Hardware/Software Co-Design Towards Quantum Advantages

Despite the pursuit of quantum advantages in various applications, 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. In this talk, I will present our open-source neural network and quantum circuit co-design framework, namely QuantumFlow, to address the issue. In QuantumFlow, we represent 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. I will further demonstrate the results on MNIST dataset using IBM quantum processors, which show that QuantumFlow can achieve an accuracy of 94.09% with a cost reduction of 10.85 × against the classical computer. This is the first time that quantum advantage is demonstrated practically on the inference of deep neural networks, and the tool has been accessed over 1,200 times within its first month of release.