Presentation
QuBound: An Efficient Workflow Enabling Prediction of Performance Bounds under Unpredictable Quantum Noise
DescriptionQuantum computing has rapidly grown to have actual devices with hundreds of qubits, showing its promise to achieve the quantum advantage over classical computing; however, the unpredictable and unstable noise in quantum devices set barriers to practically unleashing the power of quantum computing. Without understanding the impact of noise on the application, one can hardly reproduce the results or reuse the design. Although noisy quantum simulation can provide insights into the performance changes under noise, it faces the scalability issue, which cannot work for large circuits. To address this pressing problem, in this work, we propose the very first data-driven workflow to predict the bounds of performance. It applies the decomposition method to accurately decompose a trace of historical performance under noise to generate a training dataset, which can isolate different noise sources. On top of this, we develop a novel encoder to simultaneously embed circuit and noise information, which will be processed by an LSTM. The trained model can predict performance bounds for a given noise. Experimental results show that our method can efficiently produce practical bounds for various circuits with different scales.
Event Type
Work-in-Progress Poster
TimeTuesday, June 256:00pm - 7:00pm PDT
LocationLevel 2 Lobby
AI
Autonomous Systems
Cloud
Design
EDA
Embedded Systems
IP
Security