Quantum Neural Network and Soft Quantum Computing
This work addresses the challenge of building practical quantum computers by offering a more realistic approach for quantum AI, potentially enabling earlier development compared to standard quantum computing.
The authors proposed soft quantum computing as a new paradigm for nonclassical computation using real-world quantum systems with decoherence and dissipation, and introduced a quantum neural network model that mimics realistic neurons and leverages quantum laws, uncovering quantum features like discord and non-commutability.
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.