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arXiv 2204.03097quant-ph
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量子只读存储器电路的优化

Optimization of Quantum Read-Only Memory Circuits

Koustubh Phalak, Mahabubul Alam, Abdullah Ash-Saki, Rasit Onur Topaloglu, Swaroop Ghosh

摘要:

量子计算是一个快速扩展的领域,其应用范围从优化一直到复杂的机器学习任务。量子存储器虽然缺乏实用的量子计算机,但有可能带来量子优势。例如,在量子机器学习应用中,量子存储器可以简化数据加载过程,并可能加速学习任务。量子存储器还可以存储量子比特的中间量子态,其可以被重新用于计算。然而,量子存储器(诸如量子只读存储器(QROM))的深度、门计数和编译时间随着地址线的数量呈指数级缩放,使得它们在超过4位地址的现有技术的噪声中间规模量子(NISQ)计算机中不切实际。在本文中,我们提出了一些技术,如预解码逻辑和量子位复位,以减少QROM电路的深度和门数,以实现更宽的地址范围,如8位。所提出的方法减少了门的数量和深度计数至少2倍相比,在只有36%的量子位开销的天真的实现。观察到电路深度和门数减少高达75倍,编译时间减少85倍,代价是最大2.28倍量子位开销。实验上,与现有的优化方法相比,所提出的预解码电路的保真度也更高(高达73%,相比40.8%),在降低的错误率。

英文摘要:

Quantum computing is a rapidly expanding field with applications ranging from optimization all the way to complex machine learning tasks. Quantum memories, while lacking in practical quantum computers, have the potential to bring quantum advantage. In quantum machine learning applications for example, a quantum memory can simplify the data loading process and potentially accelerate the learning task. Quantum memory can also store intermediate quantum state of qubits that can be reused for computation. However, the depth, gate count and compilation time of quantum memories such as, Quantum Read Only Memory (QROM) scale exponentially with the number of address lines making them impractical in state-of-the-art Noisy Intermediate-Scale Quantum (NISQ) computers beyond 4-bit addresses. In this paper, we propose techniques such as, predecoding logic and qubit reset to reduce the depth and gate count of QROM circuits to target wider address ranges such as, 8-bits. The proposed approach reduces the number of gates and depth count by at least 2X compared to the naive implementation at only 36% qubit overhead. A reduction in circuit depth and gate count as high as 75X and compilation time by 85X at the cost of a maximum of 2.28X qubit overhead is observed. Experimentally, the fidelity with the proposed predecoding circuit compared to existing optimization approach is also higher (as much as 73% compared to 40.8%) under reduced error rates.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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