Development of ab initio cavity quantum electrodynamics approaches for describing hybrid light-matter states known as polaritons; development of reduced density matrix methods for modeling strong electron and as tools for error mitigation in NISQ applications.

Quantum materials exhibit unusual electronic and magnetic properties that arise from quantum effects. The UNF Materials Theory group focuses on understanding the behavior of quantum materials with the goal of improving the capabilities and efficiency of quantum technologies such as quantum computing and quantum sensing.

Our research investigates how quantum spintronics can be harnessed to build novel computing and memory platforms. By leveraging the robustness of topological states at the nanoscale, we aim to design devices that are more stable, energy-efficient, and scalable than conventional technologies. This approach bridges fundamental discoveries in quantum devices with practical advances in computing, sensing, and information processing.

USF quantum research targets at innovative technologies to facilitate distributed quantum computing of NISQ computers, equivalent to a large number of available logical qubits. These technologies include, but not limited to, quantum processor design, quantum communications and networking between quantum computers, error mitigation, programming language, and parallel/distributed quantum algorithms.

The quantum materials patterned on the nanoscale enable efficient extraction and manipulation of structured single quanta of light — photons — and they offer an unprecedented degree of control vital for future quantum technologies. Quantum Photonics Materials group at CREOL is working on designing, fabrication, and testing of such devices for generation of, and encoding quantum information with, optical modes “sculptured” by nano-patterns. Our group has demonstrated several “firsts” in this area, from conceptualizing quantum nanomaterials that enable structured topological modes to pioneering demonstrations of novel states of light and quantum matter, and extraction of photons into such modes, which lays a foundation for a novel platform for programmable quantum photonics.

My group develops quantum sensors that use carefully controlled ultracold molecules and light inside high-quality optical cavities to detect tiny signals with extraordinary precision. By harnessing quantum entanglement, correlations that do not exist in everyday objects, we reduce measurement noise and improve sensitivity beyond classical limits. These tools enable us to probe fundamental physics, including searches for new particles and forces. Beyond quantum sensing and metrology, we apply our platform to quantum information science, computing, and simulation.

Quantum computing enable applications that were previously not possible with conventional computers. Examples of research topics include how quantum processors can model complex many‑body systems and reveal the behavior of phase transitions and critical phenomena beyond classical limits. Other research focuses on how quantum methods can be used with machine learning and AI to accelerate discovery, optimization, and data analysis across scientific and engineering tasks. Quantum key distribution (QKD) techniques are being to developed to enable secure communication in future networks and devices.

Quantum computing has a history of being accessible only in graduate school.  As quantum computing becomes a reality, it will be necessary to create a quantum computing literate workforce that does not require a masters or doctoral degree.  My research focuses on bringing quantum computing to the masses so that undergraduate students are prepared for this inevitable future, and teachers and graduates with bachelor’s degrees can update their knowledge through certifications.

Research goal is to develop a rigorous, Clifford-algebra–based framework for self-reconfigurable nonlinear control with sliding mode for quantum systems. We design controllers that adapt their structure in real time, respect the geometry and physics of the quantum plant (unitarity, positivity, conserved quantities), and integrate AI learning modules while improving stability and robustness.  As a practical application, we target drift-robust, closed-loop calibration and control of a multi-qubit semiconductor quantum-dot platform, sustaining high-fidelity spin operations amid charge noise and hyperfine-induced dephasing.

Experimental and numerical study of quantum fluid dynamics in superfluid systems, accelerator cryogenic, WIMP dark-matter detection using superfluid target material, liquid-hydrogen based aviation, and qubit systems made by single electrons on liquid helium or solid neon surfaces.

Indistinguishable photons, individual quanta of light that share the same quantum states, encode quantum information for technologies used to develop the quantum internet. In our lab, we design and use sensitive microscopes to study the production of indistinguishable photons from single light-emitting particles on the timescale of 1-quadrillionth of a second. We also study the interactions of the light-emitting particles with tiny antennas to control the speed of the photon production for efficient and tunable indistinguishability.

Alex Krasnok’s group advances quantum photonics and sensing to detect early, hidden damage in civil infrastructure before it becomes dangerous. They develop diamond NV-center quantum magnetometry and photonic/microwave resonators to non-invasively reveal corrosion under coatings, section loss in steel reinforcement, and hairline cracks beneath thin concrete cover. The team also pioneers complex-frequency waveform control to improve the efficiency and selectivity of quantum devices, alongside near-sensor photonics designed to operate in harsh environments. The goal is reliable, field-ready quantum measurements that translate into safer bridges and buildings.

Quantum optics, optically active spin qubits, quantum memories and single-photon sources, quantum networks and quantum computing, integration of quantum emitters with nanophotonic devices, new material platforms for quantum emitters and qubits.

The Quantum Software Group in UCF’s ECE Department is developing innovative software tools to unlock the full potential of quantum computing and bring it closer to real-world applications. Our group focuses on optimizing quantum circuits for better performance and creating techniques to reduce the impact of noise in quantum hardware, both of which significantly improve the accuracy of quantum algorithms. We also design benchmarking protocols to track progress toward quantum advantage, identifying how far we are from achieving it and where improvements are most needed. Together, these efforts help accelerate the path toward practical, impactful quantum computing.

The Laboratory for Hybrid Quantum Systems at UCF Physics leverages on the coherent interconversion between fundamental quantum information carriers in optics, microwaves and acoustics at millikelvin temperatures to enable critical quantum networking technologies. By interfacing different species of quantum hardware platforms with modern ubiquitous and exceptionally low-loss optical fiber infrastructures, we build the key components towards heterogeneous quantum network architectures encompassing superconducting circuits, photonic qubits, solid-state spins and isolated ions and atoms. Our technologies allow synergistic integration of unique strength in each individual hardware component for next-generation quantum information systems.

