Quantum Algorithm Probes Particle Scattering
Quantum computers are expected to outperform classical computers in simulating phenomena such as stellar fusion and nuclear decay, which are quantum in nature. Scientists seek to use quantum computers to understand the fundamental particle interactions fueling such phenomena because these computing systems can accommodate the exponential number of states associated with each particle added to the simulated system. In a paper published November 14, 2024, in Physical Review C, researchers from Lawrence Livermore, the InQubator for Quantum Simulations, and the University of Trento describe a quantum computing algorithm that simulates nonrelativistic, elastic scattering processes.
At the quantum mechanical level, microscopic particles (such as atomic nuclei) exhibit wavelike properties. When the particles collide, the wave function that captures their quantum state experiences a phase shift within its cycle. The team’s quantum algorithm can calculate the phase shifts produced during an elastic scattering interaction. The algorithm measures these phase shifts by generating a detector wave and modulating its properties until it matches the wave function of the particle ensemble.
The team first emulated the algorithm on classical hardware, then performed scattering simulations using IBM quantum processors. The algorithm withstood sources of quantum noise that frequently disrupt the functioning of quantum hardware. The work represents a notable step forward in accurately simulating even more complex interactions with quantum computers. Livermore scientist Sofia Quaglioni says, “Scattering experiments help us probe fundamental particles and their interactions, and the scattering of particles in matter helps us understand how that matter is organized at a microscopic level.”
Contact: Sofia Quaglioni (925) 422-8152 (quaglioni1 [at] llnl.gov (quaglioni1[at]llnl[dot]gov)).
Cell Membrane Model Supports Drug Development
Livermore scientists modeled interactions between proteins and cell membranes using a combination of molecular simulations and large-scale models in a study published in the December 31, 2024, issue of the Journal of Chemical Physics. Cell membranes—complex biological structures comprising a double layer of lipids punctuated by proteins—perform cell signaling, transport, and other functions. Understanding how these proteins actively shape nearby lipid molecules is important for improving drug design and fundamental biological understanding. The study improves modeling of the unique lipid patterns that form around these proteins.
Based on dynamic density functional theory (DDFT), researchers modeled the distribution of lipids as a continuous field rather than individual molecules, enabling them to run significantly larger-scale simulations. The model more accurately captures anisotropic behaviors that vary with orientation, not just distance, and combines the detail of molecular dynamics simulations with the speed and scale of continuum models. The team states that their DDFT-based model for anisotropic interactions allows scientists to research protein–membrane interactions at a more biologically relevant scale.
The researchers applied the model to two membrane protein classes of strong medical interest: the RAS–RAF complex (frequently implicated in cancers) and G protein-coupled receptors (involved in cell responses to stimuli). This effective membrane modeling strategy could aid drug development efforts. Principal investigator Tim Carpenter says, “Drug interactions with proteins in the membrane do not adhere to the same norms or conventions as soluble drugs, so these types of models will help advance study in that area of mostly untapped therapeutic potential.”
Contact: Tim Carpenter (925) 422-2900 (carpenter36 [at] llnl.gov (carpenter36[at]llnl[dot]gov)).
Livermore and Canaery Create Nose–Computer Interface
Livermore scientists have partnered with the neurotechnology company Canaery to advance development of a cutting-edge nose–computer interface (NCI) to augment scent-detection abilities in animals, which can be trained to alert human handlers when they detect certain olfactory signatures of explosives, narcotics, or biomarkers for diseases. However, conventional training often restricts detection to one or two primary scents. The team’s NCI device represents a major step toward translating the olfactory information that an animal naturally detects into data that humans can properly interpret.
Scientists from Livermore and Canaery designed and fabricated a 767-channel microelectrode array that can be implanted directly onto the surface of the brain to digitize olfactory signals. Livermore researchers provided key input into the array’s layout design, electron beam lithography-based nanofabrication, high-density connector prototyping, and recording electronics. Canaery brought topical expertise in olfaction and odor delivery, neural implantation and electrophysiological recording, and machine learning. Together, the researchers aim to push the boundaries of neural interface technology, with applications spanning national security, healthcare, environmental monitoring, and neuroscience research.
The NCI device brings attention to a new route for threat detection. “This array, with hundreds of electrodes per square millimeter, is significantly denser than anything we have fabricated before. We are excited for the alignment of our capabilities and Canaery’s technology with Livermore’s larger national security mission,” says principal investigator Travis Massey.
Contact: Travis Massey (925) 422-9509 (massey21 [at] llnl.gov (massey21[at]llnl[dot]gov)).
