, while in this work we use an external resonator, which gives more flexibility to optimize the SNSPD architecture. The KPUP is a DC-biased superconducting resonator in which a nanowire is used as its inductive element to enable sensitivity to current perturbations. When coupled to an SNSPD, the KPUP can be used to read out current pulses on the few $\mu$A scale. The KPUP is made out of NbTiN, which has high non-linear kinetic inductance for increased sensitivity at higher current bias and high operating temperature. Meanwhile, the SNSPD is made from WSi, which is a popular material for broadband SNSPDs. To read out the KPUP and SNSPD array, a software-defined radio platform and a graphics processing unit are used. Frequency Domain Multiplexed SNSPDs have applications in astronomy, remote sensing, exoplanet science, dark matter detection, and quantum sensing.

The Conditional Markov Chain Search (CMCS) is a framework for automated design of metaheuristics for discrete combinatorial optimisation problems. Given a set of algorithmic components such as hill climbers and mutations, CMCS decides in which order to apply those components. The decisions are dictated by the CMCS configuration that can be learnt offline. CMCS does not have an acceptance criterion; any moves are accepted by the framework. As a result, it is particularly good in exploration but is not as good at exploitation. In this study, we explore several extensions of the framework to improve its exploitation abilities. To perform a computational study, we applied the framework to the three-index assignment problem. The results of our experiments showed that a two-stage CMCS is indeed superior to a single-stage CMCS., Comment: 14 pages

Superconducting nanowire single-photon detectors (SNSPDs) with high timing resolution and low background counts in the mid infrared (MIR) have the potential to open up numerous opportunities in fields such as exoplanet searches, direct dark matter detection, physical chemistry, and remote sensing. One challenge in pushing SNSPD sensitivity to the MIR is a decrease in the signal-to-noise ratio (SNR) of the readout signal as the critical currents become increasingly smaller. We overcome this trade-off with a new device architecture that employs impedance matching tapers and superconducting nanowire avalanche photodetectors to demonstrate increased SNR while maintaining saturated internal detection efficiency at 7.4 {\mu}m and getting close to saturation at 10.6 {\mu}m. This work provides a novel platform for pushing SNSPD sensitivity to longer wavelengths while improving the scalability of the readout electronics.

We report the formation of Mn-rich regions at the interface of Co2FexMn1-xSi thin films grown on GaAs substrates by molecular beam epitaxy (MBE). Scanning transmission electron microscopy (STEM) with electron energy loss (EEL) spectrum imaging reveals that each interfacial region: (1) is 1-2 nm wide, (2) occurs irrespective of the Fe/Mn composition ratio and in both Co-rich and Co-poor films, and (3) displaces both Co and Fe indiscriminately. We also observe a Mn-depleted region in each film directly above each Mn-rich interfacial layer, roughly 3 nm in width in the x = 0 and x = 0.3 films, and 1 nm in the x = 0.7 (less Mn) film. We posit that growth energetics favor Mn diffusion to the interface even when there is no significant Ga interdiffusion into the epitaxial film. Element-specific X-ray magnetic circular dichroism (XMCD) measurements show larger Co, Fe, and Mn orbital to spin magnetic moment ratios compared to bulk values across the Co2FexMn1-xSi compositional range. The values lie between reported values for pure bulk and nanostructured Co, Fe, and Mn materials, corroborating the non-uniform, layered nature of the material on the nanoscale. Finally, SQUID magnetometry demonstrates that the films deviate from the Slater-Pauling rule for uniform films of both the expected and the measured composition. The results inform a need for care and increased scrutiny when forming Mn-based magnetic thin films on III-V semiconductors like GaAs, particularly when films are on the order of 5 nm or when interface composition is critical to spin transport or other device applications.

We report on the extension of the spectral sensitivity of superconducting nanowire single-photon detectors to a wavelength of 29 $\mu$m. This represents the first demonstration of a time correlated single-photon counting detector at these long infrared wavelengths. We achieve saturated internal detection efficiency from 10 to 29 $\mu$m, whilst maintaining dark count rates below 0.1 counts per second. Extension of superconducting nanowire single-photon detectors to this spectral range provides low noise and high timing resolution photon counting detection, effectively providing a new class of single-photon sensitive detector for these wavelengths. These detectors are important for applications such as exoplanet spectroscopy, infrared astrophysics, physical chemistry, remote sensing and direct dark-matter detection., Comment: 15 pages, 5 figures

