Current Projects
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.
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.
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.
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.
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.
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. In this research project, we aim to explore the potential of Physics Informed Neural Networks (PINNs) to approximate the ground-truth state FIM in predicting quantum phase transitions. We hypothesize that the PINNs will perform faster in predicting the ground-state FIM values by introducing normalization layers into previously explored architectures, embedding the system with knowledge about the quantum phase itself.
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.
Optically Switched Liquid Crystal Cells to Create Multistable Waveplates
Kalib
Utilizing variable alignment layers and chemical switches inside of liquid crystal cells, optically controlled retardation is possible. This has possible future applications in optical computing, and optical quantum computers, as it skips any previously required electromechanical components.
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.
A Quantum Internet Transmission Protocol via Dynamic Generation and Distribution of Entangled Qubits
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 generated entangled qubit pairs to ensure greater efficiency in information teleportation via a quantum network. The framework will be validated through both computational simulations and physical experimentation.
GLASS: Glucose Level Measuring via Breathe Acetone Sensing through Laser Spectroscopy
Akul, Rohith
The proposed research aims to develop a laser spectroscopy analyzer that leverages classical machine-learning algorithms to analyze laser spectra for the detection of glucose levels and sensitivity in breath samples. This innovative approach seeks to emulate the functionality of a breathalyzer while providing a non-invasive method for monitoring metabolic health. Our hypothesis is that the analyzer will effectively distinguish and quantify specific spectral signatures associated with acetone levels in breath, allowing for an accurate assessment of glucose sensitivity through advanced data processing techniques. We are currently developing our drying mechanism and multi-pass gas chamber to establish laser spectra from breathing and analyze acetone content.
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.
Compare QAOA with quantum annealing to optimize electric vehicle charging on motorways
Anmol
This research aims to address the issue of scheduling the charging electric motor vehicles (EMV) on motorways. It uses two effective quantum optimization algorithms: quantum approximate optimization algorithm (QAOA) and quantum annealing (QA). The cost function will consider the following factors in order to optimize experience for drivers on roads: distance between charging station and starting point, chargers available, available power at charger, vehicle speed (corresponds to larger power usage), EMV battery capacity, energy in EMV battery, charging time per charger, traffic density, charging station capacity, and predicted demand of charging stations at a given time. Currently, I am focusing on QAOA and applying it to the MaxCut problem, a similar optimization problem also considered NP-hard. This is the step leading to the implementation of QAOA in the EMV situation.
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.
MRIO-QAOA: 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 principles of the Quantum Approximate Optimisation Algorithm (QAOA) to simulate and optimise environmental policy interventions in an MRIO model. Within this simulated pseudo-economy, we 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.
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.
Abnormalities of Chaotic Systems found in the Entanglement States of Quantum Scars
Victoria
Partnering with VTech Dr. Tianci Zhou to investigate the entanglement of many-body systems. Looking to also numerically represent the entanglement of eigenstates of Hamiltonians, which will be used to reproduce the abnormal low entanglement eigenstates found in the quantum scars phenomenon.
Doppler-Free Saturation Spectroscopy with Rb
Ophelia
Atoms nominally move frenetically. One way to slow, or freeze, atoms, is to use a Magneto-Optical Trap (MOT). The "Optical" part of the MOT refers to using a laser to impede the atom's motion --- essentially like someone trying to walk forward while someone is pushing them backwards. However, in order for the laser to effectively interfere with the atom, the laser must be incredibly finely tuned, or locked, to the atom's unique vibrational frequency. Doppler-Free Saturation Spectroscopy is one such method of laser locking. One common atom used in MOTs is rubidium. This is the atom I will be using to construct a DFSS apparatus, hopefully in future service of TJ producing a MOT-like object. MOTs are useful across many fields, including, but not limited to, Bose-Einstein condensates, atomic clocks, and quantum computers.
Quantum Harmonic Oscillator Model of Index Fund Return Distributions
Harry
This project uses the quantum harmonic oscillator to model past return distributions of the S&P 500 index fund. Quantum harmonic oscillators have a restoring force that pulls the system back to equilibrium, similar to how stock prices revert to their mean. In quantum models, energy levels take on specific values, which can better capture the discrete nature of transactions and price changes in the stock market. Stock prices change in discrete steps due to individual transactions. Each trade can be seen as moving the stock price from one “energy level” to another. Due to its energy levels, quantum oscillation can help with more accurate modeling. Moreover, quantum oscillations are described by wave functions which can give probability distributions to measure for a particular position. The added variability in quantum oscillations can tell more about volatility and better model non normal distributions of stock price. Modeling the stock market using quantum oscillations can be useful for companies and businesses at large for their investments. This includes allowing for better investing such as more accurate financial analysis that can better predict market interactions and changes. This could lead to more effective use of financial tools and better risk management through short and long term investments. Quantum oscillations for stock markets can also lead to interdisciplinary applications such as combining physics with data and economics. This can provide experts and students with a comprehensive understanding of market dynamics and advanced modeling techniques.
Optical Microphones
Aniketh
coming soon