Current Projects
PINNs for Ground State Determination and Fisher Information-Based Phase Transition Detection
Claire, Avery
Quantum phases are states of matter which exist at near absolute zero temperatures, where quantum effects govern the system. The transition between phases is called a quantum phase transition (QFT) and is of interest particularly when the order parameter, a value that usually characterizes transitions, is not known. Instead, quantum and classical phase transitions can be detected through the use of the Fisher Information Metric (FIM) in various unsupervised learning tasks. This project uses Physics-Informed Neural Networks (PINNs) to approximate ground-state FIM values across different quantum models, including the Frustrated Ising (ISN400), FIL24, Hubbard, and XXZ. A novel loss formulation is introduced to incorporate batch normalization during training, addressing a key limitation in previous models such as ClassiFiM, which struggled with large log-odds due to cross-entropy loss scaling. Our study finds that with PINNs there is generally comparable accuracy and runtime to that of ClassiFIM, though further research is needed into the speedup that PINNs actually presents.
EQuIP: A Novel Quantum Internet Protocol via Dynamic Entanglement and Distribution
Sanchali
The proposed research aims to develop a framework for the quantum internet to ensure the instantaneous, secure, and loss-free transmission of data between devices in the network. In response to user queries, the ideated system will dynamically generate entangled qubit pairs to ensure greater efficiency in information teleportation via a quantum network. Implemented using NetSquid, the simulation of this network involves the creation of two entangled qubits upon demand from one device to transmit information to the other. The quality of teleportation is evaluated by calculating the fidelity between the final and intended qubits. Ultimately, the novel dynamic approach, titled Event-Driven Quantum Internet Protocol (EQuIP), yielded a fidelity of 88.4%, which is comparable with the alternative approach of direct pairwise entanglements. Therefore, EQuIP is an effective framework for a scalable quantum internet to ensure accurate and efficient data transmission.
Quantum Optimization of Electric Vehicle Charging Schedules Using QAOA
Anmol
This study applies the Quantum Approximate Optimization Algorithm (QAOA) to optimize charging schedules for electric motor vehicles (EMVs) on a motorway network. Using a 20-qubit simulation in Qiskit, I address the challenge of assigning 2 vehicles to 5 chargers across 2 routes, minimizing travel time, energy consumption, and charging costs. The QAOA approach achieved a cost estimate of 10–20 units, comparable to a classical greedy algorithm’s 12.97 units, while satisfying constraints on route selection, charger capacity, and battery demands. Despite a longer runtime of approximately 30 minutes due to quantum circuit simulation, the results highlight QAOA’s potential to enhance the efficiency and scalability of EV charging infrastructure. This work bridges quantum computing and transportation energy management, offering insights for sustainable mobility solutions. Future research should focus on quantum hardware implementation and larger-scale networks.
Doppler-Free Saturation Spectroscopy with Rb vapor
Ophelia
Doppler-free saturation spectroscopy (DFSS) is a laser locking technique. It utilizes counterpropagating lasers to slow atoms in vapor form by using a feedback loop that measures the difference between two beams’ wavelengths and correspondingly adjusts the laser. DFSS can be used to lock laser systems across many fields, such as in a magneto-optical trap, which traps atoms through oscillating magnetic fields and counterpropagating lasers. DFSS is a common undergraduate project, and here, a high school student attempts to synthesize and then create a DFSS design from the literature.
Abnormalities of Chaotic Systems found in the Entanglement States of Quantum Scars
Victoria
Ergodicity, or the ability of a system to explore all accessible phase states, is a fundamental assumption in statistical mechanics, particularly in the thermodynamic representations of many-body systems. However, not all many-body systems are ergodic. Quantum many-body scars are a recently discovered class of non-thermal eigenstates embedded within otherwise thermalizing chaotic systems found to weakly violate ergodicity. In this paper, we numerically investigate the entanglement properties of scarred eigenstates in the PXP model, a paradigmatic example of quantum scars. By comparing entanglement entropy to Page’s theoretical prediction for random states and highlighting low-entropy eigenstates with large overlap with the period-2 charge density wave state ($\ket{Z_2}$), we observe distinct violations of ergodicity and uncover the unique structure of scars. Our findings support recent theoretical work proposing the universality and robustness of quantum scars and suggest future directions in using scarred states to preserve quantum coherence.
