A5052554570- Quantum Computing Algorithms and Architecture
- Quantum Information and Cryptography
- Quantum many-body systems
- Computational Physics and Python Applications
- Stochastic Gradient Optimization Techniques
- Theoretical and Computational Physics
- Emergency and Acute Care Studies
- Healthcare Technology and Patient Monitoring
- Advanced optical system design
- Injury Epidemiology and Prevention
- Balance, Gait, and Falls Prevention
- Optical Systems and Laser Technology
- Non-Invasive Vital Sign Monitoring
- Parallel Computing and Optimization Techniques
- Adaptive optics and wavefront sensing
- Patient Satisfaction in Healthcare
- Advanced Statistical Methods and Models
- Magnetic confinement fusion research
- Space Science and Extraterrestrial Life
- Healthcare Decision-Making and Restraints
- Time Series Analysis and Forecasting
- Social Movements and Cultural Identity
- Psychometric Methodologies and Testing
- Bayesian Methods and Mixture Models
- Space exploration and regulation
University of Chicago
2022-2025
Argonne National Laboratory
2022-2025
JPMorgan Chase & Co (United States)
2025
Illinois Wesleyan University
2020
Pharmaceutical Product Development (United States)
2019
University of Florida
2012-2015
VA Puget Sound Health Care System
2012
Methodist Healthcare
2012
University of Tennessee Health Science Center
2012
Malcom Randall VA Medical Center
2012
The purpose of this study was to provide normative data on fall prevalence in U.S. hospitals by unit type and determine the 27-month secular trend falls before implementation Centers for Medicare Medicaid Service (CMS) rule, which does not reimburse care related injury resulting from hospital falls.
Abstract Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training fine-tuning process. In this work, we show that fault-tolerant quantum computing could possibly provide provably efficient resolutions for generic (stochastic) gradient descent algorithms, scaling as $${{{{{{{\mathcal{O}}}}}}}}({T}^{2}\times {{{{{{{\rm{polylog}}}}}}}}(n))$$ <mml:math...
Background: Bed alarm systems intended to prevent hospital falls have not been formally evaluated. Objective: To investigate whether an intervention aimed at increasing bed use decreases and related events. Design: Pair-matched, cluster randomized trial over 18 months. Nursing units were allocated by computer-generated randomization on the basis of baseline fall rates. Patients outcome assessors blinded unit assignment; may become unblinded. (ClinicalTrials.gov registration number:...
In 2008, Medicare implemented the Hospital-Acquired Conditions (HACs) Initiative, a policy denying incremental payment for 8 complications of hospital care, also known as never events. The regulation's effect on these events has not been well studied.To measure association between Medicare's nonpayment and 4 outcomes addressed by HACs Initiative: central line-associated bloodstream infections (CLABSIs), catheter-associated urinary tract (CAUTIs), hospital-acquired pressure ulcers (HAPUs),...
Abstract Although quantum computers can perform a wide range of practically important tasks beyond the abilities classical 1,2 , realizing this potential remains challenge. An example is to use an untrusted remote device generate random bits that be certified contain certain amount entropy 3 . Certified randomness has many applications but impossible achieve solely by computation. Here we demonstrate generation certifiably using 56-qubit Quantinuum H2-1 trapped-ion computer accessed over...
Gaussian boson sampling, a computational model that is widely believed to admit quantum supremacy, has already been experimentally demonstrated and claimed surpass the classical simulation capabilities of even most powerful supercomputers today. However, whether current approach limited by photon loss noise in such experiments prescribes scalable path advantage an open question. To understand effect on scalability we analytically derive asymptotic operator entanglement entropy scaling, which...
Gaussian boson sampling is a promising candidate for showing experimental quantum advantage. While there evidence that noiseless hard to efficiently simulate using classical computer, the current experiments inevitably suffer from loss and other noise models. Despite high photon rate presence of noise, they are currently claimed be classically with best-known algorithm. In this work, we present tensor-network algorithm simulates whose complexity can significantly reduced when high. By...
Abstract Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training fine-tuning process. In this work, we show that fault-tolerant quantum computing could possibly provide provably efficient resolutions for generic (stochastic) gradient descent algorithms, scaling as O(T 2 × polylog(n)), where n is size T number iterations training, long sufficiently dissipative...
