Project: Quantum Optimization

The objective of this research is to develop quantum optimization algorithms to solve large-scale engineering problems such as materials design and topology optimization.

Quantum walks improve the probability of observing optimum in Grover search Quantum walks improve the probability of observing optimum in Grover search Convergence of Grover search in comparison with traditional heuristics new Quantum Approximate Bayesian Optimization Algorithm (QABOA) evolution of quantum systems in QABOA

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.

Quantum Approximate Bayesian Optimization Algorithm

Near-term quantum computing devices suffer from quantum circuit errors due to decoherence. The computed results are also stochastic in nature. We propose a Quantum Approximate Bayesian Optimization Algorithm (QABOA) to quantify uncertainty and accelerate the global searching process of Quantum Approximate Optimization Algorithm (QAOA).