qBraid hires Dr. Junyu Liu as QML researcher

qBraid hires Quantum Machine Learning Expert

We are excited to announce that Dr. Junyu Liu will be assuming the role of a quantum research scientist at qBraid. Dr. Liu is a world-class scientist in quantum computing and has worked in numerous areas of quantum information science. His work has interdisciplinary applications in optimization, machine learning, data science, big data, quantum computing, blockchains, computer science, and fundamental physics, from formal theoretical predictions, experimental simulations, and observations to commercial realizations.

Dr. Liu received a bachelor’s degree in Physics from the University of Science and Technology of China and his Ph.D. in theoretical physics from Caltech under the supervision of Dr. John Preskil, Clifford Cheung and David Simmons-Duffin. He is a prolific researcher, and has held positions with Walter Burke Insitute for Theoretical Physics and the Institute for Quantum Information and Matter. Currently, he also holds positions with the University of Chicago in Prof. Liang Jiang’s Group in the Pritzker School of Molecular Engineering and with the Chicago Quantum Exchange.

His notable works include the proposal of a quantum data center, an architecture combining Quantum Random Access Memory (QRAM) and quantum networks. In this work Dr. Liu and his collaborators give a precise definition of QDC, and discuss its possible realizations and extensions. This foundational work is expected to define what future quantum data centers may look like and will have applications in quantum computation, quantum communication, and quantum sensing. His work on Quantum Neural Tangent Kernels has greatly contributed to the field of Quantum Machine Learning. Predicting how well a quantum machine learning model will perform for given learning or optimization tasks is unclear. With QNTK, Dr. Liu presented dynamical equations that allow scientists to estimate how well a QML model will perform.

At qBraid, Dr. Liu will work on quantum optimization, machine learning and other quantum chemistry applications. In collaboration with scientists from MIT, he is currently leading project on making smarter power grids using quantum computers. Dr. Liu and his colleagues exploring the use of quantum algorithms for state-of-the-art smart grid problems. They have been running simulations for the project on various quantum computers through qBraid and their early explorations shows a potential exponential quantum speedup using HHL algorithms for sparse matrix inversions in the power flow algorithms. The implementation of HHL algorithms in the near-term is limited by the quantum noise, the difficulty in realizing quantum random access memories (QRAM), and the depth of the required quantum circuits. The team has developed the software on qBraid for resources estimation that calculates the hardware and software requirements for a given instance of the problem in the fault-tolerant quantum computing regime. They are also exploring the use of near-term variational quntum algorithms for this problem. We plan to make their research available to our customers on qBraid soon.

To get early access to the software for the project on qBraid when it becomes available, signup on qBraid.com or reach out to us at contact@qbraid.com

qBraid hires Dr. Junyu Liu as QML researcher

qBraid hires Quantum Machine Learning Expert

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