Qubit stabilisation via learning capable materials

Andrei T. Patrascu
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Abstract

I describe the engineered decoherence of a qubit state by means of an environment formed out of a neurally architected material. Such a material is a material that can adjust its inner properties in the same way a neural network is adjusting its weights, subject to a built-in cost function. Such a material is naturally found in biological structures (like a brain) but can in principle be engineered at a microscopic level. If such a material is used as an environment for a Nakajima-Zwanzig equation describing the controlled decoherence of a quantum state, we obtain a modified decoherence that allows for correlated states to exist longer or even to become robust. Such a neural material can also be architected to implement certain quantum gate operations on the encapsulated qubit.
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通过学习能力材料实现质子稳定
我描述了通过一种由神经构造材料形成的环境,对量子比特状态进行工程解相干的过程。这种材料可以像神经网络调整权重一样,根据内置的成本函数调整其内部属性。这种材料自然存在于生物结构(如大脑)中,但原则上也可以在微观层面进行设计。如果把这种材料用作描述量子态受控退相干的中岛-茨万齐格方程的环境,我们就能获得一种改进的退相干,使相关态存在更长时间,甚至变得稳健。这样的神经材料也可以设计成在封装的量子比特上实现某些量子门操作。
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