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We describe and model a non-cryogenic optical detector designed to count incident photons with metrological accuracy. Our design consists of a semiconductor device operating at −10 °C and is predicted to resolve pulses of up to 10 photons with an error rate of 2% in the input number of photons. We present an estimate of the overall device performance using a combination of estimates and simulations of optical loss, discrete electron loss and noise, and electronic noise.

Recent progress in building large-scale quantum devices for exploring quantum computing and simulation has relied upon effective tools for achieving and maintaining good experimental parameters, i.e., tuning up devices. In many cases, including quantum dot-based architectures, the parameter space grows substantially with the number of qubits, and may become a limit to scalability. Fortunately, machine learning techniques for pattern recognition and image classification, using so-called deep neural networks, have shown surprising successes for computer-aided understanding of complex systems. We propose a new paradigm for fully automated experimental initialization through a closed-loop system relying on machine learning and optimization techniques. We use deep convolutional neural networks to characterize states and charge configurations of semiconductor quantum dot arrays when only measurements of a current−voltage characteristic of transport are available. For simplicity, we model a semiconductor nanowire connected to leads and capacitively coupled to depletion gates using the Thomas−Fermi approximation and Coulomb blockade physics. We then generate labeled training data for the neural networks, and find at least 90 % accuracy for charge and state identification for single and double dots. Using these characterization networks, we can then optimize the parameter space to achieve a desired configuration of the array, a technique we call “auto-tuning”. Finally, we show how such techniques can be implemented in an experimental setting by applying our approach to an experimental dataset, and outline further problems in this domain, from using charge sensing data to extensions to full one- and two-dimensional arrays, that can be tackled with machine learning.Machine learning: neural networks can learn to control quantum dotsA machine learning algorithm connected to a set of quantum dots can automatically set them into the desired state. A group led by Jake Taylor at the National Institute of Standards and Technology with collaborators from the University of Maryland and India developed an approach based on convolutional neural networks which is able to “navigate” the huge space of parameters that characterize a complex, quantum system with neither human guidance nor reliance on a detailed description of the device. Instead they simulated thousands of hypothetical experiments and used the generated data to “train” the machine, which learned both to infer the internal charge state of the dots from their current-voltage characteristics, and to auto-tune them to a desired state. The method could be generalized to other platforms, such as ion traps or superconducting qubits.

Single-walled carbon nanotubes (CNTs) emit heat when they absorb energy from near-infrared (NIR) light. Tissue is relatively transparent to NIR, which suggests that targeting CNTs to tumor cells, followed by noninvasive exposure to NIR light, will ablate tumors within the range of NIR. In this study, we demonstrate the specific binding of antibody-coupled CNTs to tumor cells in vitro, followed by their highly specific ablation with NIR light. Biotinylated polar lipids were used to prepare stable, biocompatible, noncytotoxic CNT dispersions that were then attached to one of two different neutralite avidin-derivatized mAbs directed against either human CD22 or CD25. CD22⁺CD25⁻ Daudi cells bound only CNTs coupled to the anti-CD22 mAb; CD22⁻CD25⁺ activated peripheral blood mononuclear cells bound only to the CNTs coupled to the anti-CD25 mAb. Most importantly, only the specifically targeted cells were killed after exposure to NIR light.

Charge noise can be detrimental to the operation of quantum dot (QD) based semiconductor qubits. We study the low-frequency charge noise by charge offset drift measurements for Si-MOS devices with intentionally implanted donors near the QDs. We show that the MOS system exhibits non-equilibrium drift characteristics, in the form of transients and discrete jumps, that are not dependent on the properties of the donor implants. The equilibrium charge noise indicates a 1/ f noise dependence, and a noise strength as low as 1 μ eV / Hz , comparable to that reported in more model GaAs and Si/SiGe systems (which have also not been implanted). We demonstrate that implanted qubits, therefore, can be fabricated without detrimental effects on long-term drift or 1/ f noise for devices with less than 50 implanted donors near the qubit.

