Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session F47: Undergraduate Research VIUndergrad Friendly
|
Hide Abstracts |
Sponsoring Units: SPS Chair: Midhat Farooq, APS Room: Room 313 |
Tuesday, March 7, 2023 8:00AM - 8:12AM |
F47.00001: Using Entanglement to Optimize Precision in Continuous Quantum Clocks Jean-Francois S Van Huele, Diego R Gilbert Estrada Since time enters quantum mechanics as a parameter, the description of time measurement necessitates the introduction of an operator, a so-called quantum clock. Whereas the first quantum clocks were discrete clocks, ticking at specific, discrete times, continuous quantum clocks are not restricted in this way. Instead, the challenge for continuous clocks is to optimize the precision or resolution of the time measurement. We address the question of how the entanglement resource can improve the precision of continuous quantum clocks. In order to do so, we follow Ramezani et al. [Phys. Rev. A106,022427 (2022)]) in selecting system Hamiltonians that optimize the precision through the quantum Cramer-Rao bound in a parameter estimation procedure. We explore the dynamical evolution of different types of entangled states under these Hamiltonians and compare the resulting precision of these clocks. In particular, we compare how the clocks are affected by using GHZ and W entangled states. |
Tuesday, March 7, 2023 8:12AM - 8:24AM |
F47.00002: Dynamical Decoupling Protocols for Reconstructing Qubit Charge Noise Robert Cady, Guy Ramon, Robert Cady
|
Tuesday, March 7, 2023 8:24AM - 8:36AM |
F47.00003: Investigating the Performance of the Quantum Neuron Born Machine on Near-Term Hardware Aliza U Siddiqui, Kaitlin M Gili Unsupervised generative models are a class of machine learning algorithms designed to learn underlying patterns in data for the purpose of generating new samples. While these models have demonstrated powerful performance on challenging tasks ranging from drug discovery to image generation, improving their capabilities is still an active area of research. Parameterized quantum circuits can model probability distributions with classical training and therefore are a natural fit for tackling these data-driven problems. The Quantum Neuron Born Machine (QNBM) is a quantum generative model that employs non-linear activations through repeat-until-success circuits, containing mid-circuit measurements as well as classical control, which enables it to learn complex probability distributions. It has been shown to perform well on an ideal simulator but has yet to be realized and benchmarked on quantum hardware. To benchmark the QNBM on hardware, we look to IBM’s superconducting device as well as Quantinuum’s ion-trap technology. We conduct a further investigation into the model’s resource requirements such as the number of circuit executions to enable good performance of the QNBM on near-term devices while minimizing hardware execution costs. We train the model with various noise models, shot variations, and initial training parameters to provide these estimates, and then determine the essential requirements for the QNBM on the respective devices. We then compare the resource requirements for two versions of the model—one utilizing post-selection executed on IBM’s devices and one employing classical control on Quantinuum’s technology. Then, utilizing our resource estimates, we run the first small-scale hardware realization of the QNBM on both types of technologies and assess its ability to model a cardinality-constrained probability distribution. |
Tuesday, March 7, 2023 8:36AM - 8:48AM |
F47.00004: Simulating Time Evolution of Entanglement Entropy on Quantum Computers via Algebraic Compression Natalia Wilson, Efekan Kökcü, Alexander F Kemper Quantum computers are well suited to simulate the time evolution of a many-body system under both static and time-dependent Hamiltonians. The standard approach to producing time evolution circuits is with the Trotter product formula; however, for long simulation times this method produces deep circuits which have significant noise, limiting the applicability on today's quantum hardware. Thus, to obtain meaningful results, we need to minimize the noise by minimizing the depth of the circuit. This can be achieved for certain classes of Hamiltonians, as their time evolution can be algebraically compressed to a fixed depth circuit [1,2]. Here, we demonstrate the use of this compression algorithm by studying the time evolution of several fermionic models on IBM quantum computers. By using a controlled SWAP structure [3] and applying error mitigation techniques we are able to obtain the time-dependent Rényi entropy on quantum hardware, bringing us closer to understanding the behavior of entanglement entropy under time evolution. |
Tuesday, March 7, 2023 8:48AM - 9:00AM |