Quantum Harmonic Oscillator Model of Stock Return Distributions
Harry
Stochastic models have been used for decades to model stock prices. Recently, the advancement of quantum mechanics has led to novel research in using quantum models to model stock prices. This paper leverages the quantum harmonic oscillator to model the long term log stock return distributions of stock prices, which can be used for forecasting stock markets. A wave function represented by the square of the superposition of the eigenfunctions of the quantum harmonic oscillator is used to model past stock returns. A phase factor is applied to the wave function to predict future stock return distributions of that stock.
GLASS: Glucose Level Measuring via Breathe Acetone Sensing through Laser Spectroscopy
Akul, Rohith
Current continuous glucose monitoring systems are prohibitively expensive, leaving many diabetics without reliable glucose tracking. This study explores a non-invasive alternative by measuring breath acetone, an established biomarker for blood glucose, using near-UV laser spectroscopy. We developed a custom experimental setup involving a multipass gas chamber and a 275 nm UV diode. Preliminary results confirm the capability to detect acetone and the effectiveness of humidity control using a breathing tube with Drierite. Limitations in diode power and data scope restricted comprehensive analysis; however, the findings demonstrate the promise of this technique as an affordable, non-invasive glucose monitoring method.
Optimizing Hamiltonian Cycle Searching using Quantum Approximation Optimization Algorithms
Michael
Hamiltonian cycles are a cycle in a graph where each vertex is visited only once. Similar to the travelling salesman problem, the aim of finding hamiltonian cycles is to reduce the number of edges traversed while still visiting every vertex. As the graph size gets bigger, it becomes exponentially more difficult to find a cycle. Using Quantum Approximation Optimization Algorithms (QAOA), it becomes possible to turn a single hamiltonian cycle to two smaller problems, with the potential to beat the speed of classical algorithms.
Creation and Characterization of Optically-Switched Multistable Liquid Crystal Waveplates
Kalib
An arbitrary waveplate cell is developed by controlling the twisted structure of nematic liquid crystals with a thin azobenzene-derivative dye alignment layer. A multistable cell is achieved via polarizing alignment of the dye layer, rather than traditional bistable photoswitching. The alignment adjusts the twist of the liquid crystal cell, resulting in an arbitrary and controllable optical retardation. Changes were observed and quantitatively measured under cross polarizers. Demonstrated are long-term stable cells with high transmittance that may be arbitrarily set and readjusted. Fully optical control of the cell is achieved with no electrical intermediate required.
MRIO-HHL: A Quantum-Enhanced Framework for Optimisation of Sustainable Supply Chain Management
Avni, Megan
The rapid growth of global trade, outpacing real-adjusted GDP, has amplified the environmental footprint of international value chains, a prominent trend in recent decades that has been ineffectively addressed due to the complexity of global supply chains and discrepancies in sustainable policy. To inform sustainable policy-making, we integrate quantum computing and econometrics in a novel framework, leveraging usage of a Multi-Regional Input-Ouput (MRIO) model to quantify embodied CO2-emissions across global supply chains and model intersectoral relations across regions. Our approach builds upon the quantum-inspired notion of complex network optimisation, drawing from the Harrow-Hassidim-Lloyd algorithm to solve the linear system and optimise environmental policy interventions within a MRIO model. Through encoding the optimisation problem for quantum computation, we seek to achieve a faster and more efficient method of emission reduction strategies across large supply chains. Within this simulated pseudo-economy, we also provide a higher dimension of scenario analysis to evaluate the environmental impacts of global trade interventions. This paper ultimately seeks to provide empirical support for global policies to push for the sustainable management of supply chains, as well as contributing to the emerging paradigm of quantum economics.
ADAPT-VQE Sampling Noise w/VTech
Akansha, Marina, Sophia, Kade
ADAPT-VQE is a Variational Quantum Eigensolver (VQE) for solving quantum chemistry simulations and optimization problems. However, in the real world, we can only use a finite number of measurement shots to estimate expectation values, limiting the performance of the algorithm. This is known as sampling noise, which impacts the accuracy of energy evaluations and the gradients for variational parameter optimization. In this work, we analyze the performance of ADAPT-VQE under realistic noise conditions to compare multiple classical optimizers such as gradient descent, quasi-Newton methods, and simplex-based approaches. We hope that our findings contribute to the development of more accurate and efficient variational algorithms with fewer measurements.
