Quantum machine learning for spectral determination on superconducting architectures
Utilizing quantum machine learning with novel superconducting architectures, this method aims to enhance the simulation of material spectral properties using a variational quantum eigensolver and a highly connected qubit topology.
Overview
This solution leverages quantum machine learning (QML) to simulate the spectral properties of materials through a novel superconducting quantum architecture. By employing a variational quantum eigensolver (VQE), it aims to efficiently find material properties through quantum simulations. The approach enhances the accuracy and speed of simulations by utilizing a superconducting qubit modulator with partially pulsed iSWAP interactions and an all-to-all connected qubit topology. This method holds potential for significant advancements in studying complex materials and achieving faster convergence of solutions.
Technical specifications
Key features:
- Utilizes a variational quantum eigensolver (VQE) for determining material properties.
- Employs a novel superconducting qubit modulator for improved state preparation and evolution.
- Features partially pulsed iSWAP gates for faster calculations.
- Implements an all-to-all connectivity within qubit neighborhoods to enhance data movement and efficiency.
The approach is based on characterizing hardware behavior, including gate pulse durations and error channels, to achieve quantum advantage in convergence.
Technology readiness level
Currently at Technology Readiness Level 3, this solution is in the active research and development phase. Validation efforts include mapping QML algorithms to superconducting architectures and developing decomposition theory, with future plans for scalability and correctness verification through classical quantum simulation.
About Syracuse University
Syracuse University is a comprehensive private research university in upstate New York, pairing fundamental inquiry with applied, industry-facing work. Industry engagement is anchored by a downtown innovation campus with co-located university–industry labs, pilot-scale testbeds, and flexible prototyping spaces. A centralized corporate partnerships office and experiential learning pipelines link R&D teams to faculty and student talent through sponsored research, internships, and capstone collaborations. Research is supported by competitive federal funding from agencies such as the National Science Foundation, National Institutes of Health, the Department of Energy, and the Department of Defense. A dedicated technology transfer office and campus-linked incubators streamline IP, licensing, and startup formation.
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