| dc.description.abstract | Quantum computing has become an emerging field of interest due to its
computational efficiency in solving combinatorial optimization problems. This
rapid-growing paradigm of computation harnesses the principles of quantum mechanics
such as superposition and entanglement, to converge towards an optimum solution faster
than classical computation. In our advanced developing era, we are facing challenges of
storing and processing exponentially growing data. Quantum computation has proven its
value in rectifying such issues by its ability of storing large sizes of data and processing
them in a parallel manner. One of the widely used quantum optimization algorithms is the
Quantum Approximate Optimization Algorithm (QAOA), a variational hybrid
quantum-classical algorithm. In this paper, we use an active learning-based Bayesian
optimization approach to identify appropriate quantum circuit parameters. A variety of
industrial processes are modeled using combinatorial optimization with the ultimate
objective of either the improving overall efficiency or minimizing the total cost. However,
these problems are known to be computationally intractable. In the realm of combinatorial
optimization, a single-machine scheduling problem with multiple jobs and operations is
notoriously a hard problem. In this paper, we discuss the application of QAOA and active
learning approach to solve a single-machine scheduling problem, and compare the solution
obtained using classical analysis. Another one of the common problems is solving linear
systems of equations, in which there are already many existing classical algorithms that
can be implemented. A linear system of equations is a vital concept in the realm of science
and engineering. As the field of data analytics grow tremendously, the sizes of datasets can
be computationally expensive and time consuming. Hence, quantum linear regression
algorithms such as the HHL algorithm can supply exponential speedups to solve these
questions. To prove the flexibility and breadth of such quantum applications, this paper
will also discuss how a quantum linear regression algorithm can be used to solve production
scheduling problems. | |
