Recently Updated Projects

Quantum key distribution (QKD) allows two parties to establish a shared secret key while detecting whether an eavesdropper has intercepted the communication. In the BB84 protocol, information is encoded using four quantum states divided between two measurement bases. Because measuring a quantum state can disturb it, an eavesdropper attempting to intercept the transmitted information introduces detectable errors. Although BB84 is well established, many QKD systems still rely on separate fiber and bulk optical components arranged across a laboratory setup. These systems can be large, sensitive to alignment, and difficult to scale or reproduce. Integrated photonics offers a way to address these limitations by placing many of the same optical functions onto a compact chip.

Our project will design an integrated photonic circuit that performs the optical operations required for BB84 using dual-rail encoding. The circuit will use waveguides, directional couplers, phase shifters, and Mach-Zehnder interferometers to prepare and measure the four BB84 states. We will first design and simulate individual components in Tidy3D, then combine them into a complete circuit model and optimize parameters such as waveguide dimensions, coupler geometry, optical loss, and phase. The final design will be prepared for fabrication at the U.S. Naval Research Laboratory. If the chip is fabricated, we will measure its loss, splitting ratios, interference visibility, and state error rates and compare the experimental results with our simulations.

A major focus of the project is understanding how small changes in individual photonic components affect the performance of the complete QKD circuit. Parameters such as directional-coupler gaps, waveguide widths, and phase shifts can vary during fabrication, and these variations can change the splitting ratios and interference behavior of the circuit. By systematically modeling these effects, we can determine which parameters have the greatest influence on the circuit's intrinsic error rate and which errors can be compensated after fabrication. Comparing the fabricated chip with the simulated design will also allow us to evaluate how accurately our models predict real hardware and identify which assumptions or fabrication tolerances are most important.

Beyond the immediate experiment, this work contributes to the broader goal of making quantum communication hardware smaller, more stable, and easier to scale. Integrated photonic circuits can replace many alignment-sensitive optical components with structures fabricated together on a single device, which could make future QKD systems more compact and reproducible. Within QLab, the project will also establish a reusable workflow for moving from photonic simulation to circuit design, fabrication, and experimental testing. The models, layouts, test structures, and measurement procedures developed through this project could serve as a foundation for future work in integrated quantum photonics and quantum communication.

Game theory is a field of applied mathematics that models interactions between independent agents, called players, in games of strategy. Game theory analyzes each player’s choices and its impact on both the overall game and every other player’s choices [1]. One of the most studied games in game theory is the Prisoner’s Dilemma [1]. In the classical version, two players, called Alice and Bob, must choose to cooperate or defect. If both cooperate, they both receive one year. If both defect, they both receive five years. If one defects and the other cooperates, the defector will get 0 years and the cooperator 10. Logically, both will defect, because if the other cooperates, defecting will get them no punishment, and if the other defects, getting five years is better than getting 10. This is called a Nash equilibrium, a game state where no player can individually benefit from changing their option [1]. Even though it would be Pareto-optimal, or best overall for both to cooperate, defecting gives players the greatest payoff [2]. Quantum game theory allows players to use quantum strategies, i.e., apply gates to qubits. Often, this allows for communication purely through the player’s choices. This project will test several questions: Can a quantum player achieve a greater payoff than a classical player by using quantum strategies?. Can quantized games achieve Pareto-optimal Nash equilibria where classical variants fall short? This project will use the Eisert-Wilkins-Lewenstein (EWL) scheme to ”quantize”, or make quan￾tum, the Prisoner’s Dilemma. We will also quantize different versions of the Dilemma, including a version where players may choose to abstain and a version with n players. We will then compare by simulating each version using Qiskit, measuring each player’s theoretical and simulated payoff.

This project will leverage the principles of quantum mechanics to analyze variants of the Prisoner’s dilemma for differences in payoffs and shifts in the Nash equilibrium. Viewing game theory through a quantum mechanical lens can give rise to new optimal strategies in these games that are absent when viewing game theory through only a classical lens [3].

