As quantum hardware continues to evolve, resource efficiency becomes a key bottleneck in realizing practical quantum applications. In this talk, Dr.Niu will present two complementary approaches that address this challenge from distinct angles. First, she will introduce AC/DC, an automated framework for compiling dynamic quantum circuits that leverage mid-circuit measurement and feed-forward operations. AC/DC enables efficient construction of dynamic circuits for arbitrary quantum states and unitaries with reduced circuit depth and gate overhead, advancing the utility of emerging hardware features. Next, she will discuss a practical framework for observable estimation in quantum simulation tasks. This includes Pauli term grouping based on k-commutativity, Clifford-based measurement circuit construction, and adaptive shot distribution techniques. Together, these algorithmic optimizations significantly reduce measurement overhead while improving estimation accuracy on real quantum devices. By integrating compilation and benchmarking strategies, this work contributes toward scalable ,hardware-adapted quantum computation, and paves the way for demonstrating near-term quantum advantages in simulation and optimization.
Bio: Siyuan Niu is currently an assistant professor in the department of Electrical and Computer Engineering at the University of Central Florida. Previously, she was a postdoctoral researcher at Lawrence Berkeley National Laboratory and she received her Ph.D in microelectronics department from the University of Montpellier. Her research interests focus on quantum circuit compilation, quantum error mitigation/correction and benchmarking quantum systems.
Moderators: Dr. Pawel Gora, CEO of Quantum AI Foundation and Dr. Sebastian Zajac, member of QPoland
