CAREER: Enabling Trustworthy Quantum Optimization through Software and Hardware Co-Design

Quantum computers have the potential to transform critical sectors such as drug discovery, materials engineering, logistics, and energy infrastructure by solving complex decision-making and resource allocation problems, commonly known as optimization problems, that challenge even the most powerful classical supercomputers. Nations worldwide are investing heavily in the development of large-scale quantum systems capable of running increasingly sophisticated programs. However, realizing practical value from quantum computing requires more than advances in hardware; it demands new approaches that coordinate hardware and software so these systems can operate reliably, efficiently, and securely. Rather than viewing useful quantum computing as dependent solely on fully error-corrected, general-purpose quantum computers designed to run a wide range of tasks, this award advances a scalable and complementary path centered on quantum optimization as a unifying and strategically important application area. By establishing design principles that align software and hardware around optimization workloads, the project seeks to accelerate the transition of quantum technologies from experimental demonstrations to practical computing tools while helping shape the architecture of future scalable systems. The integrated education plan will prepare the next generation of quantum engineers through research-based undergraduate courses, partnerships with regional colleges that have historically had limited access to quantum research, and accessible training resources for industry. Through its educational and workforce development pathways, the project will expand who contributes to and benefits from the rapidly growing field of quantum technology. This project redefines the quantum computing stack by aligning its layers around a single high-impact application: optimization workloads. A central innovation elevates the application layer into the system-level design process, enabling architecture-aware restructuring of computational problems at the application level prior to execution. This shift unlocks three capabilities: reducing quantum error-correction overhead by embedding partial resilience into problem instances, improving efficiency on modular and distributed architectures through topology-aligned mappings, and enabling privacy-preserving quantum optimization on shared or untrusted platforms. Because these transformations operate at the problem level, the resulting application-layer techniques remain adaptable to emerging quantum hardware technologies, computational models, and algorithms as quantum systems evolve. Building on this foundation, the project co-designs the full computing stack, spanning application transformations, compilation, error correction, and hardware, to enable reliable and scalable quantum optimization. At the device level, the project develops application-aware techniques that adapt hardware behavior to problem structure, reducing communication overhead, improving fidelity, and shortening execution time. Together, these efforts establish foundational system design principles for trustworthy and scalable quantum optimization and inform the architecture of next-generation quantum computing systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2544544 | Program: 01003031DB NSF RESEARCH & RELATED ACTIVIT,01002627DB NSF RESEARCH & RELATED ACTIVIT,01002930DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Ramin Ayanzadeh | Institution: University of Colorado at Boulder, Boulder, CO | Award Amount: $436,255 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2544544 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2544544.html