As climate change accelerates, extreme weather events are becoming increasingly common, particularly in elevated latitudes where sub-zero temperatures are more frequently experienced during winter months. These evolving environmental conditions, alongside routine cold-weather and high-altitude applications, underscore the need to understand lithium-ion battery behaviour at low temperatures to ensure that both performance and safety are maintained. In this study, high-power commercial 21700-format cylindrical cells employing LiNixCoyAlzO2 (NCA) cathodes were aged under controlled sub-zero temperature conditions. Charging under such low-temperature regimes diverts current away from desirable lithium-ion intercalation and instead promotes the precipitation of metallic lithium onto the negative electrode surface. Cells exhibiting significant lithium plating were found to be less thermally stable and more prone to failure at lower temperatures. Moreover, when thermal runaway was triggered, these failures occurred through more violent and less predictable pathways. To investigate the failure mechanisms, high-speed X-ray computed tomography (CT) was employed to capture the internal dynamics and nucleation behaviour associated with thermal runaway. Notably, thermal runaway consistently initiated near the core of the jelly-roll structure, where winding tension is greatest, and lithium plating is most severe. This highlights a critical architectural vulnerability in the cell design under low-temperature ageing conditions.
Preparing matrix product states (MPSs) on quantum computers is an essential routine in the simulation of many-body physics. However, widely used schemes based on staircase circuits are often too deep to execute on current hardware. Here, we demonstrate that MPSs with short-range correlations can be prepared with shallow circuits by leveraging heuristics from approximate quantum compiling (AQC). We achieve this with ADAPT-AQC, an adaptive-ansatz preparation algorithm, and introduce a generalized initialization procedure for the existing AQC-Tensor algorithm. We first compare these methods for the task of preparing a molecular electronic structure ground state. We then use them to prepare an antiferromagnetic (AFM) ground state of the 50-site Heisenberg XXZ spin chain near the AFM-XY phase boundary. Through the execution of circuits with up to 59 cz depth and 1251 cz gates, we perform a global quench and observe the relaxation of magnetic ordering in a parameter regime previously inaccessible due to deep ground-state preparation circuits. Our results demonstrate how the integration of quantum and classical resources can push the boundary of what can be studied on quantum computers.
Optical atomic clocks based on laser-cooled trapped ions and atoms have advanced rapidly over the past decade. With fractional frequency uncertainties now surpassing 10 −18 , they are some of the most precise measurement tools ever built. Yet researchers are still pursuing new avenues of research to explore the fundamental limits to their stability, accuracy, and reproducibility. In this mini-review, we provide a survey of the current state of the art by describing the fundamental principles and techniques that underpin this progress, the architectures used to realize optical clocks, and the supporting laser technologies that are essential to their operation. We also examine the progress that has been made toward a redefinition of the second in the International System of Units and the inclusion of optical clocks into the global time and frequency metrology infrastructure. Finally, we discuss emerging applications of optical clocks and look at the prospects for making their precision more readily accessible to end users.
The Superconducting Quantum Materials and Systems Center, a U. S. Department of Energy National Quantum Information Science Research Center, has conducted a comprehensive and coordinated study using superconducting transmon qubit chips with known performance metrics to identify the underlying materials-level sources of device-to-device performance variation. Following qubit coherence measurements, these qubits of varying base superconducting metals and substrates have been examined with various non-destructive and invasive material characterization techniques at Northwestern University, Ames National Laboratory, and Fermilab as part of a blind study. We find trends in variations of the depth of the etched substrate trench, the thickness of the surface oxide, and the geometry of the sidewall, which when combined, lead to correlations with the T1 lifetime across different qubits on the same chip. In addition, we provide a list of features that varied from device to device, for which the impact on performance requires further studies. Finally, we identify two low-temperature characterization techniques that may potentially serve as proxy tools for qubit measurements. These insights provide materials-oriented solutions to not only reduce performance variations across neighboring devices but also to engineer and fabricate devices with optimal geometries to achieve performance metrics beyond the state-of-the-art values.
We present MIMIC-III-Ext-PPG, a large-scale, quality-assessed photoplethysmography (PPG) dataset derived from the matched waveform subset of MIMIC-III. Our dataset provides 30-second PPG segments with annotations tailored for various cardiovascular and respiratory analyses. In particular, with 6.3 million segments from 6,189 subjects, it represents the largest publicly available resource for heart rhythm classification, with heart rhythm annotations derived from bedside charted observations. For subsets where arterial blood pressure (ABP), respiratory (RESP), and/or electrocardiography (ECG) signals are available, we also provide systolic/diastolic blood pressure, respiratory rate, and heart rate annotations, extracted using best practice from the underlying signals. We provide signal quality assessments for all signals. This ensures a high-quality, publicly available dataset of unprecedented size that can be used as a benchmarking resource for machine learning approaches for a broad range of prediction tasks, which remains easily extendable by leveraging additional clinical metadata from the MIMIC-III clinical database.
