Quantum, Condensed Matter, & Living Systems Parallel Talks

Orphan wells are potential environmental hazards via methane emissions and groundwater contamination. Low-cost detection tools are needed for large-scale data collection across the country for the Department of Energy (DOE). Crowdsourced magnetic surveys using smartphones offer a cost-effective way for citizen scientists to confirm the locations of orphan wells. Variations in internal magnetometer hardware, sensor fusion algorithms, and operating system architectures between mobile platforms present challenges for spatial accuracy and data repeatability.

I evaluated data from an Android, iPad, and iPhone (mainly iOS vs Android) during controlled grid surveys over a documented well casing and a nearby field acting as a control site in Flagg Park in CO. Using custom Python data analysis pipelines, I assessed key parameters including spatial anomaly alignment, background noise levels, and GPS scatter.

I will present a comparative analysis of hardware stability, spatial peak shifts, and operating system-dependent noise profiles. Lastly, I will discuss the implications of the hardware trade offs for developing a standardized device-specific calibration protocol. This will enable the crowdsourced mobile data to reliably be integrated into geophysical inversion models for orphan well detection. Analysis shows that no matter the personal mobile device, orphan wells will be found in the same general several meter area and that iPhone’s are marginally the best devices to use.

The BeEST experiment uses superconducting tunnel junction (STJ) sensors that require controlled magnetic fields to function, as well as custom built magnetic shielding. To control the magnetic fields, we need to be able to make magnetic measurements accurately at the sub-Kelvin temperatures at which the experiment operates. To solve this problem in Phase IV of the BeEST, we are developing a cryogenic magnetic sensing apparatus using an off-the-shelf hall sensor and custom electronics to characterize the magnetic fields which will be affecting the STJs with the magnetic shield present, as well as to calibrate the magnet being used. To improve the sensitivity of this setup, we aim to implement a polarity switching technique which cancels out noise in our magnetic measurements. Here, we will present the status of the design and our plans for its continued development.

Acetaminophen, most widely known as a pharmaceutical, is an organic molecular crystal with applications in shock physics as an inert mock for energetic materials. In this work we develop analytical equations of state for forms I, II, and highly metastable form III of acetaminophen, as well as a pressure-temperature phase diagram for forms I and II. Density Functional Theory (DFT) is used to optimize the single unit cell geometries at zero temperature and pressures of 0-10 GPa as well as to calculate the cold curves for each polymorph. The cell geometries optimized by DFT are then used with Python package phonopy to develop the phonon density of states (DOS). This cold curve and DOS data is fitted to the MACAW reference curve to obtain EOS and thermodynamic parameters in addition to the Helmholtz and Gibbs free energy in order to predict the polymorphism and thermodynamic properties of acetaminophen at high pressure and high temperature.

LA-UR-25-26549

Understanding the properties and characteristics of quantum mechanical systems which separate them from their classical counterparts remains a central and fundamental task in the field of quantum information theory informing the development of fault-tolerant quantum computation and quantum sensing. For fermionic systems, fermionic Gaussian states form a continuous class of entanglement exhibiting quantum states which are nevertheless efficiently simulable. Previously, it has been shown that these fermionic Gaussian states exhibit a central limit theorem, which can be used to probe the amount of non-Gaussian resource contained within them. Here, we demonstrate a central limit theorem for fermionic quantum channels, which allows us to understand how arbitrary fermionic circuits or operations thermalize to classically simulable operations on these families.

Protein molecules in cells self-assemble into large structures via a process known as biocondensation. Current models of the molecular mechanisms of protein condensation draw from polymer theory. These models are limited by the small number of proteins whose condensation has been studied experimentally. To identify conserved physical mechanisms that drive protein condensation, we are using large sets of homologous proteins in the fungal subphylum Saccharomycotina as the source data for paired large-scale computational analysis and medium-throughput in vivo and in vitro condensation assays. We aim to characterize the sequence determinants and biological roles of protein condensation in yeast and improve sequence-based prediction of protein condensation.

Halide perovskite materials have recently become of interest because of their novel photovoltaic and electronic properties. It has been established that in bulk (3D) perovskites, polaron lifetimes are controlled by the molecular lattice deformation, which correlates with photovoltaic performance. In the case of 2D perovskites, the dimensional restriction results in no polaron formation, since the exciton binding energy is too high, which in turn affects the ensuing relaxation dynamics. We aim to compare the excited-state responses of 2D lead-halide and tin-halide perovskites. For lead-based 2D perovskites, excitons form exciton polarons and are screened by the lattice, extending their lifetime. For tin-based 2D perovskites, the lattice is less deformable, meaning excitons recombine more quickly. Examining dynamics in tin perovskites is important to understand, as commercial solutions will need to take into account environmental and health impacts.

As a way to improve student motivation and ensure student learning is being accurately evaluated, there has been a recent push towards implementing alternative grading strategies in undergraduate physics courses. However, completely changing grading strategies can be difficult in large multi-section courses coordinated across multiple instructors. This talk focuses on a small change made to a large introductory calculus-based mechanics course where a new policy allowed for students’ grades on portions of the final exam to replace their grades on the corresponding midterms if they performed better on the final. This new course policy was intended to prioritize whether the students ultimately learned the material over how quickly they learned it. In this talk, we discuss the effects of this policy including student perceptions of it, which students it impacted, and whether increases in grades from the policy may be linked to other metrics of student learning.

The spectral-element method is widely used to simulate large-scale dynamic systems in irregular media, offering high order approximations on a flexible spatial discretization. Meanwhile, Hamiltonian simulation, a quantum field of study, has emerged as a promising candidate for speeding up PDE solvers. With these considerations in mind, we provide the theoretical framework of a spectral-element discretization for Hamiltonian simulations of the 1-D elastic wave equation in heterogeneous media. We demonstrate a method that transforms the spectral element matrix into an imaginary Hermitian operator representing a qubit Hamiltonian. The desired wavefield displacement and its first time derivative are combined into a single vector and mapped to a quantum wavefunction with dynamics encoded in the calculated Hamiltonian. The time-evolution of the wave solution is then intrinsic to the discrete Schrodinger equation, where the wavefunction reflects the wavefield up to a normalization factor. A quantum state estimation algorithm then performs successive measurements to fully reconstruct negative wavefield information from non-negative quantum measurement probabilities. We demonstrate that the method reproduces classical predictor-corrector results on an ideal quantum simulator and perform an analysis of hardware implementation on an IBM Heron system. These results are compared to analogous methods of time-evolving finite difference solutions to the elastic wave equation. We provide an overview of current challenges limiting scalability and introduce pathways toward alleviating such limitations for large-scale simulations.