F47.00005: A machine learning analysis of energy and entanglement property of the XYZ quantum spin chain. Marcus M Corulli, Trinanjan Datta A quantum spin chain is a linear arrangement of magnetic moments. Based on competing exchange interaction between the neighboring spins this chain model can allow us to investigate a wide variety of magnetic phenomena with applications ranging from quantum computing to molecular chemistry. From a computational perspective, machine Learning (ML) is a technique based on optimizing a network of interconnected nodes to produce an expected output for any input (in our case the spin chain configuration). We have implemented the convolutional neural network (CNN) ML algorithm to investigate a couple of physical properties, energy, and entanglement, of a fully exchange anisotropic XYZ quantum spin chain. The system was investigated both in the presence of a homogeneous and an inhomogeneous magnetic field. Using computational toolkits (QuSpin for exact diagonalization and ML packages from sci-kit learn), we show that utilizing an ML approach helps to predict the ground state energy of the XYZ system. We will also investigate if our ML analysis can provide insight into the entanglement (negativity) properties of the quantum XYZ spin chain in comparison to the XXZ system. |
Tuesday, March 7, 2023 9:00AM - 9:12AM |
F47.00006: Estimation of Semimajor Axis of Exoplanet Orbit Using Machine Learning Techniques Riya A Rai, Tanmay Basu Semimajor axis is one of the most important orbital parameters of an exoplanet. It is used to calculate planet signatures like insolation flux, which may inform us about the existence of life on exoplanets. The semimajor axis is calculated using Kepler's third law of motion and techniques like transits, radial velocity etc., which require the orbital period of the planet to be known. It is a time-intensive process to precisely observe long orbital periods. We aim to bridge this gap by building a machine learning model that estimates the semimajor axis using relevant features of the exoplanets which were explored in the past and archived as NASA Kepler mission data. So far we know, this initiative is the first of its kind. A Light Gradient Boosting based regression framework is developed that uses correlation maps to remove similar features to avoid overfitting. Furthermore, noisy features are removed and one-hot-encoding scheme is used for categorical features. The performance of the proposed regression framework is used to estimate the semimajor axis of the NASA Kepler mission data, whose performance is compared with other regression techniques. The empirical analysis shows that this framework outperforms the other techniques in terms of root mean squared error and R2 score. In the future, we aim to explore how this model behaves for specific types exoplanetary systems in order to find the exoplanet data category for which this model gives optimum results. |
Tuesday, March 7, 2023 9:12AM - 9:24AM |
F47.00007: Finding EMRI Signals in Simulated LISA Data August R Muller In preparation for the upcoming Laser Interferometer Space Antenna (LISA) mission, the LISA Data Challenges pose a series of open questions on how to extract gravitational wave (GW) signals from simulated LISA data. Solving these challenges is essential to demonstrating effective analysis methods for the mission in the mid-2030s. As the LISA mission will detect GW signals in a new frequency range, a variety of previously undetected GW source types will be present in the LISA data. One such source type is the extreme mass-ratio inspiral (EMRI), an inspiraling binary system where a stellar mass object orbits a super-massive black hole. This project seeks to use Markov Chain Monte Carlo (MCMC) algorithms to develop a reliable method for identifying EMRI signals and extracting their source parameters. |
Tuesday, March 7, 2023 9:24AM - 9:36AM |
F47.00008: Energy Dependent Morphology of HAWCJ2019+368, a High Energy Pulsar in the Dragonfly Nebula Elaine Nieves Inside the Dragonfly nebula is the pulsar PSRJ2021+3651, one of the brightest sources of TeV gamma rays. Data from the High Altitude Water Cherenkov (HAWC) observatory has resolved the MGRO J2019+37 region, where the pulsar and nebula are located, into two sources: HAWCJ2019+368 and HAWC J2016+371. The study of this source's energy dependent morphology is indicative of the underling particles causing this gamma ray emission. Energy dependent morphology indicates that the size of the source is limited by energy lost in the particles producing gamma-rays. Using an expanded dataset from HAWC, we will search for energy dependent morphology. Measuring this energy dependent morphology will confirm the interpretation of HAWC J2019+368 as a TeV Pulsar Wind Nebula (PWN). |
Follow Us |
Engage
Become an APS Member |
My APS
Renew Membership |
Information for |
About APSThe American Physical Society (APS) is a non-profit membership organization working to advance the knowledge of physics. |
© 2024 American Physical Society
| All rights reserved | Terms of Use
| Contact Us
Headquarters
1 Physics Ellipse, College Park, MD 20740-3844
(301) 209-3200
Editorial Office
100 Motor Pkwy, Suite 110, Hauppauge, NY 11788
(631) 591-4000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 20045-2001
(202) 662-8700