Current Projects
Entanglement of Quantum States
Aaron, Neil
External mentor: Tianci Zhou, Virginia Tech Center for Quantum Information Science and Engineering
The proposed research aims to explore the level statistics of quantum Hamiltonians. In this case, the energy levels are the eigenvalues of the Hamiltonian operators. Recently, we have seen such statistics applied to quantum circuits. We aim to study the properties of a quantum system using its eigenvalues via random matrix level statistics. In addition, we also aim to look at the entanglement of eigenstates in quantum scars. Eigenstates typically have volume-law entanglement to be consistent with thermalization. Quantum scars, however, are a special class of quantum systems, a subset of which can have area-law entanglement (entanglement scales with area, not volume; low entanglement), allowing them to periodically revive the wave function rather than trend towards thermal equilibrium.
The intellectual merit of this research lies in the potential of our work to develop our understanding of entanglement in many-body systems. For Project 1, we will build on the description of random matrix theory (RMT) of quantum chaos that predicts that the energy levels of chaotic Hamiltonians exhibit level repulsion and follow universal gap statistics. Project 2 will extend this to quantum circuits; we will explore whether the circuit spectra obey the same behavior to see if we can make a connection to quantum chaos. Conventional eigenstate thermalization predicts volume-law entanglement across the spectrum. Recent work has contradicted this by revealing quantum scar states with area-law (low) entanglement, causing dynamical revivals. By characterizing the entanglement structure of eigenstates in quantum scars, we aim to clarify when and how scars emerge.
Our results will guide us to design circuits that can emulate chaotic thermalization behavior or avoid it, based on use case. Our study of quantum scars may also lead us to develop protocols that exploit non-thermal states to enhance the lifetimes of quantum memory. Our work will generate datasets of spectra and gap ratios that can be utilized for machine learning approaches to state classification, or for educational purposes.
Quantum control of two level systems: Rotating Wave Approxmation
Chase, Adarsh, Aarya
EXTERNAL MENTOR: Hunter Nelson, Virginia Tech Center for Quantum Information Science and Engineering
A common tool for simplifying driven two-level systems is the rotating wave approximation (RWA). It applies when the driving pulse is weak compared to the transition energy difference and has a frequency near it.
However, in some instances, it would be interesting to go beyond this assumption and account for more general driving regimes.
Can we use the SCQC framework to account for noise that is induced under the violation of the RWA assumption?
Quantum Premier League: A Quantum-Inspired Model for Fantasy Team Optimization
Andrew
Introduction
The proposed research explores how principles of quantum mechanics can be applied to solve a real-world optimization problem: selecting the highest-scoring Fantasy Premier League (FPL) team under budget and formation constraints. We model this as a combinatorial optimization problem and express it as an Ising Hamiltonian following the general approach described by Lucas [1]. By doing so, the team selection problem can be represented as a system of qubits, where the ground state corresponds to the optimal lineup.
Intellectual Merit
Fantasy Premier League (FPL) is one of the world’s largest fantasy sports competitions, with over 11 million active players each season. At its core, FPL is a constrained optimization problem: managers must choose a squad of 15 players under strict budget and formation rules to maximize weekly and seasonal points. While most participants rely on heuristics, expert advice, or simple statistical models, this project introduces a novel approach – quantum-inspired optimization – to explore whether methods rooted in quantum mechanics can improve decision-making in such a complex, combinatorial space. Each potential player is treated as a qubit, a two-state quantum object, where the state 0⟩ represents “not selected” and 1⟩ represents “selected.” Before the lineup is chosen, the entire system exists in a superposition of many possible team combinations, much like a quantum system explores many configurations simultaneously. The Hamiltonian of the system encodes negative expected points (so lower energy corresponds to higher score potential) and penalty terms for budget, position limits, and team constraints. A quantum annealing-style process is then used to find the ground state, or the lineup with the lowest total energy, i.e. the mathematically optimal team [2].
