量子模式识别
Quantum Pattern Recognition
摘要：
我回顾并扩展了我最近提出的量子联想记忆模型。在该模型中，n比特的二进制模式存储在n个量子比特的计算基的适当子集的量子叠加中。信息可以通过在该子集内执行存储器量子态的依赖于输入的旋转并测量所得到的状态来检索。这种旋转的记忆状态的振幅达到峰值的汉明距离最接近的输入，导致在一个很高的概率测量的记忆模式非常相似，它。模式召回的准确性可以通过调整一个参数的作用，有效的温度进行调整。该模型解决了经典联想记忆中众所周知的容量不足问题，提供了指数级的容量改进。付出的代价是信息检索的概率性质，然而，这个模型与我们自己的大脑共享一个特征。
英文摘要：
I review and expand the model of quantum associative memory that I have recently proposed. In this model binary patterns of n bits are stored in the quantum superposition of the appropriate subset of the computational basis of n qbits. Information can be retrieved by performing an input-dependent rotation of the memory quantum state within this subset and measuring the resulting state. The amplitudes of this rotated memory state are peaked on those stored patterns which are closest in Hamming distance to the input, resulting in a high probability of measuring a memory pattern very similar to it. The accuracy of pattern recall can be tuned by adjusting a parameter playing the role of an effective temperature. This model solves the well-known capacity shortage problem of classical associative memories, providing an exponential improvement in capacity. The price to pay is the probabilistic nature of information retrieval, a feature that, however, this model shares with our own brain.
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。