Rapid progress in semiconductor spin qubits has enabled experimental demonstrations of a two-qubit logic gate. Understanding spin decoherence in a two-qubit logic gate is necessary for optimal qubit operation. We study spin decoherence due to 1/f charge noise for two electrons in a double quantum dot used for a two-qubit controlled-phase gate. In contrast to the usual belief, spin decoherence can be dominated by the tunneling noise from 1/f charge noise instead of the detuning noise. Tunneling noise can dominate because the effect of tunneling noise on the spin qubit is first order in the charge admixture; while the effect of the detuning noise is only second order. The different orders of contributions result in different detuning dependence of the decoherence, which provides a way to identify the noise source. We find that decoherence in a recent two-qubit experiment was dominated by the tunneling noise from 1/f charge noise. The results illustrate the importance of considering tunneling noise to design optimal operation of spin qubits.

Aluminum oxide ( AlO x )-based single-electron transistors (SETs) fabricated in ultra-high vacuum (UHV) chambers using in situ plasma oxidation show excellent stabilities over more than a week, enabling applications as tunnel barriers, capacitor dielectrics or gate insulators in close proximity to qubit devices. Historically, AlO x -based SETs exhibit time instabilities due to charge defect rearrangements and defects in AlO x often dominate the loss mechanisms in superconducting quantum computation. To characterize the charge offset stability of our AlO x -based devices, we fabricate SETs with sub-1 e charge sensitivity and utilize charge offset drift measurements (measuring voltage shifts in the SET control curve). The charge offset drift ( Δ Q 0 ) measured from the plasma oxidized AlO x SETs in this work is remarkably reduced (best Δ Q 0 = 0.13 e ± 0.01 e over ≈ 7.6 days and no observation of Δ Q 0 exceeding 1 e ), compared to the results of conventionally fabricated AlO x tunnel barriers in previous studies (best Δ Q 0 = 0.43 e ± 0.007 e over ≈ 9 days and most Δ Q 0 ≥ 1 e within one day). We attribute this improvement primarily to using plasma oxidation, which forms the tunnel barrier with fewer two-level system (TLS) defects, and secondarily to fabricating the devices entirely within a UHV system.

We describe a new instrument that forms the core of a long-term high contrast imaging program at the 200 inch (5 m) Hale Telescope at Palomar Observatory. The primary scientific thrust is to obtain images and low-resolution spectroscopy of brown dwarfs and young exoplanets of several Jupiter masses in the vicinity of stars within 50 pc of the Sun. The instrument is a microlens-based integral field spectrograph integrated with a diffraction-limited, apodized-pupil Lyot coronagraph. The entire combination is mounted behind the Palomar adaptive optics (AO) system. The spectrograph obtains imaging in 23 channels across the J J and H H bands (1.06–1.78 μm). The image plane of our spectrograph is subdivided by a200 × 200 200 × 200 element microlens array with a plate scale of 19.2 mas per microlens, critically sampling the diffraction-limited point-spread function at 1.06 μm. In addition to obtaining spectra, this wavelength resolution allows suppression of the chromatically dependent speckle noise, which we describe. In addition, we have recently installed a novel internal wave front calibration system that will provide continuous updates to the AO system every 0.5–1.0 minutes by sensing the wave front within the coronagraph. The Palomar AO system is undergoing an upgrade to a much higher order AO system (PALM-3000): a 3388-actuator tweeter deformable mirror working together with the existing 241-actuator mirror. This system, the highest-resolution AO corrector of its kind, will allow correction with subapertures as small as 8.1 cm at the telescope pupil using natural guide stars. The coronagraph alone has achieved an initial dynamic range in the H H band of2 × 10-4 2 × 10 - 4 at 1″, without speckle noise suppression. We demonstrate that spectral speckle suppression provides a factor of 10–20 improvement over this, bringing our current contrast at 1″ to∼2 × 10-5 ∼ 2 × 10 - 5 . This system is the first of a new generation of apodized-pupil coronagraphs combined with high-order adaptive optics and integral field spectrographs (e.g., GPI, SPHERE, HiCIAO), and we anticipate that this instrument will make a lasting contribution to high-contrast imaging in the Northern Hemisphere for years.