Broader Impact
This research serves two purposes: it builds a predictive and prescriptive model for FPL that is objective, reproducible, and potentially superior to expert heuristics, and it helps students engage with abstract quantum concepts in a hands-on, tangible way. By translating team selection into qubits and Hamiltonians, the project demonstrates how principles from quantum mechanics can be applied to real-world decision-making problems. The resulting framework could be extended beyond sports: for example, to stock portfolio optimization, scheduling, or logistics planning. In addition, this project can serve as an educational bridge for classmates, showing how physics, computer science, and statistics can intersect to solve engaging, competitive, and widely relatable problems.
Hybrid Quantum-Classical Pipeline for Identifying Higher Order Correlations in Neurons
Caroline, Jennifer
Introduction
The proposed research aims to develop a hybrid quantum-classical pipeline to identify higher-order, or motif-level, correlations in neurons populations. We will analyze two-photon calcium imaging data collected from the mouse auditory cortex when mice are given auditory stimuli of different frequencies. Currently, we plan to use the data collected by Bowen et al. 2024. We will begin by pre-processing the data: identifying neuronal spikes, reducing dimensionality, and calculating correlation between neurons. The output will be a neuronal graph that is both directional and weighted: neurons are expressed as nodes, connections between neurons are expressed as edges, directionality indicates whether neuron A affected neuron B or vice versa, and the weights inform relatedness or possible motifs among neurons.
Intellectual Merit
The intellectual merit of this experiment is rooted in previously developed methods for analyzing calcium imaging data and constructing neuronal graphs. In the brain, neurons often form connected circuits where a firing event in one neuron can either activate or suppress firing in another neuron, or even a group of neurons (often called a functional subcircuit). An accepted method to represent these connections in this field is in the form of neuronal graphs. There are several approaches to creating these graphs classically, and the most recent papers follow the steps listed below:
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Data structure: Convert the calcium imaging data to a matrix where the rows represent neurons and the columns represent time points.
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Noise reduction: Denoise the calcium imaging data to separate the signal from complex physiological noise (there are a variety of methods for this, but we have yet to synthesize possible approaches).
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Similarity and correlation: Compare the similarity between neurons using a variety of metrics, such as Pearson correlation coefficients or transfer entropy values (accounts for temporality, and therefore enables the creation of directional graphs).
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Cluster analysis: Perform cluster analysis with k-means, Louvain community detection, or DBSCAN.
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Network analysis: If the data is represented as a network, statistics such as modularity, small-worldness, and calculated clustering coefficients can also be derived from the adjacency matrix of the neuronal graph.
However, motif identification within networks is an NP-hard algorithm and becomes slower with multiple constraints such as activation and suppression. We propose a neuroscience application of the QOMIC (quantum optimization for motif identification) algorithm developed by Ngo et al. 2025. Using QOMIC, we hope to outperform classical algorithms by identifying more motifs and reducing the computational expense. With the knowledge gained from the QOMIC implementation, we will develop our own quantum algorithm that aims to address QOMIC's weaknesses. Identifying more functional subcircuits and motifs in the brain can enable a better understanding of how different neurons interact with each other and respond to sound stimuli.
Broader Impact
Our research may indicate functional subcircuits in the brain. These dynamics are critical for understanding how groups of neurons collectively encode information and transition between neural states. Importantly, the patterns we identify may be extrapolated beyond the auditory system and reflect the general organizational motifs of the cerebral cortex. Many cortical regions share a similar layered architecture and exhibit comparable population-level computations, suggesting that higher-order coactivation patterns and network structures observed within the auditory cortex may also underlie processes in visual, somatosensory, and association cortices. Identifying recurring activation motifs and community structures in these areas could offer a deeper, systems-level understanding of how the brain encodes sensory information, supports memory, and produces complex cognitive functions such as decision-making and conscious perception. This approach thus provides a scalable and generalizable framework for exploring collective neural behavior across the brain.
