Lightning Talk Presenter

Junpeng Zhan

Junpeng Zhan

Alfred University

About


Dr. Junpeng Zhan is an Assistant Professor in Renewable Energy Engineering at Alfred University starting from July 2020. He received his Bachelor’s and Ph.D. degrees in Electrical Engineering from Zhejiang University, China, in 2009 and 2014, respectively. From 2012 to 2020, he held multiple research positions at the University of Liverpool, Liverpool, U.K., the Hong Kong Polytechnic University, Hong Kong, China, the University of Saskatchewan, Saskatoon, Canada, and the Brookhaven National Laboratory, New York, U.S.A., respectively. He is the PI of an NSF-funded project working on quantum computing for power systems and a co-PI of a project funded by NYSERDA and an ARL-funded multi-million-dollar project working on machine learning for 3D printing. He has worked on multiple federally funded projects at the Brookhaven National Laboratory, USA and Canada. He has published 35 papers in various journals, conferences, and preprints, including 14 in the prestigious IEEE Transactions series journals. He is an Associate Editor of the journal IET Generation, Transmission & Distribution. His research interests include quantum computing, smart grid technologies, machine learning, and optimization methods.

Variational Quantum Search and its Applications

With powerful quantum computers already built, we need more efficient quantum algorithms to achieve quantum supremacy over classical computers in the noisy intermediate-scale quantum (NISQ) era. Grover’s search algorithm and its generalization, quantum amplitude amplification, provide quadratic speedup in solving many important problems. However, they still have exponential time complexity as the depths of their quantum circuits increase exponentially with the number of qubits. Can we reduce the circuit depth of Grover’s algorithm from exponential to polynomial? To answer this question, we propose a new algorithm, Variational Quantum Search (VQS), which is based on the celebrated variational quantum algorithms and includes a parameterized quantum circuit, known as Ansatz. We show that a depth-10 Ansatz can amplify the total probability of k (k≥1) good elements, out of 2n elements, from k/2n to nearly 1, as verified for n up to 26, and that the maximum depth of quantum circuits in the VQS increases linearly with the number of qubits. We demonstrate that a depth-56 circuit in VQS can replace a depth-270,989 circuit in Grover’s algorithm. Thus, the VQS has shown an exponential advantage in terms of circuit depth, up to 26 qubits, over Grover’s algorithm.

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