Surface acoustic waves (SAWs) are a versatile tool for coherently interfacing with a variety of solid-state quantum systems spanning microwave to optical frequencies, including superconducting qubits, spins, and quantum emitters. Here, we demonstrate SAW cavity optomechanics with quantum emitters in 2D materials, specifically monolayer WSe$_2$, on a planar lithium niobate SAW resonator driven by superconducting electronics. Using steady-state photoluminescence spectroscopy and time-resolved single-photon counting, we map the temporal dynamics of modulated 2D emitters under coupling to different SAW cavity modes, showing energy-level splitting consistent with deformation potential coupling of 30 meV/%. We leverage the large anisotropic strain from the SAW to modulate the excitonic fine-structure splitting on a nanosecond timescale, which may find applications for on-demand entangled photon-pair generation from 2D materials. Cavity optomechanics with SAWs and 2D quantum emitters provides opportunities for compact sensors and quantum electro-optomechanics in a multi-functional integrated platform that combines phononic, optical, and superconducting electronic quantum systems.

Deep learning networks have demonstrated high performance in a large variety of applications, such as image classification, speech recognition, and natural language processing. However, there exists a major vulnerability exploited by the use of adversarial attacks. An adversarial attack imputes images by altering the input image very slightly, making it nearly undetectable to the naked eye, but results in a very different classification by the network. This paper explores the projected gradient descent (PGD) attack and the Adaptive Mask Segmentation Attack (ASMA) on the image segmentation DeepLabV3 model using two types of architectures: MobileNetV3 and ResNet50, It was found that PGD was very consistent in changing the segmentation to be its target while the generalization of ASMA to a multiclass target was not as effective. The existence of such attack however puts all of image classification deep learning networks in danger of exploitation., Comment: 10 pages, 6 figures

Optically active defects in 2D materials, such as hexagonal boron nitride (hBN) and transition metal dichalcogenides (TMDs), are an attractive class of single-photon emitters with high brightness, room-temperature operation, site-specific engineering of emitter arrays, and tunability with external strain and electric fields. In this work, we demonstrate a novel approach to precisely align and embed hBN and TMDs within background-free silicon nitride microring resonators. Through the Purcell effect, high-purity hBN emitters exhibit a cavity-enhanced spectral coupling efficiency up to $46\%$ at room temperature, which exceeds the theoretical limit for cavity-free waveguide-emitter coupling and previous demonstrations by nearly an order-of-magnitude. The devices are fabricated with a CMOS-compatible process and exhibit no degradation of the 2D material optical properties, robustness to thermal annealing, and 100 nm positioning accuracy of quantum emitters within single-mode waveguides, opening a path for scalable quantum photonic chips with on-demand single-photon sources.

The dual tasks of quantum Hamiltonian learning and quantum Gibbs sampling are relevant to many important problems in physics and chemistry. In the low temperature regime, algorithms for these tasks often suffer from intractabilities, for example from poor sample- or time-complexity. With the aim of addressing such intractabilities, we introduce a generalization of quantum natural gradient descent to parameterized mixed states, as well as provide a robust first-order approximating algorithm, Quantum-Probabilistic Mirror Descent. We prove data sample efficiency for the dual tasks using tools from information geometry and quantum metrology, thus generalizing the seminal result of classical Fisher efficiency to a variational quantum algorithm for the first time. Our approaches extend previously sample-efficient techniques to allow for flexibility in model choice, including to spectrally-decomposed models like Quantum Hamiltonian-Based Models, which may circumvent intractable time complexities. Our first-order algorithm is derived using a novel quantum generalization of the classical mirror descent duality. Both results require a special choice of metric, namely, the Bogoliubov-Kubo-Mori metric. To test our proposed algorithms numerically, we compare their performance to existing baselines on the task of quantum Gibbs sampling for the transverse field Ising model. Finally, we propose an initialization strategy leveraging geometric locality for the modelling of sequences of states such as those arising from quantum-stochastic processes. We demonstrate its effectiveness empirically for both real and imaginary time evolution while defining a broader class of potential applications., Comment: 24 + 49 pages, 5 + 4 figures

When making everyday decisions, people are guided by their conscience, an internal sense of right and wrong. By contrast, artificial agents are currently not endowed with a moral sense. As a consequence, they may learn to behave immorally when trained on environments that ignore moral concerns, such as violent video games. With the advent of generally capable agents that pretrain on many environments, it will become necessary to mitigate inherited biases from environments that teach immoral behavior. To facilitate the development of agents that avoid causing wanton harm, we introduce Jiminy Cricket, an environment suite of 25 text-based adventure games with thousands of diverse, morally salient scenarios. By annotating every possible game state, the Jiminy Cricket environments robustly evaluate whether agents can act morally while maximizing reward. Using models with commonsense moral knowledge, we create an elementary artificial conscience that assesses and guides agents. In extensive experiments, we find that the artificial conscience approach can steer agents towards moral behavior without sacrificing performance., Comment: NeurIPS 2021. Environments available here https://github.com/hendrycks/jiminy-cricket

