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.

翻译：量子设备使用量子比特表示信息，使其能够利用量子物理的重要特性，特别是叠加和纠缠。因此，量子计算机有潜力超越最先进的经典计算机。近年来，量子算法已展现出这一前景的迹象，并有许多针对量子领域提出的算法。在量子计算机上解决困难现实世界问题面临两个关键障碍。首先是硬件方面——最先进量子系统中的量子比特数量太少，无法使大规模问题求解变得实用。其次是算法本身——由于量子计算机使用量子比特，在此类设备上运行的算法与传统计算机上的算法根本不同。受这些约束，研究重点一直集中在开发小规模问题求解方法作为概念验证，同时认识到一旦拥有足够量子比特的量子设备问世，这些方法便有望扩展。本文目标与此类似。我们提出了一种量子方法来解决数据共享背景下已得到充分研究的问题。该启发式方法使用了著名的量子近似优化算法（QAOA）。我们展示基于小规模数据集的实验结果，以说明如何用量子算法解决该问题。结果表明该方法具有潜力，并能提供接近最优的解。同时，我们也意识到该方法仍有改进空间。