English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Despite the pursuit of quantum advantages in various applications, the powerof quantum computers in neural network computations has mostly remainedunknown, primarily due to a missing link that effectively designs a neuralnetwork model suitable for quantum circuit implementation. In this article, wepresent the co-design framework, namely QuantumFlow, to provide such a missinglink. QuantumFlow consists of novel quantum-friendly neural networks (QF-Nets),a mapping tool (QF-Map) to generate the quantum circuit (QF-Circ) for QF-Nets,and an execution engine (QF-FB). We discover that, in order to make full use ofthe strength of quantum representation, it is best to represent data in aneural network as either random variables or numbers in unitary matrices, suchthat they can be directly operated by the basic quantum logical gates. Based onthese data representations, we propose two quantum friendly neural networks,QF-pNet and QF-hNet in QuantumFlow. QF-pNet using random variables has betterflexibility, and can seamlessly connect two layers without measurement withmore qbits and logical gates than QF-hNet. On the other hand, QF-hNet withunitary matrices can encode 2^k data into k qbits, and a novel algorithm canguarantee the cost complexity to be O(k^2). Compared to the cost of O(2^k)inclassical computing, QF-hNet demonstrates the quantum advantages. Evaluationresults show that QF-pNet and QF-hNet can achieve 97.10% and 98.27% accuracy,respectively. Results further show that for input sizes of neural computationgrow from 16 to 2,048, the cost reduction of QuantumFlow increased from 2.4x to64x. Furthermore, on MNIST dataset, QF-hNet can achieve accuracy of 94.09%,while the cost reduction against the classical computer reaches 10.85x. To thebest of our knowledge, QuantumFlow is the first work to demonstrate thepotential quantum advantage on neural network computation.

A Co-Design Framework of Neural Networks and Quantum Circuits Towards Quantum Advantage