Alumni Projects - Graduation Year: 2023
Simulating Self-Gravitating Dark Matter with Variational Quantum Computing
Abhinav Angirekula, Dhruv Anurag, Rohan Kompella
Galaxies have far too little observable matter for them to be gravitationally bound. Thus, there must be a theoretical, unobservable from of matter that makes up this missing mass: dark matter. Dark matter does not interact with light and we can only see its effects through gravitational lensing. The current leading candidates for dark matter are weakly interacting massive particles (WIMPs), primordial black holes, and axions. Accurate dark matter simulations are vital for researching their true nature, as scientists can compare simulations to their observations. If there are discrepancies between the two, there might be an undiscovered property or interaction that dark matter has. While dark matter simulation appears to be incredibly complicated, it really just boils down to time evolving a system using differential equations. In our case, the Schrödinger-Poisson equations are a system of non-linear differential equations that govern the evolution of several types of dark matter models: from fuzzy dark matter to standard cold dark matter. Mocz and Szasz were able to solve the Schrödinger-Poisson equations using a classical spectral method, and used a variational quantum algorithm outlined by Lubasch et. al. and were able to successfully model dark matter. However, instead of running their quantum algorithm on a quantum computer, they ran it on a simulation of a quantum computer on a classical machine. For our project, we want to replicate the results of Mocz and Szasz and implement both their classical and quantum algorithms. We want to run their quantum algorithm on an actual quantum computer (e.g. IBM), and the number of qubits used in it is under the maximum limit of quantum computers we could use so that we can compare the efficiency of each algorithm.
Presentation Video (FCPS Only)
Using Quantum Optimization for Monte Carlo Option Pricing
Nikhil Chintalapati, Sumanth Kalluru, Jean Lavigne Du Cadet
Options can be priced accurately using a Monte Carlo simulation, but these simulations are extremely computer intensive to run, and are not optimal for real world options. Monte Carlo can be optimized using quantum computing, creating an accurate and fast estimation of an option's inherent value.
Presentation Video (FCPS Only)
Machine Learning Decoding of Quantum Surface Code Error Syndromes
Jeffrey Chen
Presentation Video (FCPS Only)
Designing a laser microphone using a Michelson Interferometer and Machine Learning
Praneeth Bhandaru
The Buran Eavesdropping System, also known as a Laser Microphone, was first developed by Leon Theremin in the Soviet Union during the Cold War. It is a reconnaissance tool by the KGB for eavesdropping targets through solid objects using a low-power infrared beam. Various advancements from its creation increased the system's accuracy and portability; However, the precision and set-up time has seen little to no advances. The lack of innovation is a significant setback to the mass use of this reconnaissance system, as being covert and timely is critical.
There have been other projects detailing ways to solve this problem, many theoretical with no physical model to support their case. However, the information they provide is of utmost importance for understanding the principles behind the design of the laser microphone.
Modernizing and creating a new Buran Eavesdropping System aims to verify the system's future in reconnaissance and prove the system's reliability. There is an ongoing information war going on in the world, and every single innovation in the field of reconnaissance is necessary to protect innocent civilians from possible threats and to create the world a safer place. The new system would help do precisely that and create opportunities for further innovations of the system.
Presentation Video (FCPS Only)
A Novel Analysis of Alprazolam Metabolism Energy via Quantum Computing Techniques
Raed Mirza
Quantum Computing for Novel Pharmaceutical Development
Presentation Video (FCPS Only)
Blind Source Separation via Quantum Slime Mould Algorithm
Hein Htut
Source separation, as the name suggests, separates the combined output of multiple sources into some representation of each individual source. Each audio source has its own unique overtone which we can use to identify them with, however, it also causes the massive overlapping frequencies that sum up to an incredibly noisy and messy wave representation of the overall mixed audio source. Even having sufficient data, it requires massive computational power to not only identify the wanted overtone as some unique pattern in the evolution of that audio source's frequencies over time but also separate that one waveform from the bigger overall waveform. Thus, this is where we can use quantum computing, specifically a Quantum CNN to speed up computation.
Presentation Video (FCPS Only)
Modeling Double Pendulums with Wave Functions and Recurrent Neural Networks
Nathan Cheng, Akshay Vellore
Chaotic systems are normally modeled by nonlinear differential equations. These equations usually require numerical methods to be solved, but they are computation intensive. Our project aims to compare two novel solutions to predicting chaotic systems: machine learning (particularly with reservoir computing) and approximating the nonlinear system of differential equations as a (infinite) list of linear differential equations and modeling it with a quantum computing algorithm.
Presentation Video (FCPS Only)
Implementing a Variational Quantum Eigensolver to Model DNA
Anjali Pillai
Respiratory syncytial virus (RSV) is a negative single-stranded RNA virus of the Pneumoviridae family. RSV contains 3 main protein structures - the attachment G glycoprotein, the fusion F glycoprotein, and the small hydrophobic proteins which serve as a viroprotein. This project will primarily focus on the attachment G protein in relation to the receptor protein on the host side. The main aim of this project is to implement a Variational Quantum Eigensolver (VQE) to compare ground state energy levels and binding energy levels of Respiratory Syncytial Virus attachment G proteins and possible receptor shapes in order to identify the optimal receptor shape for a viable vaccine or drug treatment.
