Quantum Neural Network Software Testing, Analysis, and Code Optimization for Advanced IoT Systems: Design, Implementation, and Visualization

TL;DR

The paper addresses training instability in quantum neural networks for IoT systems due to barren plateaus. It introduces TACO, a runtime testing, analysis, and code optimization tool that tracks gradient variance in VQC gates and visualizes it via TensorBoard to guide design. Key contributions include the world-first integration of dynamic run-time testing with HCI-based visualization for QNNs in IoT, and a feedback loop to modify QNN structure. This approach lowers the barrier for software engineers lacking quantum training and supports practical deployment of QNN-based IoT applications.

Abstract

This paper introduces a novel run-time testing, analysis, and code optimization (TACO) method for quantum neural network (QNN) software in advanced Internet-of-Things (IoT) systems, which visually presents the learning performance that is called a barren plateau. The run-time visual presentation of barren plateau situations is helpful for real-time quantum-based advanced IoT software testing because the software engineers can easily be aware of the training performances of QNN. Moreover, this tool is obviously useful for software engineers because it can intuitively guide them in designing and implementing high-accurate QNN-based advanced IoT software even if they are not familiar with quantum mechanics and quantum computing. Lastly, the proposed TACO is also capable of visual feedback because software engineers visually identify the barren plateau situations using tensorboard. In turn, they are also able to modify QNN structures based on the information.

Figures (6)

• Figure 1: Run-Time Software Testing and Analysis in QNN-based IoT Systems.
• Figure 2: Overall Process of TACO, i.e., Dynamic Run-Time Quantum Software Testing, Analysis, and Code Optimization.
• Figure 3: Flowchart of TACO
• Figure 4: The Example Code for VQC Structure.
• Figure 5: Test Accuracy.