Trapped Electrons and Ions as Particle Detectors.

Electrons and ions trapped with electromagnetic fields have long served as important high-precision metrological instruments, and more recently have also been proposed as a platform for quantum information processing. Here we point out that these systems can also be used as highly sensitive detectors of passing charged particles, due to the combination of their extreme charge-to-mass ratio and low-noise quantum readout and control. In particular, these systems can be used to detect energy depositions many orders of magnitude below typical ionization scales. As illustrations, we suggest some applications in particle physics. We outline a nondestructive time-of-flight measurement capable of sub-eV energy resolution for slowly moving, collimated particles. We also show that current devices can be used to provide competitive sensitivity to models where ambient dark matter particles carry small electric millicharges ≪e. Our calculations may also be useful in the characterization of noise in quantum computers coming from backgrounds of charged particles.

Cooling a Harmonic Oscillator by Optomechanical Modification of Its Bath.

Optomechanical systems show tremendous promise for the high-sensitivity sensing of forces and modification of mechanical properties via light. For example, similar to neutral atoms and trapped ions, laser cooling of mechanical motion by radiation pressure can take single mechanical modes to their ground state. Conventional optomechanical cooling is able to introduce an additional damping channel to mechanical motion while keeping its thermal noise at the same level, and, as a consequence, the effective temperature of the mechanical mode is lowered. However, the ratio of the temperature to the quality factor remains roughly constant, preventing dramatic advances in quantum sensing using this approach. Here we propose an approach for simultaneously reducing the thermal load on a mechanical resonator while improving its quality factor. In essence, we use the optical interaction to dynamically modify the dominant damping mechanism, providing an optomechanically induced effect analogous to a phononic band gap. The mechanical mode of interest is assumed to be weakly coupled to its heat bath but strongly coupled to a second mechanical mode, which is cooled by radiation pressure coupling to a red-detuned cavity field. We also identify a realistic optomechanical design that has the potential to realize this novel cooling scheme.

Interacting Atomic Interferometry for Rotation Sensing Approaching the Heisenberg Limit.

Atom interferometers provide exquisite measurements of the properties of noninertial frames. While atomic interactions are typically detrimental to good sensing, efforts to harness entanglement to improve sensitivity remain tantalizing. Here we explore the role of interactions in an analogy between atomic gyroscopes and SQUIDs, motivated by recent experiments realizing ring-shaped traps for ultracold atoms. We explore the one-dimensional limit of these ring systems with a moving weak barrier, such as that provided by a blue-detuned laser beam. In this limit, we employ Luttinger liquid theory and find an analogy with the superconducting phase-slip qubit, in which the topological charge associated with persistent currents can be put into superposition. In particular, we find that strongly interacting atoms in such a system could be used for precision rotation sensing. We compare the performance of this new sensor to an equivalent noninteracting atom interferometer, and find improvements in sensitivity and bandwidth beyond the atomic shot-noise limit.

Quantum-Enhanced Machine Learning.

The emerging field of quantum machine learning has the potential to substantially aid in the problems and scope of artificial intelligence. This is only enhanced by recent successes in the field of classical machine learning. In this work we propose an approach for the systematic treatment of machine learning, from the perspective of quantum information. Our approach is general and covers all three main branches of machine learning: supervised, unsupervised, and reinforcement learning. While quantum improvements in supervised and unsupervised learning have been reported, reinforcement learning has received much less attention. Within our approach, we tackle the problem of quantum enhancements in reinforcement learning as well, and propose a systematic scheme for providing improvements. As an example, we show that quadratic improvements in learning efficiency, and exponential improvements in performance over limited time periods, can be obtained for a broad class of learning problems.

Psychosocial Factors That Create Barriers to Managing Serum Phosphorus Levels in Pediatric Dialysis Patients: A Retrospective Analysis.

