A new paradigm of quantum computing, namely, soft quantum computing, is
proposed for nonclassical computation using real world quantum systems with
naturally occurring environment-induced decoherence and dissipation. As a
specific example of soft quantum computing, we suggest a quantum neural
network, where the neurons connect pairwise via the "controlled Kraus
operations", hoping to pave an easier and more realistic way to quantum
artificial intelligence and even to better understanding certain functioning of
the human brain. Our quantum neuron model mimics as much as possible the
realistic neurons and meanwhile, uses quantum laws for processing information.
The quantum features of the noisy neural network are uncovered by the presence
of quantum discord and by non-commutability of quantum operations. We believe
that our model puts quantum computing into a wider context and inspires the
hope to build a soft quantum computer much earlier than the standard one.

提出了一种新的量子计算方法，称为软量子计算，利用真实的量子系统和自然环境引起的相干和耗散来开展非经典计算。作为软量子计算的一个具体示例，我们建议使用 “受控 Kraus 操作” 通过成对连接神经元，希望为量子人工智能甚至更好地理解人脑的某些功能开辟一条更容易和更现实的道路。我们的量子神经元模型尽可能地模仿现实神经元，并同时使用量子定律来处理信息。噪声神经网络的量子特性是通过量子不和谐性和非压缩性来发现的。我们相信，我们的模型将量子计算置于更广泛的背景下，并激发了早期发展软量子计算机的希望。