A Hybrid Quantum-Classical Attention Mechanism for Efficient Large Language Models
Smaran, Ansh
abstract
Quantum Simulation of Elliptic Curve Crytography Arithmetic
Sophia, Luv
Introduction:
Our proposed research aims to explore the implementation of elliptic curve cryptography (ECC) arithmetic on quantum computers by developing quantum circuits for group operations across multiple elliptic curve models. Building on prior work that optimized affine Weierstrass addition formulas for Shor’s algorithm, we extend these approaches to alternative curve models, including Montgomery curves, Twisted Edwards curves, and curves defined over binary fields. Our main hypothesis is that while all these models yield functionally equivalent group laws, their quantum circuit cost, in terms of qubit width, depth, and $T$-gate count, will vary substantially. By systematically comparing models, we aim to identify which curve representations present the lowest-cost path for quantum cryptanalysis of ECC.
Intellectual Merit:
The intellectual merit of this project lies in broadening the resource estimates for quantum attacks on ECC beyond the standard Weierstrass form. While previous studies have primarily focused on affine Weierstrass coordinates over prime fields, classical cryptography routinely employs Montgomery and Edwards curves for their efficiency and complete addition laws. Moreover, binary fields $\mathbb{F}_{2^m}$ remain in use in certain standards, yet quantum resource estimates for these settings remain scarce. By constructing and analyzing reversible arithmetic circuits for these models, our work contributes new insight into the comparative security of different ECC families under quantum adversaries. The results will not only test the universality of previous findings but also advance the methodology for designing and evaluating quantum circuits for algebraic structures beyond integer modular arithmetic.
Broader Impact:
Evaluating quantum circuit costs across multiple elliptic curve models will provide the cryptography community with a clearer picture of which ECC variants are most vulnerable to quantum attacks, thereby guiding future cryptographic standardization and migration to post-quantum schemes. Beyond cryptography, our work develops reusable building blocks for reversible polynomial arithmetic (for binary fields) and modular inversion circuits, both of which can be leveraged in other domains of quantum simulation and number-theoretic algorithms.
Ansatz significance in sample-based quantum diagonalization
Olivia
EXTERNAL MENTOR: Dr. Jason Saroni, Virginia Tech
Introduction
The proposed research aims to answer a key quantum chemistry question in finding ground states of many-body systems. Sample-based quantum diagonalization (SQD) methods are an alternative approach to traditional Variational Quantum Eigensolvers (VQEs) for approximating ground states by projecting the Hamiltonian into a sampled subspace. As VQE is an iterative process, it can be computationally expensive as opposed to SQD. However, a requirement of SQD is that the circuit from which the subspace is created must be able to sample the ground-state wave function. We will explore the guidelines of such a circuit in hopes of making SQD a more efficient solution to finding the ground state of molecular systems.
Intellectual Merit
The intellectual merit of this experiment rests on choosing an appropriate ansatz such that sampling circuit electronic configurations creates a subspace that contains the Hamiltonian's ground state. This will allow us to diagonalize the Hamiltonian. There is already research on neural network-enhanced SQD approaches, sampling with time-evolution circuits, and on extending the use of SQD to find excited states rather than ground states of electronic systems, but this research will focus solely on SQD itself and sampling driven by variational ansatz.
Broader Impact
So far, scientists have shown that SQD succeeds in approximating ground states in active spaces of up to 36 orbitals and 77 qubits. By improving SQD, we can use this algorithm on more complex systems that take up more orbitals and qubits. SQD is critical to solvation modeling efforts, in which solutes and solvents are considered together in the same system. Ultimately, research on SQD can make leaps in efficiently solving for ground states of many-body systems, aiding in drug discovery and modern electronics development.
Quantum Dot-Enabled Biosensing of E. coli for Waterborne Pathogen Detection
Holly
The proposed research aims to explore the development of a carbon quantum dot-based detection system for Escherichia coli (E. coli). It builds off the findings and methodology of a 2023 QLab project and aims to refine methods to improve sensitivity. Quantum dots are versatile nanomaterials with unique optoelectronic properties due to its broad absorption bands. The optical properties of these nanomaterials are size-tunable and can be adjusted in a specific range of the electromagnetic spectrum by changing size.
