A5026770835- Quantum Computing Algorithms and Architecture
- Quantum Information and Cryptography
- Quantum and electron transport phenomena
- Quantum-Dot Cellular Automata
- Computational Drug Discovery Methods
- Neuropeptides and Animal Physiology
- Cold Atom Physics and Bose-Einstein Condensates
- Spectroscopy and Quantum Chemical Studies
- Analytical Chemistry and Chromatography
- Receptor Mechanisms and Signaling
- Genetics, Bioinformatics, and Biomedical Research
Yale University
2024-2025
Lafayette College
2023-2025
Bosonic quantum devices offer a novel approach to realize computations, where the two-level system (qubit) is replaced with (an)harmonic oscillator (qumode) as fundamental building block of simulator. The simulation chemical structure and dynamics can then be achieved by representing or mapping Hamiltonians in terms bosonic operators. In this perspective, we review recent progress future potential using for addressing wide range challenging problems, including calculation molecular vibronic...
Hybrid quantum-classical computing algorithms offer significant potential for accelerating the calculation of electronic structure strongly correlated molecules. In this work, we present first quantum simulation conical intersections (CIs) in a biomolecule, cytosine, using superconducting computer. We apply contracted eigensolver (CQE)─with comparisons to conventional variational deflation (VQD)─to compute near-degenerate ground and excited states associated with intersection, key feature...
Quantum harmonic oscillators, or qumodes, provide a promising and versatile framework for quantum computing. Unlike qubits, which are limited to two discrete levels, qumodes have an infinite-dimensional Hilbert space, making them well-suited wide range of simulations. In this work, we focus on the molecular electronic structure problem. We propose approach map Hamiltonian into qumode bosonic problem that can be solved devices using variational eigensolver (VQE). Our is demonstrated through...
Hybrid quantum-classical computing algorithms offer significant potential for accelerating the calculation of electronic structure strongly correlated molecules. In this work, we present first quantum simulation conical intersections (CIs) in a biomolecule, cytosine, using superconducting computer. We apply Contracted Quantum Eigensolver (CQE) -- with comparisons to conventional Variational Deflation (VQD) compute near-degenerate ground and excited states associated intersection, key feature...
The nexus of quantum computing and machine learning - offers the potential for significant advancements in chemistry. This review specifically explores neural networks on gate-based computers within context drug discovery. We discuss theoretical foundations learning, including data encoding, variational circuits, hybrid quantum-classical approaches. Applications to discovery are highlighted, molecular property prediction generation. provide a balanced perspective, emphasizing both benefits...