论文标题
用于变化计算的空间效率二进制优化
Space-efficient binary optimization for variational computing
论文作者
论文摘要
在嘈杂的中等规模量子（NISQ）计算机时代，对于设计不需要许多Qubits或深层电路的量子算法至关重要。不幸的是，最著名的量子算法过于苛刻，无法在当前可用的量子设备上运行。此外，即使是为NISQ时代开发的最新算法，也常常遭受特定问题类别的高空间复杂性要求。在本文中，我们表明，有可能大大减少旅行推销员问题（TSP）所需的量子数，这是一项范式优化任务，以更深的变化电路为代价。尽管重点是这个特定问题，但我们声称该方法可以推广到标准位编码高效效率低下的其他问题。最后，我们还提出了编码方案，该方案在Qubit效率和电路深度效率模型之间平稳插值。所有提出的编码在量子近似优化算法框架中的实现仍然有效。
In the era of Noisy Intermediate-Scale Quantum (NISQ) computers it is crucial to design quantum algorithms which do not require many qubits or deep circuits. Unfortunately, the most well-known quantum algorithms are too demanding to be run on currently available quantum devices. Moreover, even the state-of-the-art algorithms developed for the NISQ era often suffer from high space complexity requirements for particular problem classes. In this paper, we show that it is possible to greatly reduce the number of qubits needed for the Traveling Salesman Problem (TSP), a paradigmatic optimization task, at the cost of having deeper variational circuits. While the focus is on this particular problem, we claim that the approach can be generalized for other problems where the standard bit-encoding is highly inefficient. Finally, we also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models. All the proposed encodings remain efficient to implement within the Quantum Approximate Optimization Algorithm framework.