Intellectual Merit
The intellectual merit of this study lies in advancing ongoing efforts in nanomaterials for precise biodetection. Traditional culture-based and immunological assays are reliable, but slow means of identifying E. coli contamination. Molecular methods, such as polymerase-chain reaction (PCR), can accelerate detection, but require specialized equipment and extensive technical expertise. These methods are labor-intensive, time-consuming, and inadequate for point of care water screening. The development of quantum dots (QDs), nanoparticles with unique electronic structures that confer narrow, size-dependent emission spectra under excitation, present a new avenue of research. Previous demonstrations of nanoparticle-biomolecule conjugation suggest that these fluorophores could be adapted for biosensing applications, yet achieving organism-specific recognition, particularly to E. coli, remains a challenge. This project seeks to develop a novel method for specifically conjugating carbon QDs to E. coli and exploiting the fluorescence properties of QDs for rapid detection.
Broader Impact
The capacity to rapidly and affordably detect E. coli has far reaching implications for public health, environmental safety, and social equity. Globally, unsafe water, poor sanitation, and inadequate hygiene remain among the leading contributors to diarrheal disease and mortality, especially in low-income regions. According the the Centers for Disease Control and Prevention (CDC), waterborne pathogens are estimated to cause over 7 million illnesses, 118,000 hospitalizations, 6,630 deaths, and direct healthcare costs exceeding $3.3 billion annually in the United States alone. Approximately 1.6 million people die each year from waterborne diseases worldwide. By identifying a novel system of on-site, low-cost, and rapid detection of E. coli, the proposed QD biosensor system could transform water quality monitoring practices.
Generalizing commuting operators for ADAPT-VQE
Olivia
EXTERNAL MENTOR: Dr. Bharath Sambasivam, Virginia Tech
Introduction
The proposed research aims to improve the traditional approach of Variational Quantum Eigensolver (VQE) for finding the ground state of many-body systems. Used in quantum chemistry and molecule simulations, VQE is a hybrid quantum-classical algorithm that works by taking energy as the cost function. VQE relies on an ansatz, or parametrized guess wave function, which has led to much research regarding the goal of creating a compact ansatz that provides high accuracy with few parameters and shallow circuits.
Intellectual Merit
One major advance, ADAPT-VQE, introduced adaptivity into ansatz construction by iteratively selecting operators from a predefined pool based on gradient magnitudes, leading to more compact and circuits. Building on this, TETRIS-ADAPT-VQE relaxes the sequential one-operator rule by adding multiple operators to act on different qubits simultaneously, significantly reducing circuit depth. TETRIS-ADAPT-VQE highlights the role of operator selection. The use of commuting operators is something we plan to look into to extend this line of improvement. By partitioning Hamiltonian terms and excitation operators into commuting sets, we can potentially enhance parallelism in operator insertion and reduce circuit depth.
Broader Impact
Incorporating commuting operators into adaptive VQE frameworks such as TETRIS-ADAPT-VQE has the potential to substantially improve the scalability of quantum simulations on noisy intermediate-scale quantum (NISQ) devices. These advances directly target the primary bottlenecks of circuit depth and measurement cost. If successful, this research could pave the way for near-term quantum advantage in simulating materials, thereby expanding the reach of quantum computing in science and engineering.
Synthesis of Nano-crystalline (Red) Structurally Colored Ceramics
Eva, Siwen
Introduction:
Our project aims to expand the fabrication of stable ceramic-based structural colors beyond the blue/green range of the visible light spectrum. We hypothesize that this can be done using a non-reflective material similar to the work of Li et al. who used liquid photonic crystals as a coating. A multi-layered computation model that incorporates single-particle scattering, structural resonance, multiple scattering, and full wave verification using a combination of Mie, S(q), RTE/MC, and FDTD will be used to first simulate the desired outcome and then fabricate the product through high-temperature crystallization.
Intellectual Merit:
Red has been difficult to fabricate as a stable, angle-independent structural color because of the internal "polluting" of blue wavelengths, which can dominate resonance. Other methods of reproducing red structural color has been done through colloidal and self assembly as well as atomic layer deposition. This project, combined with a recent 2024 study that was able to fabricate green/blue nano-structural color, would achieve a full spectrum method of ceramic-based structural color fabrication.