Empirical evidence for a gap between the computational powers of classical and quantum computers has been provided by experiments that sample output distributions two-dimensional circuits. Many attempts to close this have utilized simulations based on tensor network techniques, their limitations shed light improvements hardware required frustrate simulability. In particular, having in excess $\sim 50$ qubits are primarily vulnerable simulation due restrictions gate fidelity connectivity,...
Quantum embedding learning is an important step in the application of quantum machine to classical data. In this paper we propose a few-shot paradigm, which learns embeddings useful for training downstream tasks. Crucially, identify circuit bypass problem hybrid neural networks, where learned parameters are optimized represent dataset without kernel. We observe that generalize unseen classes, and suffer less from terms better occupation parameter space compared with regression classification.
BACKGROUND The Centers for Medicare & Medicaid Services (CMS) implemented the Hospital‐Acquired Conditions (HACs) Initiative in October 2008; CMS no longer reimbursed hospitals fall injury. effects of this payment change on and injury rates are not well described, nor its effect physical restraint use. OBJECTIVE aim study was to examine 2008 HACs falls, injurious DESIGN/SETTING This a nine‐year retrospective cohort (July 2006‐December 2015) involving 2,862 adult medical,...
Abstract Random quantum circuits have been utilized in the contexts of supremacy demonstrations, variational algorithms for chemistry and machine learning, blackhole information. The ability random to approximate any unitaries has consequences on their complexity, expressibility, trainability. To study this property circuits, we develop numerical protocols estimating frame potential, distance between a given ensemble exact randomness. Our tensor-network-based algorithm polynomial complexity...
Gaussian boson sampling, a computational model that is widely believed to admit quantum supremacy, has already been experimentally demonstrated and claimed surpass the classical simulation capabilities of even most powerful supercomputers today. However, whether current approach limited by photon loss noise in such experiments prescribes scalable path advantage an open question. To understand effect on scalability we analytically derive asymptotic operator entanglement entropy scaling, which...
Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training fine-tuning process. In this work, we show that fault-tolerant quantum computing could possibly provide provably efficient resolutions for generic (stochastic) gradient descent algorithms, scaling as O(T^2 polylog(n)), where n is size T number iterations training, long sufficiently dissipative sparse, with...
Abstract In this paper, we develop methods for longitudinal quantile regression when there is monotone missingness. particular, propose pattern mixture models with a constraint that provides straightforward interpretation of the marginal parameters. Our approach allows sensitivity analysis which an essential component in inference incomplete data. To facilitate computation likelihood, novel way to obtain analytic forms required integrals. We conduct simulations examine robustness our...
Simulation of many-body systems is extremely computationally intensive, and tensor network schemes have long been used to make these tasks more tractable via approximation. Recently, algorithms that can exploit the inherent symmetries underlying quantum proposed further reduce computational complexity. One class systems, namely those exhibiting a global U(1) symmetry, especially interesting. We provide state-of-the-art, graphical processing unit-accelerated, highly parallel supercomputer...
Liu et al. (ITCS22) initiated the study of designing a secure position verification protocol based on specific proof quantumness and classical communication. In this paper, we interesting topic further answer some open questions that are left in paper. We provide new generic compiler can convert any single round quantumness-based certified randomness to communication-based scheme. Later, extend our different kinds multi-round protocols. Moreover, instantiate with random circuit sampling...
Noisy quantum simulation is challenging since one has to take into account the stochastic nature of process. The dominating method for it density matrix approach. In this paper, we evaluate conditions which inferior a substantially simpler way simulation. Our approach uses ensembles circuits, where random Krauss operators are applied original gates represent errors modeling channels. We show that our error relatively low, even large numbers qubits. implemented as part QTensor package. While...
Recently, deep neural network (DNN) based adaptive optics systems were proposed to address the issue of latency in existing wavefront sensorless (WFS-less) aberration correction techniques. Intensity images alone are sufficient for DNN model compute necessary correction, removing need iterative processes and allowing practical real-time be implemented. Specifically, we generate desired phase profiles utilizing a system that outputs set coefficients 27 terms Zernike polynomials. We present an...