Game theory models arise in several disciplines, including economics and network flow. Much of the work that has been done in the field uses a classical model. Applying quantum mechanics to these settings allows us to gain a new framework for understanding them. In addition, the use of quantum strategies in game theory may resemble real-world interactions like those occurring in finance or communication networks [3]. Finally, this project can also provide a precedent and inspiration for future QLab projects on quantum game theory, as it is one area where work is less plentiful.

Introduction: Diabetes management depends on measurements that are often invasive, intermittent, or expensive. Acetone in exhaled breath is linked to ketone metabolism and can be measured optically, but a breath spectrum is not itself a metabolic state: the clinically meaningful quantities—blood β-hydroxybutyrate (BHB), acetoacetate, acetone, and their rates of change—must be inferred from noisy, indirect observations. Prior cavity-enhanced systems have detected breath acetone near 1670~nm, establishing a realistic optical starting point. Earlier QLab students also demonstrated the feasibility and practical limitations of student-built breath-acetone spectroscopy.

We propose a coupled computational and experimental project. Mohan will extend a verified slow-fast ketone model and classical physics-informed neural network (PINN) with a small variational quantum layer, then use quantum Fisher information (QFI) to quantify which model parameters and optical features are most identifiable. Aarjav will design and fabricate a Fabry-Pérot interferometer, select light-emitting diodes and a near-infrared spectrometer, and characterize its resolution, stability, and acetone sensitivity. QFI-guided priorities will inform wavelength and component choices; measured instrument noise and spectral response will then be returned to the inverse model. The project will test whether this feedback loop improves reconstruction accuracy and uncertainty relative to matched classical baselines, without presuming a quantum advantage or requiring a large number of qubits.

Intellectual Merit: The project connects mechanistic multiscale modeling, hybrid quantum-classical learning, quantum information geometry, and a real optical instrument in one identifiable inverse problem. Beyond investigating quantum modeling applications and optical sensing, this project could result in a practically useful tool for monitoring a disease impacting hundreds of millions of people worldwide.

Broader Impact: A low-cost, noninvasive method for tracking ketone dynamics could ultimately support earlier warning of dangerous metabolic changes. In particular, a byproduct of our research could involve a tool for predicting diabetic ketoacidosis (a dangerous complication of diabetes caused by insufficient insulin and rising ketones) without requiring skin insertion. With that said, this project serves as a feasibility study, not a diagnostic device, and any faculty breath study will proceed only with required approval and informed consent.

Deception can produce involuntary and measurable changes in cardiovascular response. For example, significant differences in heart rate have been found when comparing truthful and deceptive responses (Godert et al. 2001). Atomic magnetometers can achieve subfemtotesla sensitivity to magnetic fields without the cryogenic cooling required by superconducting sensors (Kominis et al. 2003). More recent OPM designs have been used directly for human magnetocardiography (Kim et al. 2019).

Rather than measuring cardiac activity through conventional electrical or mechanical sensors, this project proposes using a rubidium optically pumped magnetometer (OPM) to measure the magnetic field generated by the heart. The objective is not to demonstrate the validity of magnetocardiography, but to construct and validate the experimental capability and then determine whether heart activity as measured by the rubidium OPM can significantly distinguish truthful responses from deceptive ones.

Intellectual Merit: The project will apply quantum sensing techniques to cardiovascular responses associated with deception. Its scientific value is the construction and characterization of rubidium OPM, which uses optical pumping to polarize atomic spins and detect extremely weak magnetic fields for noninvasive observation of deceptive signals. The final result will be a documented assessment of whether deception-associated cardiac activity is detectable magnetically and whether a rubidium OPM is sensitive and stable enough for this type of measurement.

Broader Impact: A functioning and characterized rubidium OPM would provide QLab with a reusable experimental capability for future investigations involving weak magnetic fields and quantum biological sensing, as well as possible extensions to other fields such as geophysics and underwater surveillance. The documented design and operating procedures could also serve as a starting point for future student projects. The study will help establish both the capabilities and limitations of applying quantum to human physiology.

