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Hardware-centric Quantum Algorithm Design
Murphy Niu
IRB-4105 Virtual-https://umd.zoom.us/j/98095131895?pwd=bFRySUJZSytQcjFVVis0dFpuWU1TZz09
Friday, April 1, 2022, 11:00 am-12:00 pm Calendar
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Over the last half-century, theoreticians have outlined a quantum path to provably secure communication, unprecedented sensor sensitivities, and a quantum advantage to solving problems that are classically hard. However, cutting-edge research in quantum algorithms is often detached from exciting progress in quantum hardware engineering. This has created a gap between algorithmic requirements and hardware capabilities. To close this gap, a deep understanding of quantum hardware physics should be integrated into our development of quantum algorithms–what we call hardware-centric algorithm designs. I will review some examples of hardware-centric quantum algorithm design for quantum control, metrology, and error correction. In each case, the physical details of quantum hardware can be harnessed in the algorithm design to achieve what is otherwise infeasible:

  • Demonstrating and verifying quantum advantages in random circuits,

  • Solving quantum chemistry problems,

  • Training quantum neural networks for classification with experimental noisy intermediate-scale quantum computers. 

Lastly, I will outline the philosophy and software infrastructure to assist the co-development of quantum hardware and quantum applications to move the field forward.


Murphy Niu is a researcher with the Google Quantum AI team.  She received her Ph.D. from MIT under the joint supervision of Jeffrey Shapiro and Isaac Chuang and then served as a postdoc at UC Berkeley.  Her research focuses on modeling, algorithm, and system designs of superconducting qubit systems towards realizing scalable and fault-tolerant quantum computation.  She is an author on a number of Science and Nature papers, including the Google quantum supremacy paper demonstrating that quantum computers had surpassed the power of classical computers for a specific computational task.

This talk is organized by Richa Mathur