Presentation Video (FCPS Only)
Comparative Analysis of Quantum Machine Learning Algorithms for Cyber Attack Detection
Karthik Bhargav, Tanish Jain
Quantum ML to Classify Cyberattacks
Presentation Video (FCPS Only)
Modeling Lattice Quantum Chromodynamics using Quantum Computing
Abhinav Angirekula, Rohan Kompella
Within a hadron - a title for a group that includes the likes of protons, neutrons, and more - there are quarks. The force holding these quarks together is incredibly strong, strong because it needs to be in order to keep the quarks within the hadron. This strength earned said force the name of “the strong nuclear force,” often abridged to simply “the strong force.” This force is explained in a theory called quantum chromodynamics (QCD) and this theory asserts that the strong force is perpetuated by gluons; we now have an understanding of QCD. What, then, is Lattice QCD? There is a complicated explanation to it, but we’re still trying to understand it, so for now, suffice it to say that it is a means of calculating results to understand how QCD works.
People have explored computational approaches like Lattice QCD extensively. The conventional approach on classical computers is to use Markov chain Monte Carlo methods on classical computers to simulate QCD(Ichihara et al., 2014). However, when coupling constants get large, finding the values of the path integrals involved in these calculations, it becomes impossible to get very accurate results without the use of a supercomputer. However, recently, people have been considering whether or not a quantum computer could help resolve these issues(Kan & Nam, 2022).
From our brief literature review, it seems that a comprehensive implementation has yet to be fully executed and applied. Even Kan 2022 explicitly states in their introduction that they only “provide a complete layout of computational instructions at a gate-by-gate level to be run on a quantum computer, to efficiently simulate quantum electrodynamics (QED) and quantum chromodynamics (QCD), in the hopes that it will serve as a stepping stone to future scientific and technological developments.” With a bit of luck, determination, and cold emailing, we hope to bring about the future developments they described.
Presentation Video (FCPS Only)
Quantum Fourier Transformations to Decompose and Restructure Low-Cost Chemoresistive Gas Sensor Data
Amit Sai Erraguntla, Arnav Jain, Nikhil Pesaladinne
Current air quality sensors are too expensive and are not feasible to use on a regular basis. While cheaper sensors exist, they output resistances to various gases rather than a measure of air quality. Previous attempts to restructure the gas resistance data using machine learning models overfit and fail to extrapolate to outside data. Since the sensor data is slightly sinusoidal, we plan to use quantum Fourier transformations to estimate a function for air quality in a more robust manner.
Presentation Video (FCPS Only)
Near-Term Safe Semiprime Factorization
Utkarsh Goyal, Kisna Matta
Near Term Prime Number Factorization with VQE
Presentation Video (FCPS Only)
Designing an Inexpensive Single-Photon Detector/Emitter System for Quantum Key Distribution
Mira Singh, Aditya Vasantharao, Sherry Yu
Current single-photon detectors are expensive, require temperature control, and must be used in dark rooms—they are susceptible to overload, which shorts the sensor if exposed to excess light, rendering it useless. This paper proposes a novel low-cost Geiger-mode Avalanche Photodiode (G-APD) which utilizes an inexpensive, reverse-biased LED as a photodiode and is compatible with SMA fiber-optic connectors so that it can be used in a Quantum Key Distribution system. Since the LED used has very low quantum efficiency, this G-APD also features machine learning-based dark count correction. We also propose the creation of a heralded single-photon source using GaN/AlN quantum dots, due to their high efficiency at room temperature. While these quantum dots are typically created using molecular beam epitaxy (MBE), we propose the use of metalorganic chemical vapor deposition (MOCVD) for quantum dot manufacturing.
Presentation Video (FCPS Only)
CdSe/ZnS Quantum Dots as Biosensors for E. coli
Clarissa Ding, Shahzad Sohail
Quantum Biology Interdisciplinary Project Using Quantum Dots
Presentation Video (FCPS Only)
Reducing Phase Encoding Steps in Undersampled MRI k-Space using Novel Deep Learning Algorithms
Sauman Das
Acquiring MRI scans is a time-taking and uncomfortable process. Any patient movement during the acquisition time can result in low quality scans which cause inaccurate diagnoses and problems during surgical treatment. In this study, I plan to study different signal undersampling procedures for k-space acquisition and develop a generative deep learning model to interpolate missing details. If this algorithm works successfully, the MRI acquisition process can be made more efficient and help millions of patients across the world.
Presentation Video (FCPS Only)
Quantum Policy and Quantum Workforce Development
Anisha Talreja
Developing an economic model for Quantum Information Science and Technology(QIST) education-to-workforce pipeline.
Improving infrared reflectivity and carbon retention of concrete modeled with quantum computing
Suraj Vaddi
Concrete production and application are detrimental to the environment. This project aims to develop an environmentally friendly concrete that has the potential to reduce the urban heat island effect and reduce contamination into surrounding environments and water supplies. We will look into methods such as reducing concrete's infrared radiation reflectance and reducing carbon emission over time, all through the lens of thermodynamics and molecular modeling with quantum computing.
Photon Polarization Control for QKD
Katherine Hartley
Working to design a polarization control system for QKD
Quantum Information Theory Limits
Pi Rogers
This project seeks to find an upper bound for the information transfer of various quantum channels. Channels and qubits are mathematically modeled and manipulated using advanced calculus and linear algebra techniques to find the information-theoretic capacity of these channels. Full description in first blog post.
Lab Cat
Tarini Basireddy
Causing chaos and expediting entropy!