  Quantum devices use qubits to represent information, which allows them to
exploit important properties from quantum physics, specifically superposition
and entanglement. As a result, quantum computers have the potential to
outperform the most advanced classical computers. In recent years, quantum
algorithms have shown hints of this promise, and many algorithms have been
proposed for the quantum domain. There are two key hurdles to solving difficult
real-world problems on quantum computers. The first is on the hardware front --
the number of qubits in the most advanced quantum systems is too small to make
the solution of large problems practical. The second involves the algorithms
themselves -- as quantum computers use qubits, the algorithms that work there
are fundamentally different from those that work on traditional computers. As a
result of these constraints, research has focused on developing approaches to
solve small versions of problems as proofs of concept -- recognizing that it
would be possible to scale these up once quantum devices with enough qubits
become available. Our objective in this paper is along the same lines. We
present a quantum approach to solve a well-studied problem in the context of
data sharing. This heuristic uses the well-known Quantum Approximate
Optimization Algorithm (QAOA). We present results on experiments involving
small datasets to illustrate how the problem could be solved using quantum
algorithms. The results show that the method has potential and provide answers
close to optimal. At the same time, we realize there are opportunities for
improving the method further.