Jing Guo’s research focuses on modeling, simulation, and design to advance the robustness and performance of quantum computing and sensing hardware. His expertise spans multiple areas, including semiconductor-based quantum computing, where he investigates qubit and quantum circuit design through advanced modeling and simulation. He also explores the application of quantum computing algorithms to semiconductor technologies as a representative example of how his work connects theory with hardware implementation.

Dr. Bhimani’s research agenda targets the practical adoption of variational quantum algorithms (VQAs) and quantum neural networks (QNNs) by attacking three interlocking barriers in NISQ-era quantum computing: (1) inefficient and unstable training, (2) brittle theoretical assumptions about optimization landscapes, and (3) fragility to hardware noise and platform heterogeneity. Collectively, we develop algorithmic, theoretical, and systems-level solutions that improve QNN trainability, provide principled recipes for optimizer design, and make QML workflows robust and reproducible on real quantum hardware.

Dr. Das is an experimental high energy physicist working on the Compact Muon Solenoid experiment (CMS) at the Large Hadron Collider since 2006. He investigates nature at its smallest scales, where quantum rules dominate. His work at CMS contributed to the discovery of the Higgs boson in 2012, and for that he is a co-recipient of the 2025 Breakthrough Prize in Fundamental Physics. He develops quantum-annealing based algorithms for track reconstruction at CMS and other collider experiments. He also teaches a course on quantum computing at the Florida Institute of Technology.

Quantum materials exhibit unusual electronic and magnetic properties that arise from quantum effects. The UNF Materials Theory group focuses on understanding the behavior of quantum materials with the goal of improving the capabilities and efficiency of quantum technologies such as quantum computing and quantum sensing.

We create nanoscale quantum sensors built from atom-like systems embedded in solid materials. By designing nanophotonic structures that control how these sensors emit or absorb light, we can measure extremely small magnetic and electric fields and even produce high-resolution images in complex environments. This opens the door to applications such as imaging through biological tissue, identifying chemical fingerprints at the nanoscale, and diagnosing advanced electronic devices. Our ultimate goal is to move quantum sensing beyond today’s limits and unlock new scientific and technological possibilities.

Investigate ways to make quantum devices work more reliable, even in the presence of dissipation. These devices, built from very small structures such as quantum dots, can be used to store information or to produce special kinds of light. By controlling the quantum state of light, we can improve communication systems and sensing technologies. This work on preparing quantum systems more efficiently also supports advances in quantum information processing.

Improved quantum-level models will be an enableing technology for the development of promising next‑generation materials for electronics, spintronics, quantum optics, and catalysis. Building on advances in quantum algorithm development, the theory and implementation of the variational quantum eigensolver (VQE) is being researched, with the goal of calculating material energies with chemical accuracy. A key target is highly magnetic systems with many unpaired electrons—exemplified by the FeMoco complex—whose precise modeling could influence global energy use and food production. This effort ams to develop practical computational tools to tackle long-standing challenges and accelerate materials discovery.

Dr. Li’s research focuses on neutral atom arrays and atom-like solid state systems as qubit platforms. In particular, Dr Li’s group employs advanced laser spectroscopic techniques to study these qubit platforms as many-body systems of interacting single quantum entities. Understanding their many-body properties is critical for realizing scalable quantum computing based on these platforms. Dr. Li also explores the potential and challenges of distribution quantum computing.

Many critical scientific and industrial challenges are fundamentally NP-Hard optimization problems, which are computationally hard for even the fastest traditional computers to solve efficiently. The core of this work is to harness the natural energy-minimizing properties inherent in quantum and nanoscale physical systems to perform computation. Instead of using traditional methods, a given optimization problem is physically embodied by mapping it onto a custom spatial layout of Quantum-dot Cellular Automata (QCA) cells. The positions of these elements are precisely synthesized so that their natural physical interactions directly mirror the mathematical structure of the problem. The system is then allowed to relax into its lowest-energy state, which directly corresponds to the problem’s optimal solution. This paradigm offers a powerful alternative to traditional algorithms, demonstrating performance that is orders of magnitude faster for certain problems. This approach has been successfully applied to complex computer vision challenges, such as identifying salient objects in real images, and holds the potential to accelerate discoveries in any field requiring large-scale optimization. Building upon this foundational quantum concept, the approach was experimentally realized to solve QUBO problems using a system of nanomagnets.

In the Atomic-Layered Epitaxial Growth of Oxides (Atomic-LEGO) lab at UNF, we synthesize thin films one atomic layer at a time. This gives us unique capabilities to create and optimize materials with new and useful physical properties. For example, we are developing superconducting Josephson junctions using both insulating and ferromagnetic tunnel barriers that can serve as building blocks for quantum computing. These devices are studied both within our group and through collaborative efforts utilizing the specialized capabilities of the FAQT team.

Dr. Robert Usselman is a biophysical chemist who studies how electron spins play a role in biology. His research uses advanced light-based tools and imaging techniques to explore how these quantum effects influence proteins and living cells. By combining quantum sensing technologies, such as nanodiamonds, with biological systems, Dr. Usselman’s work aims to reveal new insights into how life functions at the most fundamental level.

The Quantum Silicon Photonics group at CREOL is working on integrated topological photonics platforms for the robust generation and manipulation of scalable defect-resilient quantum photonic entanglement. Encoding information in topological degrees of freedom, inherently resilient to errors, is a promising path to scalable quantum computing and ultrasensitive and defect-immune sensors. Our group pioneered the use of topology to protect photonic quantum information and is now working on truly scalable approaches that can enable the complex quantum architectures necessary for modern quantum information.