OBJECTIVE: Abnormal phosphorus homeostasis is among the medley of metabolic disturbances commonly associated with chronic kidney disease. We sought to determine the psychosocial factors that create barriers to controlling serum phosphorus levels in children on dialysis and to evaluate the perceptions of children and caregivers on the ease or difficulty of following a dietary phosphorus restriction and taking phosphorus binder medications. DESIGN: Single center cross-sectional study. SETTING: Pediatric dialysis unit at a children's hospital. SUBJECTS: Forty-eight patients on chronic hemodialysis or peritoneal dialysis (mean age: 11.03 ± 6.88 years; 69% male). MAIN OUTCOME MEASURE: Serum phosphorus levels were recorded from electronic health records and converted to a mean phosphorus standard deviation score (SDS) for each individual. Mean phosphorus SDS values were compared to each independent categorical variable using an analysis of variance test, continuous variables were analyzed using linear regression, and logistic regression was used to determine odds ratios. RESULTS: There was a significant relationship between age and phosphorus SDS (P < .001), with patients over 13 years of age having the highest prevalence of hyperphosphatemia (88%). Patients and caregivers who identified phosphorus levels as "controlled" had lower phosphorus SDS values compared to the other subjects (P = .003). However, of the patients and caregivers who reported that serum phosphorus levels were "controlled," 46% were hyperphosphatemic. Furthermore, 73% and 87% of patients and caregivers reported that following a phosphorus-restricted diet and taking phosphorus binders were "easy"; yet, 40% and 49% of these patients were hyperphosphatemic, respectively. CONCLUSION: In the present study, elevated serum phosphorus levels were most common in adolescent dialysis patients. There also appears to be a disconnect between the perceived ease of following a phosphorus-restricted diet and taking phosphorus binders and the achievement of normal serum phosphorus levels. These data further emphasize the importance of ongoing education regarding dietary and medical management requirements.

Scanning localized magnetic fields in a microfluidic device with a single nitrogen vacancy center.

Nitrogen vacancy (NV) color centers in diamond enable local magnetic field sensing with high sensitivity by optical detection of electron spin resonance (ESR). The integration of this capability with microfluidic technology has a broad range of applications in chemical and biological sensing. We demonstrate a method to perform localized magnetometry in a microfluidic device with a 48 nm spatial precision. The device manipulates individual magnetic particles in three dimensions using a combination of flow control and magnetic actuation. We map out the local field distribution of the magnetic particle by manipulating it in the vicinity of a single NV center and optically detecting the induced Zeeman shift with a magnetic field sensitivity of 17.5 µT Hz(-1/2). Our results enable accurate nanoscale mapping of the magnetic field distribution of a broad range of target objects in a microfluidic device.

Renal formulas pretreated with medications alters the nutrient profile.

BACKGROUND: Pretreating renal formulas with medications to lower the potassium and phosphorus content is common in clinical practice; however, the effect of this treatment on other nutrients is relatively unstudied. We examine whether nutrient composition is affected by pretreating renal formulas with sodium polystyrene sulfonate (SPS) suspension and sevelamer carbonate. METHODS: Fixed medication doses and treatment times were utilized to determine changes in the nutrient composition of Suplena® and Similac® PM 60/40. The effect of simultaneously adding both medications (co-administration) to the formula on the nutrient composition of Suplena® was also evaluated. RESULTS: Pretreatment of Suplena® with SPS reduced the concentrations of calcium (11-38 %), copper (3-11 %), manganese (3-16 %), phosphorus (0-7 %), potassium (6-34 %), and zinc (5-20 %) and increased those of iron (9-34 %), sodium (89-260 %), and sulfur (19-45 %) and the pH (0.20-0.50 units). Pretreatment of Similac® PM 60/40 with SPS reduced the concentrations of calcium (8-29 %), copper (5-19 %), magnesium (3-26 %), and potassium (33-63 %) and increased those of iron (13-87 %) and sodium (86-247 %) and the pH (0.40-0.81 units). Pretreatment of both formulas with the SPS suspension led to significant increases in the aluminum concentration in both formulas (507-3957 %). No differences in potassium concentration were observed between treatment times. Unexpectedly, the levels of neither phosphorus nor potassium were effectively reduced in Suplena® pretreated with sevelamer carbonate alone or when co-administered with SPS. CONCLUSIONS: Pretreating formula with medications alters nutrients other than the intended target(s). Future studies should be aimed at predicting the loss of these nutrients or identifying alternative methods for managing serum potassium and phosphorus levels in formula-fed infants. The safety of pretreating formula with SPS suspension should also be examined.

