论文标题
量子算法，用于高阶无约束二进制优化和MIMO最大似然检测
Quantum Algorithm for Higher-Order Unconstrained Binary Optimization and MIMO Maximum Likelihood Detection
论文作者
论文摘要
在本文中，我们提出了一种量子算法，该算法支持一个实现的高阶不约束二进制优化（HUBO）问题。该算法基于Grover自适应搜索，该搜索最初以整数系数支持Hubo。接下来，作为一个应用程序示例，我们将多输入多输出最大似然检测作为带有实价系数的人为问题，在该问题中，我们使用5G标准中指定的灰色编码的位至符号映射。提出的方法使我们能够为检测问题构建有效的量子电路，并分析特定数量的量子和量子门，而其他常规研究则认为这样的电路是可行的，可以作为量子甲骨文。为了进一步加速量子算法，我们还得出了目标函数值的概率分布，并确定了唯一的阈值以样本更好的状态。假设未来的耐断层量子计算，我们提出的算法有可能显着降低经典域中的查询复杂性并在量子域中提供二次加速。
In this paper, we propose a quantum algorithm that supports a real-valued higher-order unconstrained binary optimization (HUBO) problem. This algorithm is based on the Grover adaptive search that originally supported HUBO with integer coefficients. Next, as an application example, we formulate multiple-input multiple-output maximum likelihood detection as a HUBO problem with real-valued coefficients, where we use the Gray-coded bit-to-symbol mapping specified in the 5G standard. The proposed approach allows us to construct an efficient quantum circuit for the detection problem and to analyze specific numbers of required qubits and quantum gates, whereas other conventional studies have assumed that such a circuit is feasible as a quantum oracle. To further accelerate the quantum algorithm, we also derive a probability distribution of the objective function value and determine a unique threshold to sample better states. Assuming a future fault-tolerant quantum computing, our proposed algorithm has the potential for significantly reducing query complexity in the classical domain and providing a quadratic speedup in the quantum domain.