Broader Impact
Finding new ways to synthesize structural colors beyond red can open the doors to new possibilities in reducing the needs for artificial coloring that often is produced from petroleum byproducts and harms the planet, or natural dyes from rare earth metals or hard to find natural materials. Expanding the range of structural color can also open doors in industries that rely on color to be preserved for a long time, such as in art restoration, where having color that is immune to UV damage from destroying/ bleaching pigments off, or compounds in pigments that are reactive. Nanoparticles are topics of interest in industries that work with color.
Topological Graphs and the Quantum Transition to encode information topologically
Tanush
This research advances graph theory and its applications by introducing a novel framework in which edges are represented not as static scalar weights, but as vector-valued functions. Through this approach, graphs are encoded as webs of B-splines, enabling edges to capture topology, geometry, and node significance in ways that linear definitions cannot. The spline structure allows connections to self-adjust while preserving smoothness and local control, creating a bridge between discrete combinatorial structures and continuous geometric representation.
Topological information is incorporated through Hodge Laplacian, which provide spectral invari- ants of the original graph. These invariants can then be expressed within the spline web and further compressed into classical geometric invariants—such as line integrals, curvature, and torsion—using the Frenet–Serret framework. This interplay between discrete topology, spline geometry, and in-variant compression highlights the adaptability of the model, ability to switch between invariants,and allows for high expression to be encoded into the edges.This research extends our own spline-based graph framework into quantum mechanics by introducing the Quantum Topographical Spline Basis (QTSB). QTSB redefines spline recursion in terms of quantum normalization and phase, creating a Hilbert-space formulation that adapts B-splines to quantum principles. This basis provides a mathematically rigorous way to encode qubit information with compactness and expressiveness, while remaining consistent with the rules of Quantum Mechanics. By replacing Cox-De Boor’s Partition of Unity with our new Partition of Squared unity and adding an additional parameterizing term of phi to the Basis vector N, I am able to introduce imaginary numbers and splines that are able to not only add together but also have constructive and destructive properties which is extremely useful. It also manages to encode several nodes together in one state, and relate topological information with them.
This research has the potential to influence multiple areas of science and technology by providing a unified framework that links graph theory, geometry, and quantum computation. On the classical side, a spline-based graph representation offers new tools for analyzing networks in fields such as transportation, communication, and data science, where richer edge structures can capture geometric and topological properties that traditional graphs overlook. Such capabilities could lead to more efficient routing algorithms, improved models of physical systems, and deeper insights into the structural properties of large-scale networks.In the quantum domain, the Quantum Topographical Spline Basis (QTSB) introduces a novel quantum basis that can completely revolutionize how we see quantum. Because my QTSB is not normalized in each pair but in each shifting pair, if offers a unique opportunity to use spline advantages and apply them to a quantum-system that meets all the requirements but rather than local normalized is adjacency normalized(a term I invented). This allows you to essentially be able to use in quantum mechanics. In addition, because degree is different in the quantum sense, it essentially allows to recur over superposition. Similar to a Bell State, it allows you to superpose over a superposed state, but unlike the bell curve, it has no limit on the degree you place except the limits faced computationally or for the problem at hand. This also encodes many nodes together and also forms the basis of quantum graphs represented as spline webs.
Proof of Quantumness
Devin
EXTERNAL MENTOR: Atul Mantri, Virginia Tech.
