Park Daniel K, Petruccione Francesco, Rhee June-Koo Kevin
School of Electrical Engineering, KAIST, Daejeon, 34141, Republic of Korea.
ITRC of Quantum Computing for AI, KAIST, Daejeon, 34141, Republic of Korea.
Sci Rep. 2019 Mar 8;9(1):3949. doi: 10.1038/s41598-019-40439-3.
A prerequisite for many quantum information processing tasks to truly surpass classical approaches is an efficient procedure to encode classical data in quantum superposition states. In this work, we present a circuit-based flip-flop quantum random access memory to construct a quantum database of classical information in a systematic and flexible way. For registering or updating classical data consisting of M entries, each represented by n bits, the method requires O(n) qubits and O(Mn) steps. With post-selection at an additional cost, our method can also store continuous data as probability amplitudes. As an example, we present a procedure to convert classical training data for a quantum supervised learning algorithm to a quantum state. Further improvements can be achieved by reducing the number of state preparation queries with the introduction of quantum forking.
许多量子信息处理任务要真正超越经典方法的一个先决条件是要有一个将经典数据编码到量子叠加态的有效程序。在这项工作中，我们提出了一种基于电路的触发器量子随机存取存储器，以系统且灵活的方式构建经典信息的量子数据库。对于注册或更新由M个条目组成的经典数据，每个条目由n位表示，该方法需要O(n)个量子比特和O(Mn)步。通过额外代价的后选择，我们的方法还可以将连续数据存储为概率幅。作为一个例子，我们给出了一个将量子监督学习算法的经典训练数据转换为量子态的程序。通过引入量子分叉减少态制备查询的数量可以实现进一步的改进。
