Recently Updated Projects

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.

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.

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 (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.

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.

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.

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.