Continuous quantum error correction has been found to have certain advantages over discrete quantum error correction, such as a reduction in hardware resources and the elimination of error mechanisms introduced by having entangling gates and ancilla qubits. We propose a machine learning algorithm for continuous quantum error correction that is based on the use of a recurrent neural network to identify bit-flip errors from continuous noisy syndrome measurements. The algorithm is designed to operate on measurement signals deviating from the ideal behavior in which the mean value corresponds to a code syndrome value and the measurement has white noise. We analyze continuous measurements taken from a superconducting architecture using three transmon qubits to identify three significant practical examples of non-ideal behavior, namely auto-correlation at temporal short lags, transient syndrome dynamics after each bit-flip, and drift in the steady-state syndrome values over the course of many experiments. Based on these real-world imperfections, we generate synthetic measurement signals from which to train the recurrent neural network, and then test its proficiency when implementing active error correction, comparing this with a traditional double threshold scheme and a discrete Bayesian classifier. The results show that our machine learning protocol is able to outperform the double threshold protocol across all tests, achieving a final state fidelity comparable to the discrete Bayesian classifier., Comment: 21 pages, 12 figures

Microtubules are hollow cylindrical polymers composed of the highly negatively-charged (~23e), high dipole moment (1750 D) protein a,b-tubulin. While the roles of microtubules in chromosomal segregation, macromolecular transport and cell migration are relatively well-understood, studies on the electrical properties of microtubules have only recently gained strong interest. Here, we show that while microtubules at physiological concentrations increase solution capacitance, free tubulin has no appreciable effect. For a particular microtubule concentration, we were able to quantify these effects by determining the capacitance and resistance of a single 20 um-long microtubule to be 1.86 x 10^(-12) F and 1.07 x 10^12 Ohms respectively. Further, we observed a decrease in electrical resistance of solution, with charge transport peaking between 20-60 Hz in the presence of microtubules, consistent with recent findings that microtubules exhibit electric oscillations at such low frequencies. Our results show that in addition to macromolecular transport, microtubules also act as charge-storage devices through counterionic condensation across a broad frequency spectrum. We conclude with a hypothesis of an electrically-tunable cytoskeleton where the dielectric properties of tubulin are polymerization-state dependent., Comment: In submission

Certain two-dimensional (2D) materials exhibit intriguing properties such as valley polarization, ferroelectricity, superconductivity and charge-density waves. Many of these materials can be manually assembled into atomic-scale multilayer devices under ambient conditions, owing to their exceptional chemical stability. Efforts have been made to add a magnetic degree of freedom to these 2D materials via defects, but only local magnetism has been achieved. Only with the recent discoveries of 2D materials supporting intrinsic ferromagnetism have stacked spintronic devices become realistic. Assembling 2D multilayer devices with these ferromagnets under ambient conditions remains challenging due to their sensitivity to environmental degradation, and magnetic order at room temperature is rare in van der Waals materials. Here, we report the growth of air-stable ultra-thin epsilon-phase iron oxide crystals that exhibit magnetic order at room temperature. These crystals require no passivation and can be prepared in large quantity by cost-effective chemical vapor deposition (CVD). We find that the epsilon phase, which is energetically unfavorable and does not form in bulk, can be easily made in 2D down to a seven unit-cell thickness. Magneto-optical Kerr effect (MOKE) magnetometry of individual crystals shows that even at this ultrathin limit the epsilon phase exhibits robust magnetism with coercive fields of hundreds of mT. These measurements highlight the advantages of ultrathin iron oxide as a promising candidate towards air-stable 2D magnetism and integration into 2D spintronic devices.

A distinguishing feature of spin accumulation in ferromagnet-semiconductor devices is precession of the non-equilibrium spin population of the semiconductor in a magnetic field. This is the basis for detection techniques such as the Hanle effect, but these approaches become less effective as the spin lifetime in the semiconductor decreases. For this reason, no electrical Hanle measurement has been demonstrated in GaAs at room temperature. We show here that by forcing the magnetization in the ferromagnet (the spin injector and detector) to precess at the ferromagnetic resonance frequency, an electrically generated spin accumulation can be detected from 30 to 300 K. At low temperatures, the distinct Larmor precession of the spin accumulation in the semiconductor can be detected by ferromagnetic resonance in an oblique field. We verify the effectiveness of this new spin detection technique by comparing the injection bias and temperature dependence of the measured spin signal to the results obtained using traditional methods. We further show that this new approach enables a measurement of short spin lifetimes (< 100 psec), a regime that is not accessible in semiconductors using traditional Hanle techniques., Comment: 4 figures