We discuss how the impending redefinition of the SI system of units might affect the ability of students to understand the link between the units and the new system. The redefinition will no longer define a set of base units, but rather a set of constants of nature, such as the elementary charge, e. We point out that this list of constants need not be the only way to introduce students to the subject, either in class or in textbooks. We suggest an alternative way to introduce high school and undergraduate students to the redefined SI, by suggesting a list of experiments for some units; this list would be completely compatible with the redefined SI, and would have all of the same scientific and technological advantages. We demonstrate by questionnaire results that this alternative is more appealing to students. We hope to spur a discussion amongst teachers regarding this important topic for high school and undergraduate physics courses.

Ferroalloy production can release a number of metals into the environment, of which manganese (Mn) is of major concern. Other elements include lead, iron, zinc, copper, chromium, and cadmium. Mn exposure derived from settled dust and suspended aerosols can cause a variety of adverse neurological effects to chronically exposed individuals. To better estimate the current levels of exposure, this study quantified the metal levels in dust collected inside homes ( n =85), outside homes ( n =81), in attics ( n =6), and in surface soil ( n =252) in an area with historic ferroalloy production. Metals contained in indoor and outdoor dust samples were quantified using inductively coupled plasma optical emission spectroscopy, whereas attic and soil measurements were made with a X-ray fluorescence instrument. Mean Mn concentrations in soil (4600  μ g/g) and indoor dust (870  μ g/g) collected within 0.5 km of a plant exceeded levels previously found in suburban and urban areas, but did decrease outside 1.0 km to the upper end of background concentrations. Mn concentrations in attic dust were ~120 times larger than other indoor dust levels, consistent with historical emissions that yielded high airborne concentrations in the region. Considering the potential health effects that are associated with chronic Mn inhalation and ingestion exposure, remediation of soil near the plants and frequent, on-going hygiene indoors may decrease residential exposure and the likelihood of adverse health effects.

Background Release of ambient metals during ferroalloy production may be an important source of environmental exposure for nearby communities and exposure to these metals has been linked to adverse respiratory outcomes. We sought to characterize the association between personal air levels of metals and respiratory health in Italian adolescents living in communities with historic and current ferroalloy activity. Methods As part of a study in the industrial province of Brescia, Italy, 410 adolescents aged 11–14 years were recruited. Participants were enrolled from three different communities with varying manganese (Mn) levels: Bagnolo Mella which has current ferroalloy activity, Valcamonica, which has historic ferroalloy activity and Garda Lake which has no history of ferroalloy activity. Particulate matter <10 μm in diameter (PM 10 ) was collected for 24 h in filters using personal sampling. Mn, nickel (Ni), zinc (Zn), chromium (Cr) and iron (Fe) were measured in filters using x-ray fluorescence. Data on respiratory health was collected through questionnaire. Data for 280 adolescents were analyzed using a modified Poisson regression, and risk ratios were calculated for an interquartile (IQR) range increase in each pollutant. Results In adjusted models including PM 10 as a co-pollutant, we found significant associations between concentrations of Mn (RR: 1.09, 95 % CI [1.00, 1.18] per 42 ng/m 3 increase), Ni (RR: 1.11, 95 % CI [1.03, 1.21] per 4 ng/m 3 increase) and Cr (RR: 1.08, 95 % CI [1.06, 1.11] per 9 ng/m 3 increase) and parental report of asthma. We also found significant associations between increased Mn and Ni and increased risk of asthma medication use in the past 12 months (RR: 1.13, 95 % CI [1.04, 1.29] and (RR: 1.13, 95 % CI [1.01, 1.27] respectively). Conclusions Our findings suggest that exposure to ambient Mn, Ni and Cr may be associated with adverse respiratory outcomes.