Quantum Teleportation of Information with Diamonds
Anneli, Kara
The proposed research aims to explore the ability to teleport information into macroscopic diamonds to be then sent to a seperate output source. Adding onto an experiment by Hou et al., we will send femtosecond laser pulses from a Ti–sapphire laser into a diamond to produce a Stokes photon, which is an excitation in the phonon mode. We will then prepare an input state on the photon’s polarization degree of freedom, allowing the photon to carry two qubits, one by its polarization and one by its spatial modes. We will then perform Bell measurements, which will then allow the phonon state to be projected to the same state input as the photon’s polarization. A second ultra fast laser pulse will be applied to the diamond to convert the phonon back to an anti-Stokes photon. The result will then be compared to the original photon’s polarization state [1]. Intellectual Merit: The intellectual merit of this experiment rests on and directly tests the ability to store information as vibrational states within a diamond. Diamonds are used as a good qubit system because of their long coherence for spins [2]. Chaudhary et al. developed theoretical protocols to teleport macroscopic quantum states [3]. By teleporting information into a macroscopic, nitrogen- vacancy diamond, we are working to experiment on a protocol to store information in a diamond in a secure manner. Broader Impact: By successfully using quantum teleportation utilizing a nitrogen-vacancy center, we would create fundamentals for storing information within diamonds for long periods of time as well as develop a safer way to send information through quantum computers. Our research would lay the foundation for further research on large storage methods for quantum computers that are resistant to hacking and other data breaches.
NV Diamond Based Magnometer and its Applications
Eric, Harry, Isaac
The proposed research will aim to utilize Nitrogen Vacancy (NV)-Diamond based Continuous Wave-Optically Detected Magnetic Resonance (CW-ODMR) Magnetometers to measure magnetic fields with targeted sensitivity. The proposed NV-Diamonds to be used in the contraption will be grown in the lab. Once a magnetometer has been constructed, we will apply it in a small-scale GPS scenario within TJHSST, where we will attempt to map out the magnetic field throughout TJ and use subsequent data for positioning within the campus. This application will serve as a proof-of-concept for a larger scale implementation of the device.
The intellectual merit of this experiment stems from a recent paper by Bülau, Walter, and Fritz published in August of 2025 that details the creation of a low-cost CW-ODMR Magnetometer. A typical Magnetometer requires a light source, color filter, TIA (Transimpedance amplifier), a microwave structure, and microwave generator. Bülau et al. opted to use an LED (instead of the traditional laser) as a light source, a single stage TIA from Texas Instruments, and a low cost microwave generator. Their results prove promising for a low cost, lightweight, and compact contraption to serve as a magnetometer
An Agent Based Model of the Elderly Care Market for Technological Services
Chenxi
abstract
Effects of Symmetry Breaking in Trotterized Real-Time Dynamics
Ayla, Alexandra
EXTERNAL MENTOR: Dr. Bharath Sambasivam, Virginia Tech Center for Quantum Information Science and Engineering
The proposed research aims to develop symmetry-preserving algorithms for simulating time-dependent quantum dynamics on quantum computers. Building on foundational work in quantum simulation and the use of product-formula (Trotterization) methods to approximate complex Hamiltonians, we will investigate new approximation strategies that respect conserved quantities inherent to the target system, such as particle number or total spin projection for symmetry-aware simulation frameworks. Conventional Trotterization decomposes the full interaction into a sequence of simpler unitary operations that can be implemented on quantum hardware, but this process can break the exact symmetries of the system, therefore allowing the quantum state to leak into other irrelevant subspaces.
The intellectual merit of this project lies in advancing the theoretical and algorithmic foundations of quantum simulation by directly addressing one of its central limitations: the breaking of phys- ical symmetries during time-evolution approximations. Conventional product-formula (Trotter) methods decompose complex interactions into implementable unitary steps but can also violate conserved quantities such as particle number or spin projection. Recent theoretical proposals have emphasized the importance of symmetry-adapted bases and constrained dynamics for improving quantum-simulation fidelity; however, a systematic framework for constructing such approximations has not yet been developed.
Beyond its immediate scientific contributions, this project will broaden the impact of quantum simulation by making it more resource-efficient and physically accurate. Additionally, this will accelerate its application to problems of chemical reactivity, materials discovery, and energy con- version.