Colloquium: Advances in automation of quantum dot devices control.

Arrays of quantum dots (QDs) are a promising candidate system to realize scalable, coupled qubit systems and serve as a fundamental building block for quantum computers. In such semiconductor quantum systems, devices now have tens of individual electrostatic and dynamical voltages that must be carefully set to localize the system into the single-electron regime and to realize good qubit operational performance. The mapping of requisite QD locations and charges to gate voltages presents a challenging classical control problem. With an increasing number of QD qubits, the relevant parameter space grows sufficiently to make heuristic control unfeasible. In recent years, there has been considerable effort to automate device control that combines script-based algorithms with machine learning (ML) techniques. In this Colloquium, a comprehensive overview of the recent progress in the automation of QD device control is presented, with a particular emphasis on silicon- and GaAs-based QDs formed in two-dimensional electron gases. Combining physics-based modeling with modern numerical optimization and ML has proven effective in yielding efficient, scalable control. Further integration of theoretical, computational, and experimental efforts with computer science and ML holds vast potential in advancing semiconductor and other platforms for quantum computing.

Perspective on the Current State-of-the-Art of Quantum Computing for Drug Discovery Applications.

Computational chemistry is an essential tool in the pharmaceutical industry. Quantum computing is a fast evolving technology that promises to completely shift the computational capabilities in many areas of chemical research by bringing into reach currently impossible calculations. This perspective illustrates the near-future applicability of quantum computation of molecules to pharmaceutical problems. We briefly summarize and compare the scaling properties of state-of-the-art quantum algorithms and provide novel estimates of the quantum computational cost of simulating progressively larger embedding regions of a pharmaceutically relevant covalent protein-drug complex involving the drug Ibrutinib. Carrying out these calculations requires an error-corrected quantum architecture that we describe. Our estimates showcase that recent developments on quantum phase estimation algorithms have dramatically reduced the quantum resources needed to run fully quantum calculations in active spaces of around 50 orbitals and electrons, from estimated over 1000 years using the Trotterization approach to just a few days with sparse qubitization, painting a picture of fast and exciting progress in this nascent field.

Ray-based framework for state identification in quantum dot devices.

Quantum dots (QDs) defined with electrostatic gates are a leading platform for a scalable quantum computing implementation. However, with increasing numbers of qubits, the complexity of the control parameter space also grows. Traditional measurement techniques, relying on complete or near-complete exploration via two-parameter scans (images) of the device response, quickly become impractical with increasing numbers of gates. Here we propose to circumvent this challenge by introducing a measurement technique relying on one-dimensional projections of the device response in the multidimensional parameter space. Dubbed the "ray-based classification (RBC) framework," we use this machine learning approach to implement a classifier for QD states, enabling automated recognition of qubit-relevant parameter regimes. We show that RBC surpasses the 82% accuracy benchmark from the experimental implementation of image-based classification techniques from prior work, while reducing the number of measurement points needed by up to 70%. The reduction in measurement cost is a significant gain for time-intensive QD measurements and is a step forward toward the scalability of these devices. We also discuss how the RBC-based optimizer, which tunes the device to a multiqubit regime, performs when tuning in the two-dimensional and three-dimensional parameter spaces defined by plunger and barrier gates that control the QDs. This work provides experimental validation of both efficient state identification and optimization with machine learning techniques for non-traditional measurements in quantum systems with high-dimensional parameter spaces and time-intensive measurements.

Autotuning of double dot devices in situ with machine learning.

The current practice of manually tuning quantum dots (QDs) for qubit operation is a relatively time-consuming procedure that is inherently impractical for scaling up and applications. In this work, we report on the in situ implementation of a recently proposed autotuning protocol that combines machine learning (ML) with an optimization routine to navigate the parameter space. In particular, we show that a ML algorithm trained using exclusively simulated data to quantitatively classify the state of a double-QD device can be used to replace human heuristics in the tuning of gate voltages in real devices. We demonstrate active feedback of a functional double-dot device operated at millikelvin temperatures and discuss success rates as a function of the initial conditions and the device performance. Modifications to the training network, fitness function, and optimizer are discussed as a path toward further improvement in the success rate when starting both near and far detuned from the target double-dot range.

QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments.

BACKGROUND: Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting their properties to data mining and analysis to assisting drug discovery to advancing cybersecurity. Recently, we added to this list by showing how a machine learning algorithm (a so-called learner) combined with an optimization routine can assist experimental efforts in the realm of tuning semiconductor quantum dot (QD) devices. Among other applications, semiconductor quantum dots are a candidate system for building quantum computers. In order to employ QDs, one needs to tune the devices into a desirable configuration suitable for quantum computing. While current experiments adjust the control parameters heuristically, such an approach does not scale with the increasing size of the quantum dot arrays required for even near-term quantum computing demonstrations. Establishing a reliable protocol for tuning QD devices that does not rely on the gross-scale heuristics developed by experimentalists is thus of great importance. MATERIALS AND METHODS: To implement the machine learning-based approach, we constructed a dataset of simulated QD device characteristics, such as the conductance and the charge sensor response versus the applied electrostatic gate voltages. The gate voltages are the experimental 'knobs' for tuning the device into useful regimes. Here, we describe the methodology for generating the dataset, as well as its validation in training convolutional neural networks. RESULTS AND DISCUSSION: From 200 training sets sampled randomly from the full dataset, we show that the learner's accuracy in recognizing the state of a device is ≈ 96.5% when using either current-based or charge-sensor-based training. The spread in accuracy over our 200 training sets is 0.5% and 1.8% for current- and charge-sensor-based data, respectively. In addition, we also introduce a tool that enables other researchers to use this approach for further research: QFlow lite-a Python-based mini-software suite that uses the dataset to train neural networks to recognize the state of a device and differentiate between states in experimental data. This work gives the definitive reference for the new dataset that will help enable researchers to use it in their experiments or to develop new machine learning approaches and concepts.

Kidney Function Reserve Capacity in Early and Later Stage Autosomal Dominant Polycystic Kidney Disease.

BACKGROUND AND OBJECTIVES: It is assumed that in autosomal dominant polycystic kidney disease (ADPKD), kidney function remains in the normal range for several decades because of hyperfiltration of remnant nephrons. In this study, we investigate the extent to which patients with ADPKD hyperfilter. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: In this cross-sectional study, we measured GFR as urinary clearance using continuous infusion of 125I-iothalamate. Kidney function reserve capacity was determined as increase in measured GFR after adding a dopamine infusion of 4.4-6 mg/h. Potential kidney donors were used as healthy controls and matched by age and sex to patients with ADPKD for comparisons across age groups and CKD stages. Hyperfiltration was defined by a loss of kidney function reserve capacity compared with healthy controls. RESULTS: A total of 300 participants were studied. In the youngest age group (18-29 years), measured GFR was not different between patients with ADPKD and healthy controls (103±21 versus 111±9 ml/min per 1.73 m2; P=0.14). In this age group kidney function reserve capacity was higher compared with healthy controls (11.1%±8.3% versus 5.3%±6.5%; P=0.04). Moreover, kidney function reserve capacity was similar to healthy controls in patients with ADPKD with early-stage disease (eGFR≥60 ml/min per 1.73 m2), either overall or when divided into fast or slow progressors according to their Mayo height-adjusted total kidney volume class. However, in patients with ADPKD, lower measured GFR was associated with lower kidney function reserve capacity (ß=1.0 [95% confidence interval, 0.5 to 1.5] % per 10 ml/min per 1.73 m2; P<0.001). Kidney function reserve capacity was therefore lower compared with healthy controls at older age and later CKD stages. CONCLUSIONS: Patients with early-stage ADPKD, either classified as having rapidly or slowly progressive disease, are able to increase their GFR in response to dopamine. Hyperfiltration, defined by a loss of kidney function reserve capacity, may therefore not be an early phenomenon in ADPKD.

Dynamically induced robust phonon transport and chiral cooling in an optomechanical system.