Introduction:

Infrared sensors are currently the most popular method for industrial temperature mapping, but they have limitations when applied to complex materials and micrometer-level scales. For example, infrared wavelengths can range from 0.7μm to 20μm. In addition, factors such as the emissivity of a material, as well as changes in ambient temperature and humidity, drastically influence the accuracy of infrared sensors. Therefore, the aim of this project is to investigate an alternative option to thermal sensing that is more effective for mapping changes across microscopic electronic components. This project will utilize the nitrogen-vacancy (NV) center point defects inside a diamond plate to create a sensor capable of mapping thermal signatures across microchips on a micrometer scale. The goal of this project is to replicate the experimental procedure described by [1] and evaluate its accuracy by mapping heat signatures across resistors, capacitors, and transistors, gradually working up in complexity until finally mapping a microchip.

Intellectual Merit:

The research advances the field of quantum thermal mapping by establishing a high-resolution, non-invasive wide-field imaging platform that uses the temperature-sensitive spins of NV centers in diamonds. By using optically detected magnetic resonance, our project will account for interference from separate magnetic-field effects, which have been major problems for NV thermo-mapping in the past. It also pushes the boundary of thermal sensing on a microscale, offering an alternative to traditional thermal mapping by providing highly precise localized temperature mapping without disrupting the electronics of the chip.

Broader Impact:

The study helps the semiconductor and microelectronic industries by giving them a diagnostic tool to monitor thermal data in the new generation’s complex chips. By giving these industries a way of early detection of hot spots and other thermal stresses signifying structural issues, this imaging technology will help reduce error rates in microchip manufacturing and maintenance and improve the development of reliable electronic hardware. By integrating quantum mechanics, material science, and thermal engineering, it provides a foundation for future STEM professionals in the field of microelectronic technologies, which is a high priority for many nations around the world.

put introduction, intellectual merit, and broader impacts here

Physics-informed neural networks (PINNs) solve differential equations by training a neural net- work while penalizing violations of the governing equation and boundary conditions in the loss function [1]. Quantum physics-informed neural networks (QPINNs) extend this idea by embedding a parameterized quantum circuit within part of the classical network [2, 3]. This project will first test how three approaches solve the same one-dimensional Poisson prob- lem on $x∈[−1,1]$: a classical numerical method, a classical “vanilla” PINN, and a hybrid QPINN containing a quantum layer implemented with the Python library PennyLane. A fourth imple- mentation will discretize the equation and apply the Harrow–Hassidim–Lloyd (HHL) quantum linear-systems algorithm as a distinct quantum baseline [4]. The prescribed profile $u_0(x) = sin(πx)$ will be treated as a manufactured exact solution, since the time-independent Poisson equation has boundary conditions rather than an initial condition. After the 1D implementations are validated, the numerical, PINN, and QPINN pipelines will be extended to a 2D Poisson equation on $[−1,1]^2$; HHL will also be tested in 2D if the required circuit is worth it. We will measure relative L2 solution error against the exact solution, physics residual, convergence, training time, circuit resources, and variation across repeated runs. The study will alow the quantum methods to improve, match, or underperform the classical methods.

The project will isolate the effect of a quantum layer through controlled benchmarking rather than evaluating whether a hybrid model can produce a reasonable solution. Comparing the QPINN with a matched classical PINN, an exact solution, a conventional discretization, and HHL may clarify whether observed differences arise from representation, parameter count, optimization dynamics, discretization, quantum noise, or computational overhead.

Clear negative or neutral results are useful in quantum machine learning because they help identify where quantum components should be used and whether their use is justified. The benchmark, code, and analysis workflow developed here could also be reused by future QLab projects evaluating hybrid scientific-machine-learning models or quantum algorithms for computational fluid dynamics (CFD) and partial differential equations more generally.