Using QEC to Improve Accuracy of Survey-Based Population Synthesis for Small Area Estimation
Aanya
EXTERNAL MENTOR: Dr. Taylor Anderson, George Mason University
The proposed research aims to improve the accuracy and stability of small-area estimation (SAE) for health indicators by applying quantum error correction (QEC) to survey-based synthetic population generation. SAE is a critical tool in public health research, allowing localized estimates of health outcomes such as chronic disease prevalence, obesity rates, or vaccination uptake, even when survey data are sparse. Traditional methods like iterative proportional fitting (IPF) and combinatorial optimization adjust microdata to match population-level constraints, but these methods often yield unstable or highly variable results in small geographic areas. Current models typically achieve predictive power in the range of R² = 0.3–0.8 when validated against real-world surveillance data, but exhibit wide fluctuations across repeated runs.
The intellectual merit of this project rests on bridging quantum information science with applied survey-based modeling. Quantum error correction has enabled progress toward fault-tolerant quantum computation by reducing decoherence through redundancy and structured codes. Translating these principles to small-area estimation represents a novel research direction: error-correcting codes would be adapted to constrain survey synthesis, and QNN architectures could be trained to recognize and correct error-prone adjustments during IPF. By directly comparing baseline SAE methods with QEC-enhanced approaches, the research will quantify whether QEC principles improve both the accuracy and the reproducibility of predictions. This work contributes to both computational social science and quantum-inspired algorithm design by demonstrating the transferability of quantum stability methods to statistical inference.
The broader impact of this research lies in its potential to improve public health decision-making at the community level. Reliable SAE estimates are used by health departments to allocate funding, design interventions, and track disparities in underserved populations. If QEC-based methods reduce variability and increase reliability, local policymakers will have access to more consistent data to guide programs such as vaccination drives, diabetes prevention, and mental health outreach. Beyond health, the project demonstrates the interdisciplinary potential of applying physics-inspired methods to the social sciences, offering a model for cross-pollination across fields. Educationally, the project will provide a case study for introducing quantum computing concepts into applied statistics, inspiring students and researchers to explore novel intersections between emerging technologies and practical societal challenges.
Exploring the Mathematical and Physical Foundations of Relativity
Anant
This project aims to explore and understand the mathematical and physical foundations of Einstein’s theories of special and general relativity. Rather than beginning with a fixed research question, the goal is to develop a strong conceptual and mathematical grounding in relativity and use that foundation to identify a specific topic for deeper investigation later in the year.
Our study will emphasize the connections between geometry, algebra, and physics in describing spacetime. We will begin with special relativity, covering Lorentz transformations, spacetime intervals, and the Minkowski metric, and gradually extend to general relativity, focusing on curvature and tensor analysis. By doing so, we aim to gain a comprehensive understanding of how mathematical structures encode the behavior of space, time, and gravity.
The broader value of this project lies in its emphasis on deep theoretical understanding before specialization. This approach mirrors the way physicists build research questions from first principles, allowing us to approach advanced topics in relativity with both rigor and clarity.
Multiparticle Quantum Control via Plasmonic Near-Fields on Integrated Photonic Chips
Anusha
Introduction:
This research aims to learn how plasmonic near-fields, and those embedded in integrated photonic architectures can be used to control multiphoton quantum states. Traditional quantum photonics rely on probabilistic entanglement sources such as spontaneous parametric down-conversion and four-wave mixing, which suffer from scalability and loss limitations. In contrast, plasmonic nanostructures have the potential to support strong optical near-fields that can couple directly to photons with high spatial confinement, enabling deterministic entanglement. We hypothesize that by engineering metallic nanostructures and integrating them onto a photonic chip, it is possible to steer multiphoton interference, suppress decoherence, and enable deterministic quantum control of entangled photon states. Unlike conventional dielectric photonic platforms, plasmonic near-fields are dissipative, but this dissipation can be engineered to guide multiphoton scattering trajectories.
Intellectual Merit:
The intellectual merit of this project lies in combining nanoelectronics, plasmonics, and integrated photonics to achieve quantum control at the hardware level. While theoretical work in quantum optics often consider photons as weakly interacting and easily decohered, the introduction of plasmonic near-fields allows for photon/photon interactions with high precision. In particular, we aim to design/simulate chip-scale circuits in which waveguides route single photons into plasmonic nanostructures that allow for controlled interference and scattering. By tuning these near-fields, we will test whether multiphoton coherence can be protected, moving toward deterministic entangling operations that are currently missing in most photonic quantum architectures.
