arXiv subjects

Benjamin A. Cordier

Publications and source records attributed to Benjamin A. Cordier.

3 recordsLinked to original sources

arXiv2025

Open Data Sharing in Clinical Research and Participants Privacy: Challenges and Opportunities in the Era of Artificial Intelligence

Sharing clinical research data is key for increasing the pace of medical discoveries that improve human health. However, concern about study participants' privacy, confidentiality, and safety is a major factor that deters researchers from openly sharing clinical data, even after deidentification. This concern is further heightened by the evolution of artificial intelligence (AI) approaches that pose an ever-increasing threat to the reidentification of study participants. Here, we discuss the challenges AI approaches create that blur the lines between identifiable and non-identifiable data. We present a concept of pseudo-reidentification, and discuss how these challenges provide opportunities for rethinking open data sharing practices in clinical research. We highlight the novel open data sharing approach we have established as part of the Artificial Intelligence Ready and Exploratory Atlas for Diabetes Insights project, one of the four Data Generation Projects funded by the National Institutes of Health Common Fund's Bridge2AI Program.

q-bio.OT↗

arXiv2022

PennyLane: Automatic differentiation of hybrid quantum-classical computations

PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for hardware providers including the Xanadu Cloud, Amazon Braket, and IBM Quantum, allowing PennyLane optimizations to be run on publicly accessible quantum devices. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, JAX, and Autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications.

quant-ph↗

arXiv2021

Biology and medicine in the landscape of quantum advantages

Quantum computing holds significant potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning approaches for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is typically contingent on a reduction in the consumption of a computational resource, such as time, space, or data. Here, we distill the concept of a quantum advantage into a simple framework that we hope will aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to i) assess the potential of quantum advantages in specific application areas and ii) identify gaps that may be addressed with novel quantum approaches. Bearing in mind the rapid pace of change in the fields of quantum computing and classical algorithms, we aim to provide an extensive survey of applications in biology and medicine that may lead to practical quantum advantages.

quant-ph↗