Utilizing Grover’s Algorithm Quantum Optimization for Options Pricing Following a Binomial Model
Ryeen
This research project aims to investigate the application of Grover’s algorithm, a quantum search algorithm, to optimize options pricing in discrete-time financial models, specifically by utilizing a binomial time distribution. Binomial models approximate the behavior of an asset’s price by modeling it as a series of up or down movements over discrete time steps. These models are typically used for pricing American-style options, where holders can exercise the option at any time before expiration. This project focuses on encoding the binomial model into a quantum search Boolean Satisfiability problem. Each node in the binomial price tree will represent a possible asset price and decision point. Grover’s will be used to search through every node and identify the optimal exercise strategy using the predicted payoff at each node. Ultimately, the goal of this project will be to create as accurate as possible of a model for exercising options in American markets, and will compare results to those of European Market estimations.
Quantum Cryptography
Kyril
As cybersecurity threats grow, quantum computing offers both solutions and challenges to encryption methods. My research explores Grover’s Algorithm, a quantum search algorithm that significantly accelerates brute-force attacks on cryptographic systems. Specifically, I am investigating how Grover’s Algorithm can be applied to password key encryption by reducing the search complexity from classical computing to using quantum superposition and amplitude amplification. Through theoretical analysis and quantum simulation, I am studying how this speedup affects the security of common encryption schemes, such as AES, and how cryptographic resilience must adapt in a post-quantum landscape. Additionally, I am experimenting with Qiskit to simulate quantum circuits, implementing Grover’s Algorithm on small-scale datasets to analyze its practical implications. By bridging quantum computing principles with real-world cryptographic challenges, this project aims to understand both the vulnerabilities and potential countermeasures needed for future encryption standards.
Exploring Quantum Based Adapter Methods and Reparameterization for Transformer Models
Shaurya, Joshua
Recent deep learning research has widely popularized the transformer model. Transformers have now ubiquitously been implemented in state-of-the-art models like GPT, Whisper, DALLE, GoogleViT, Facebook DETR, among many others. However, the true success of these models has to do with their adaptability to different downstream tasks. Traditional fine-tuning approaches involve re-training all of the weights of the model, which can increase inference latency and decrease efficiency due to the millions or even billions of parameters potentially involved, making full fine-tuning prohibitive. To avoid full fine-tuning, researchers developed Low Rank Adaptation (LoRA), which greatly reduces the number of parameters necessary to train. We propose a novel method of LoRA that takes advantage of a quantum circuit module that applies U3 transformations to inputs treated as normalized quantum superposition state vectors. In doing so, we aim to achieve superior accuracy or faster convergence to a reasonable accuracy in comparison to other state-of-the-art fine-tuning methods. Our new LoRA method can be applied to any pre-existing deep learning architectures that utilize weight matrices, as a method of parameter efficient fine tuning. This includes large language models, diffusers, and more. The research can empower others to create deep learning models specific to their own tasks efficiently while avoiding expensive training. In the future, we propose exploring the use of other gates and operators, as well as photonic operations that are not necessarily spin based.
Using Geant4 to Model Ground Level Enhancements
David
The Earth is continuously exposed to a stream of high-energy particles originating from extraterrestrial space. Such particles come in various forms, including galactic cosmic rays (GCRs) traveling from distant galaxies or solar energetic particles (SEPs) exuding from the Sun. Although Earth's magnetosphere acts as a shield for much of this radiation, interactions between cosmic particles and our atmosphere result in secondary particles, causing adverse health effects particularly prominent to those at an aviation altitude. Specifically, intense solar particle events known as ground level enhancements (GLEs) can produce photons with energies exceeding 200 MeV. In order to effectively assess the health risk to those at altitudes susceptible to secondary radiation, computational analysis can be conducted using the Geant4 toolkit, which utilizes Monte Carlo radiation transport codes to simulate the passage of particles through matter. In this study, we use this toolkit to develop a software to measure the radiation dosimetry due to GLEs at aviation altitudes.