Broader Impact:
The potential impact of this research extends across quantum information science, secure communications, and sensing technologies. If we are successful, the integration of plasmonic nanostructures into photonic chips could enable compact and deterministic entangling devices, providing a foundation for scalable photonic architectures. The same principles could be applied to on-chip processors for quantum key distribution, making them especially relevant for free-space or satellite-based communication networks where size, weight, and integration are critical. The precise control of multiphoton interference could also significantly enhance the sensitivity of photonic interferometers, offering improvements for quantum-enhanced sensing platforms that may benefit navigation, Earth observation, and other aerospace applications.
Magneto-Optical Trap
Soren, Christoph, Xavier
The proposed research aims to advance the Quantum Lab’s in progress magneto-optical trap (MOT) which has been worked on over the past two years. We hope to fully understand, complete, and improve the current doppler-free saturation spectroscopy (DFSS) setup in the QLab and rework the design for the vacuum chamber. Doing so would allow us to trap rubidium atoms using lasers and magnetic fields, allowing us to cool the atoms and bring forth properties not found in standard conditions.
The intellectual merit of this project lies in its subatomic nature. Our goal is ambitious in that we seek to manipulate the energy states of atoms and then cool them to temperatures nearing 0K. Once cooled, the Rubidium atoms will be one step closer toward becoming a Bose-Einstein-Condensate, a state of matter with unique quantum properties that is a significant topic of quantum research in the academic scene today. In such a condensate, a large part of the atoms occupy the lowest quantum state, making quantum properties appear on a macroscopic scale and allowing for previously impossible experiments. To do so requires a theoretical and experimental understanding of lasers, rubidium, and electromagnetic properties.
The broader impacts of this research are profound and far-reaching. Magneto-Optical Traps produce ultracold neutral atoms, which have a myriad of applications in the Quantum and broader Physics Field. Cold atoms are essential for quantum computing, quantum simulation, and other quantum measurements. They are the first step in producing Atom interferometers, which are used in gradiometers and other sensory technology. The production of controlled precision atoms enables use cases such as atomic clocks. Cold atoms are especially relevant in undergraduate physics research, as they allow students to explore these advanced concepts in a hands-on manner, which is exactly what we seek to allow TJ’s research program to do in the future. Because of these far reaching possible use cases, the Magneto Optical Trap will serve to better the QLab as a powerful educational tool for future projects and students.
Precision AFM Measurement of the Casimir Force with Sphere–Plate Geometry and Exploratory Simulation
Nivaan, Nived
abstract
Secure computation in the Quantum Era
Ethan
abstract
SCQC project 4a: Going beyond the rotating wav approximation
Medhansh
abstract
Randomness and symmetries in ADAPT
Anish, Ibrahim, Clinton
abstract
Quantum Mean-field Multi Agent Reinforcement Learning
Rishi
abstract
Quantum Attention Deep Q-Network for Financial Market Prediction
Amrit, Abhiraj
abstract
Quantum Agent-Based Modeling for Disease Dynamics
Ryan
abstract
Optical Tweezers to Aid With Cell Movement
Lulu, Ishita
abstract
Observing and Simulating Effects of Spin on Drag for a Soccer Ball
Kaleb
abstract
Magic and Entanglement for ADAPT-VQE
Haasini, Michelle
abstract
Grover vs. Symmetric Cryptography
Jaeyoon, Caroline, Jennifer
abstract
Efficient Device Independent Quantum Number Generator Design and Analysis
Max
abstract
Detecting Long Period Transients with VLITE
Bowen, Anavi
abstract
Casting Composite Pulse sequences within the SCQC framework
Anna
abstract
A Quantum-Inspired Evolutionary Approach to Supplementing Dynamic Trim Control in Rockets
Taran
abstract