The transport of sound and heat, in the form of phonons, can be limited by disorder-induced scattering. In electronic and optical settings the introduction of chiral transport, in which carrier propagation exhibits parity asymmetry, can remove elastic backscattering and provides robustness against disorder. However, suppression of disorder-induced scattering has never been demonstrated in non-topological phononic systems. Here we experimentally demonstrate a path for achieving robust phonon transport in the presence of material disorder, by explicitly inducing chirality through parity-selective optomechanical coupling. We show that asymmetric optical pumping of a symmetric resonator enables a dramatic chiral cooling of clockwise and counterclockwise phonons, while simultaneously suppressing the hidden action of disorder. Surprisingly, this passive mechanism is also accompanied by a chiral reduction in heat load leading to optical cooling of the mechanics without added damping, an effect that has no optical analog. This technique can potentially improve upon the fundamental thermal limits of resonant mechanical sensors, which cannot be attained through sideband cooling.Chiral transport can provide robustness against disorder, resulting in improved resonant modes for sensing and metrology. Here, Kim et al. demonstrate chiral phonon transport, disorder suppression and anomalous cooling without damping in an asymmetrically-pumped optomechanical system.

Urinary Calcium Excretion and Risk of Chronic Kidney Disease in the General Population.

INTRODUCTION: High urinary calcium excretion (UCaE) has been shown to lead to accelerated renal function decline in individuals with renal tubular diseases. It is not known whether this association also exists in the general population. Therefore, we investigated whether high UCaE is associated with risk of developing chronic kidney disease (CKD) in community-dwelling subjects. METHODS: Urine samples of 5491 subjects who were free of CKD at baseline and participated in the Prevention of Renal and Vascular End-Stage Disease study (a prospective, observational, general population-based cohort of Dutch men and women aged 28-75 years) were examined for UCaE. UCa concentration was measured in two 24-hour urine samples at baseline (1997-1998) by indirect potentiometry. UCaE was treated as a continuous variable and a categorical variable grouped according to sex-specific quintiles for UCaE. UCaE was compared with de novo development of estimated glomerular filtration rate <60 ml/min per 1.73 m2 and/or albuminuria >30 mg/24 h. RESULTS: Baseline median UCaE was 4.13 mmol/24 h for men and 3.52 mmol/24 h for women. During a median follow-up of 10.3 years, 899 subjects developed CKD. After multivariable adjustment, every 1 mmol/24 h higher baseline UCaE was associated with a 6% lower risk for incident CKD during follow-up (hazard ratio: 0.94 [0.88-0.99], P = 0.02). The association was shown to be significantly nonlinear, with highest risk of CKD in the lowest quintile for UCaE (hazard ratio: 1.28 [0.97-1.68], P = 0.09). There was no association between UCaE and mortality or cardiovascular health during follow-up, suggesting that this association was not a reflection of poor nutritional intake due to bad health. DISCUSSION: These findings indicate that high UCaE does not increase risk of CKD, but rather that low UCaE may be harmful.

Diet and polycystic kidney disease: A pilot intervention study.

BACKGROUND & AIMS: Dietary sodium, protein, acid precursors, and water have been linked to cyst growth in polycystic kidney disease; yet, no studies in patients have examined the feasibility of using a dietary intervention that controls all of these factors. The aim of this study was to determine if a diet, appropriate for persons of most ages, reduces the excretion of sodium, urea, acid, and decreases mean urine osmolality while gaining acceptance by patients with autosomal dominant polycystic kidney disease (ADPKD). METHODS: Twelve adults with ADPKD enrolled in a pre-post pilot feasibility study and served as their own controls. Individuals consumed their usual diet for one week then for four weeks followed an isocaloric diet lower in sodium and protein and higher in fruits, vegetables, and water. Three-day diet records and two 24-h urine samples were collected at baseline, week 2, and week 4 visits; blood pressure, weight, and serum were obtained at all three visits. A modified nutrition hassles questionnaire was completed on the last visit. RESULTS: During the dietary intervention, subjects (n = 11) consumed less sodium, protein, and dietary acid precursors 36%, 28%, and 99%, respectively, and increased fluid intake by 42%. Urinary sodium, urea, net acid excretion, osmoles, and osmolality decreased 20%, 28%, 20%, 37%, and 15%, respectively; volume increased 35%. Urine changes were in accord with the diet record. Ninety-one percent of participants reported that none of the hassles were worse than "somewhat severe", and most participants felt "somewhat confident" or "very confident" that they could manage the new diet. CONCLUSIONS: A majority of adult patients with ADPKD successfully prepared and followed a composite diet prescription with decreased sodium, protein, acid precursors, and increased fluid intake. This trail was registered at ClinicalTrials.gov (NCT01810614).

