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arXiv 1806.05960quant-phcond-mat.stat-mechcs.IThep-thmath.IT
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基于经典比特的量子计算

Quantum computing with classical bits

C. Wetterich

摘要:

比特-量子映射将伊辛自旋或经典比特的概率信息与量子自旋或量子比特相关联。量子系统是经典统计系统的子系统。伊辛自旋可以代表宏观两能级观测量,量子子系统采用适当的期望值和关联。我们讨论了基于伊辛自旋的静态存储材料,其边界信息可以在广义平衡态中通过体传输。它们可以作为量子子系统的Hadamard门或hocT门实现量子操作。经典概率系统可以解释量子自旋的纠缠。任意数量的量子自旋的任意幺正演化可以通过无限数量的伊辛自旋的静态存储材料来描述,而伊辛自旋反过来可以对应于连续变量或场。我们讨论了有限数量的伊辛自旋实现的酉操作的离散子集。它们可能对新的计算结构有用。我们认为量子计算或更一般的概率计算的特征可以通过神经网络、神经形态计算甚至大脑来实现。这既不需要小的孤立实体,也不需要非常低的温度。在这些系统中,概率信息的处理可以比静态记忆材料更普遍。我们提出了一个一般的概率计算的形式,其中确定性计算和量子计算的特殊限制情况。

英文摘要:

A bit-quantum map relates probabilistic information for Ising spins or classical bits to quantum spins or qubits. Quantum systems are subsystems of classical statistical systems. The Ising spins can represent macroscopic two-level observables, and the quantum subsystem employs suitable expectation values and correlations. We discuss static memory materials based on Ising spins for which boundary information can be transported through the bulk in a generalized equilibrium state. They can realize quantum operations as the Hadamard or CNOT-gate for the quantum subsystem. Classical probabilistic systems can account for the entanglement of quantum spins. An arbitrary unitary evolution for an arbitrary number of quantum spins can be described by static memory materials for an infinite number of Ising spins which may, in turn, correspond to continuous variables or fields. We discuss discrete subsets of unitary operations realized by a finite number of Ising spins. They may be useful for new computational structures. We suggest that features of quantum computation or more general probabilistic computation may be realized by neural networks, neuromorphic computing or perhaps even the brain. This does neither require small isolated entities nor very low temperature. In these systems the processing of probabilistic information can be more general than for static memory materials. We propose a general formalism for probabilistic computing for which deterministic computing and quantum computing are special limiting cases.

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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