Project: Quantum Computing Algorithms for Optimization and Simulation
The objective of this research is to develop quantum walk based quantum computing algorithms to solve engineering problems including global optimization and system dynamics simulation.
Quatum Walk Enhanced Global Optimization
Grover’s algorithm for unsorted database search shows a quadratic speedup. It has been applied to solve global optimization problems. However, determing the optimum number of Grover rotations for optimization remains empirical. We combine continuous-time quantum walks with Grover search so that the threshold functional value in Grover’s algorithm can be quickly improved so that the efficiency of search can be improved, especially when the number of Grover rotations is limited by decoherence. Similarly, the Quantum Approximate Optimization Algorithm (QAOA) is restricted by quantum circuit depth. We use continuous-time quantum walks as the mixer in combination with Grover rotation to accelerate the global searching process in our new Quantum Approximate Bayesian Optimization Algorithm (QABOA).
Quantum Walk for Stochastic Dynamics Simulation
We introduced continuous-time quantum walks into stochastic dynamics simulation so that the stochastic resonance simulation can be accelerated by up to 100 times, compared to traditional path integral approach.