A Quantum Model for Entropic Springs.

Motivated by understanding the emergence of thermodynamic restoring forces and oscillations, we develop a quantum-mechanical model of a bath of spins coupled to the elasticity of a material. We show our model reproduces the behavior of a variety of entropic springs while enabling investigation of non-equilibrium resonator states in the quantum domain. We find our model emerges naturally in disordered elastic media such as glasses, and is an additional, expected effect in systems with anomalous specific heat and 1/f noise at low temperatures due to two-level systems that fluctuate.

Parents' perspective on weight management interventions for adolescents with intellectual and developmental disabilities.

BACKGROUND: Adolescents with intellectual and developmental disabilities (IDD) have high rates of obesity. However, little research has been conducted demonstrating effective strategies and barriers for weight loss or weight management in adolescents with IDD. Furthermore, parents play a large role in terms of weight management in children and adolescents with IDD, and their views should be taken into consideration when designing a diet and PA intervention for weight management. OBJECTIVE: The aims of this study are to better understand the parents' perspectives on the strategies and barriers for helping children and adolescents with IDD be successful in a weight management program and to identify how this information to guide future approaches. METHODS: Semi-structured interviews were conducted in 18 parents whose children had just finished a diet and PA intervention. Interviews were transcribed verbatim, and thematic analysis was performed. RESULTS: Parents reported a positive attitude toward the program, liked the convenience of the program, and felt that they learned beneficial strategies to encourage healthy habits in the home. Parents found time to be a major barrier to supporting their child with a diet and PA intervention. Parents were willing to change their own dietary behaviors to help their children successfully follow a weight loss intervention; however, no parent reported changing their own PA levels. CONCLUSION: Future diet and PA studies should aim to reduce parental time commitment and increase importance of PA.

Experiences and Perspectives of Polycystic Kidney Disease Patients following a Diet of Reduced Osmoles, Protein, and Acid Precursors Supplemented with Water: A Qualitative Study.

BACKGROUND: Salt, protein, acid precursors, and fluid intake have been identified as factors that influence cyst growth in ADPKD. Unfortunately, the feasibility of following these dietary restrictions/enhancements from a patient's point-of-view has yet to be studied. The purpose of this study is to understand better the experiences of patients following a relatively complex dietary prescription targeting these factors. METHODS: Twelve adults with ADPKD and kidney function >30ml/min/1.73m2 were recruited from the University of Kansas Medical Center Polycystic Kidney Disease clinic. In a qualitative design, semi-structured interviews of participants were conducted following a four week dietary intervention (experimental diet lower in sodium, protein, and acid precursors, and supplemented with water) either face-to-face or by telephone. All interviews were recorded, transcribed verbatim, and checked for accuracy. Transcripts were analyzed thematically for emerging themes. RESULTS: Participants reported that eating less meat and more fruits and vegetables were the easiest components of the diet, whereas reaching the daily goal amount of fruits and vegetables and tracking the diet constantly were the most difficult components. Participants had little difficulty with fluid intake and reported the prescribed fluid goal as achievable. The tracking system for fruits and vegetables and protein was reported to be both helpful and intuitive, but tracking their intake on paper was tedious. Eating out was the most significant barrier to following the diet with some individuals avoiding restaurants in order to comply with the dietary prescription. CONCLUSION: Participants on the experimental diet heightened their awareness of the consumption of dietary salt, protein, acid precursors, and fluid intake. Additionally, most participants believed adherence to the prescribed diet was feasible. However, participants wanted less cumbersome ways to track and monitor the diet, especially given that the prescribed diet is designed for lifelong adherence. Future studies should focus on targeting these specific dietary factors in larger groups of more ethnically and culturally diverse populations to help inform clinicians and how best to help diverse populations adhere to the dietary intervention. TRIAL REGISTRATION: ClinicalTrials.gov NCT01810614.