QuantumFlow, A Co-Design Framework of Neural Networks and Quantum Circuits Towards Quantum Advantage
(Nature) The power of quantum computers in executing neural network has mostly remained unknown, primarily due to a missing tool that effectively designs a neural network suitable for quantum circuit. A research team recently developed a neural network and quantum circuit co-design framework, namely QuantumFlow, to address the issue.
Researchers have published an extensive article with calculations and references here in Nature but their conclusion demonstrates that potential advantage can be achieved for executing neural networks on quantum computing against a classical one.
In QuantumFlow, they represented data as unitary matrices to exploit quantum power by encoding n = 2k inputs into k qubits and representing data as random variables to seamlessly connect layers without measurement.
Coupled with a novel algorithm, the cost complexity of the unitary matrices-based neural computation can be reduced from O(n) in classical computing to O(polylog(n)) in quantum computing. Results show that on MNIST dataset, QuantumFlow can achieve an accuracy of 94.09% with a cost reduction of 10.85 × against the classical computer. All these results demonstrate the potential for QuantumFlow to achieve the quantum advantage.
Source data can be accessed here.