Recently Updated Projects

Introduction: Quantum computers must detect and correct physical errors without directly measuring the encoded quantum state. Surface codes address this problem by repeatedly measuring local stabilizers on a two-dimensional qubit lattice. Changes in those measurements, called detection events, reveal where an error may have occurred, but they do not identify a unique physical-error pattern. A decoder must instead choose any correction that returns the state to the correct logical equivalence class. Minimum-weight perfect matching (MWPM) is a strong standard decoder, while recent neural decoders show that learned models can exploit spatial, temporal, and device-specific error correlations.

This project will test one narrow question: does preserving a neural model's uncertainty about physical errors improve surface-code decoding? A spatiotemporal neural network will map repeated syndrome measurements to per-qubit probabilities for the $X$ and $Z$ components of Pauli errors. A second, syndrome-consistent correction stage will receive those probabilities rather than a binary error map. We will compare this soft-information pipeline with MWPM, a direct neural decoder, and an otherwise matched model whose probabilities are thresholded before correction. The primary outcome will be logical error rate, not exact physical-error classification accuracy.

Intellectual Merit: The project will isolate the value of uncertainty through controlled ablation rather than evaluating only whether a neural decoder can correct errors. It will distinguish improvements caused by retaining probability information from improvements caused by model size, a learned syndrome representation, or a strong conventional decoding stage. The study will also compare physical-error inference metrics with the logically meaningful outcome, testing whether better per-qubit predictions actually produce fewer logical failures.

Broader Impact: Reliable decoding is necessary for useful fault-tolerant quantum computation, including future quantum-machine-learning workloads. A reproducible benchmark showing when soft neural information helps, has no effect, or hurts would help researchers avoid judging decoders by misleading physical-error accuracy alone. The data pipeline, ablation protocol, and logical-error analysis could also be reused by future QLab projects studying other codes, noise models, or quantum devices.

Superconductors show promising applications in future electric grids in that they are capable of conducting electricity with zero resistance. Thus, they have the potential to massively increase the efficiency of current flow, which is applicable to commercial power grids and EV chargers. However, currently discovered superconductors exhibit superconducting properties only at low temperatures or extremely high pressures, with neither environment being practical enough to reproduce on a global scale. Furthermore, ceramic superconductors suffer from brittleness and poor heat dissipation, leading to breakdown caused by thermal stress. Therefore, more research is needed to find ways to address these shortcomings before superconductors can see widespread use. Yttrium-barium-copper-oxide (YBCO) shows desirable superconducting properties such as strong flux pinning, high critical current density, and a high critical temperature that allows cooling using liquid nitrogen. However, its natural brittleness can produce structural defects that weaken such properties. Therefore, this project will attempt to introduce calcium into the YBCO crystal lattice that will enhance its structural integrity while maintaining its superconducting properties.

Intellectual Merit The project will further explore how external materials can interact with YBCO crystals in ways that support superconducting. It can suggest standout materials for this purpose for further experimentation.

Broader Impact Research in materials science will contribute to overall knowledge needed to eventually create a room-temperature superconductor that can be used practically across different applications, from the power grid to computing. This would also reduce the need for cooling in circuitry.

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.

Introduction

A quantum network can be modeled as a graph, with vertices representing sites that can hold an excitation and edges representing couplings between those sites. An excitation may be spread across several sites in a superposition. As the state evolves, probability amplitudes combine through interference, changing where the excitation is likely to be found [1]. Adding a connection can therefore help or hurt transfer. Since each coupling also takes work to implement and calibrate, we have a reason to examine networks with relatively few edges.

We will investigate how connectivity affects the probability of transferring an excitation between two sites within a fixed time. Starting with minimally connected networks, we will compare the best transfer available when one or two additional edges are allowed. We will also measure what happens when a single edge is added to a particular network while the source–target distance stays the same. We will examine every small network in our stated range, then use a heuristic search to explore larger networks.

Intellectual Merit

Previous research has studied how to achieve reliable state transfer with limited network resources [2, 3]. Our project will compare transfer under four constraints, namely the number of sites, the number of extra edges, the source–target distance, and the available time. This comparison will help distinguish the benefit of generally higher edge density from the effect of placing an edge in a particular location. Our results will factor in improvements, decreases, and negligible differences in transfer across graphs.

Broader Impact

Our project aims to produce a database of small networks, with their transfer probabilities and the code used to calculate them. Future research groups could use these results to choose networks for further study or check their own simulations. Documenting the analysis and its limits will make our work easier to reproduce and build off of.

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