Creation and Analysis of YBa2Cu3O7-δ by Citrate Synthesis, Pyrolysis, and Furnace Oxygenation
Kaiwan, Niels-Oliver
The proposed project aims to prepare and characterize samples of superconducting YBCO via citrate synthesis, pyrolysis, and furnace oxygenation processes. Upon successfully creating solid YBCO pellets, we hope to expand this project to thin-film applications via chemical vapor deposition, allowing for wide-scale creation of high-temperature superconducting surfaces.
Implications of Maxwell's Equations Generalizations on Classical Physics Paradoxes
Max
When taking AP Physics or Physics 1, many students may be baffled by apparent paradoxes in classical electrodynamics. For example, one might see that, according to the classical theory, the electric field strength of a charged plate is inversely proportional to the distance to it. However, this would mean that the electric field strength is infinite on the charged plate. This project aims to explain such paradoxes which appear upon analysis with classical electrodynamic theory. In pursuit of this goal, this research paper will incorporate novel theoretical analysis techniques in quantum electrodynamics which were recently proposed, such as the . In addition, this project will make generalizations of the Maxwell equations for application in quantum mechanics, as they fail upon analysis of these phenomena.
High-Resolution Turbulence Modeling Using the HHL Algorithm for the Poisson Navier-Stokes Equations
Taiki
This project explores how the Harrow-Hassidim-Lloyd (HHL) quantum algorithm can accelerate high-resolution turbulence simulations by solving linearized fluid equations, such as the pressure Poisson equation, on a quantum computer. Classical methods for solving these equations are computationally expensive, especially on high-resolution grids, which limits their scalability. The HHL algorithm, however, offers an exponential speedup for solving linear systems, making it a promising alternative to overcome these challenges.
My focus is on optimizing the HHL algorithm for fluid dynamics. By incorporating preconditioning techniques specifically designed for this problem, I aim to reduce the condition number of the pressure Poisson equation, improving computational efficiency. Additionally, by leveraging the sparsity and symmetry of these fluid equations, I work toward bridging the gap between quantum computing and turbulence modeling. My goal is to enable high-resolution simulations that were previously infeasible due to computational constraints.
Implementing Grover’s Diffusion Operator in A Single Photon, 2-Qubit Spatial Mode System
Neha
Our research proposes a novel method for photonic quantum computing using a single-photon, 2-qubit spatial mode system. By encoding qubits in the polarization states of individual spatial modes, we implement and analyze Bell State entanglement and Grover’s algorithm. Experimentally, we will use a 405 nm pump laser with a diode controller and BBO crystal, along with a combination of alignment mirrors, waveplates, and beamsplitters from the ThorLabs quantum optics kit. Computationally, we will use Perceval, a Python-based linear optical quantum computing library.
However, phase interferences introduced by the initial beam splitter operation, modeled as a "Black Box” operator, are shown to skew the measurement outcomes and influence the behavior of single-qubit gates. We mitigate these effects by balancing the circuit with Hadamard gates, effectively canceling extra phase contributions. Our approach enables us to construct a single photon controlled-NOT gate that achieves uniform Hong-Ou-Mandel (HOM) interference across all detectors. Our Bell State created with this gate successfully violates the Clauser-Horne-Shimony-Holt (CHSH) inequality. Expanding on this framework, we observe that Grover’s algorithm finds the marked state with 100% accuracy using Perceval’s noisy Strong Linear Optical Simulator (SLOS) and with 98.8% accuracy when random phase shifts are infused into the model. By training a simple linear regression model, we can predict the algorithm’s accuracy for specific nonrandom phase shifts with a mean squared error (MSE) of 3.70e-05 and an R^2 of 0.904.
Quadrupole Ion Trap
Jason, Chris
The goal of our project is to build a Quadrupole Ion Trap (QIT), using Lycopodium club-moss spores. Charged particles are confined within the metal-glass trap by high-voltage oscillating electric fields from a transformer operating at 6 kV at 60 Hz and controlled by a function generator. The critical elements of the trap's structure are its two end-cap electrodes and central ring electrode made from either stainless steel or copper alloy materials to maximize manipulation control. Analysis of both mass-to-charge ratios and spatial confinements of Lycopodium spores occurs after we apply electrical charging with a Teflon rod supported by cloth through triboelectric interactions. The combination of a green laser emitting less than one milliwatt power with a camera system visualizes and captures spore movement inside the trap.
