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神经形态量子计算

Neuromorphic quantum computing.

作者信息

Pehle Christian, Wetterich Christof

机构信息

Kirchhoff-Institute for Physics, Heidelberg University, Im Neuenheimer Feld 227, 69120 Heidelberg, Germany.

Institute for Theoretical Physics, Heidelberg University, Philosophenweg 16, 69120 Heidelberg, Germany.

出版信息

Phys Rev E. 2022 Oct;106(4-2):045311. doi: 10.1103/PhysRevE.106.045311.

Abstract

Quantum computation builds on the use of correlations. Correlations could also play a central role for artificial intelligence, neuromorphic computing or "biological computing." As a step toward a systematic exploration of "correlated computing" we demonstrate that neuromorphic computing can perform quantum operations. Spiking neurons in the active or silent states are connected to the two states of Ising spins. A quantum density matrix is constructed from the expectation values and correlations of the Ising spins. We show for a two qubit system that quantum gates can be learned as a change of parameters for neural network dynamics. These changes respect restrictions which ensure the quantum correlations. Our proposal for probabilistic computing goes beyond Markov chains and is not based on transition probabilities. Constraints on classical probability distributions relate changes made in one part of the system to other parts, similar to entangled quantum systems.

摘要

量子计算基于相关性的运用。相关性在人工智能、神经形态计算或“生物计算”中也可能发挥核心作用。作为迈向系统探索“相关计算”的一步,我们证明神经形态计算可以执行量子操作。处于活跃或静默状态的脉冲神经元与伊辛自旋的两种状态相连。量子密度矩阵由伊辛自旋的期望值和相关性构建而成。对于一个双量子比特系统,我们表明量子门可以作为神经网络动力学参数的变化来学习。这些变化遵循确保量子相关性的限制条件。我们提出的概率计算方法超越了马尔可夫链,且不基于转移概率。对经典概率分布的约束将系统一部分的变化与其他部分联系起来,类似于纠缠量子系统。

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