ADAPT-VQE: Examining Fermionic Properties
Rabia, Japneet
Strongly correlated molecules present a significant challenge in quantum chemistry due to the complex electron interactions. These molecules, which often exhibit highly entangled electronic structures, require more sophisticated methods in calculating the ground state energies than traditional methods, which tend to produce large error, require greater circuit depth, and have less efficiency. To address this, our research focuses on testing the Adaptive Variational Quantum Eigensolver (ADAPT-VQE) algorithm, which is well-suited for handling strongly correlated systems. Our research compares the quantum observables, through the expectation value of the fermionic number, total spin, and Z spin projection operators, at each iteration in algorithm in both fixed (unitary coupled cluster singles and doubles (UCCSD)) and adaptive (fermionic-ADAPT-VQE) ansätze, which are combinations of complex operators from varying operator pools.
Quantum Cryptography with Virginia Tech
Lavanya
My project focuses on utilizing Grover's Algorithm, an advanced quantum algorithm designed to enhance the probability of finding the correct solution in unstructured search problems. The primary application is in quantum cryptography, where Grover's Algorithm is used to analyze and potentially break classical encryption schemes by significantly reducing the time required for brute-force attacks. I am grateful to be under the guidance of Dr. Atul Mantri, a professor at Virginia Tech.
As part of this research, I am also applying Grover's Algorithm to Boolean satisfiability (SAT) problems, which involve determining whether there exists a set of inputs that satisfy a given logical formula. This serves as a foundational study for broader applications in optimization and problem-solving. In this case, I will utilize what I learned from these SAT problems to solve more complex encryption problems. Additionally, the project includes an analysis and comparison of Simon’s Algorithm, another quantum algorithm, and its potential uses in cryptography and data pattern recognition. These investigations aim to deepen our understanding of quantum computing’s implications for secure data systems and for future developments in quantum technology.
TETRIS-ADAPT-VQE with tiled unitary slate products (tUPS)
William, William, Justin
We're working on ADAPT-VQE with Mafalda Ramôa and Dr. Economou's group at Virginia Tech. Quantum computing offers new computational methods for quantum mechanical analysis of molecular systems. We are testing TETRIS-ADAPT-VQE, an algorithm which optimizes a quantum circuit's parameters while adding new gates into the circuit from a pool of operators. The operators come from tiled unitary product states (tUPS), which preserve importation molecular symmetries like spin numbers.
Adaptive Zero-Knowledge Proofs: Enhancing Privacy in DApps and Hardware Systems
Samarth, Alexander, Arjun, Abhinav
In today’s day and age, information is key. At the same time, information leaks can cause tremendous harm in the wrong hands, especially sensitive information pertaining to personal identity. Zero-Knowledge Proofs (ZKPs) are a way to share the minimal amount of information to confirm a certain property about an entity. For example, Zero-Knowledge Proofs can enable someone to confirm their age is over 21 to a third party without compromising their true age. Our goal in this project is to investigate zero-knowledge proofs and create a library that enables any DApp (Distributed Application) to implement zero-knowledge proofs in an adaptive manner, and later deploy this functionality onto an Arduino system to test for side-channel attacks (physical vulnerabilities). So far, we’ve done a survey of all current applications of zero-knowledge proofs, and implemented a zero-knowledge system to verify someone’s grade at TJHSST using nextJS for the frontend and the ION API to verify credentials.
High-precision ground-based astronomical imaging in the near-infrared spectrum
Alan
In this project, we aim to characterize the performance and capabilities of a ground-based near-infrared (NIR) camera for use in high-precision astronomical photometry. The near-infrared $JHK$ bands are important for many astronomical applications, including to brown dwarf and exoplanet research. However, currently, most high-precision NIR observations are conducted from space observatories. Ground-based observations in the NIR have proven to be challenging due to NIR detector instabilities and strong telluric contamination (including atmospheric absorption in the NIR). This project aims to enable high-precision photometry from the ground using a First Light Imaging C-RED2 camera operating in the short-wave infrared (SWIR) (0.9 - 1.7µm wavelength). We aim to leverage the high-speed, low-noise capabilities of this camera to mitigate previous NIR detector shortcomings to achieve 0.1% relative photometric precision.
Optical Microphones
Aniketh
coming soon