New research from Australia's national science agency, CSIRO, has shown that AI could be used to resolve quantum computing errors. The research is the first of its kind demonstrating the ability of AI to reduce qubit noise.
The research, published in Physical Review Research, includes implementing an AI neural network syndrome decoder to implement appropriate corrections to complex errors from quantum hardware.
"Our work for the first time establishes that a machine learning-based decoder can, in principle, process error information obtained directly from measurements on IBM devices and suggest suitable corrections despite the very complex nature of noise," said Dr. Muhammad Usman, CSIRO's Data61 Quantum Systems Team Leader. "In our work, we do not observe error suppression when the error correction code distance is increased, as theoretically anticipated, due to currently large noise levels (above code threshold) in IBM quantum processors."
Quantum error correction codes have previously been developed to interpret error information by measuring stabilizers within a lattice of qubits to combat the physical noise of qubits. The neural network syndrome decoder developed will assist in maximizing the performance of quantum error correction codes.
During analysis, the team benchmarked the performance of the decoder on IBM quantum processors, demonstrating its efficacy in processing complex errors in quantum hardware. The research provides a pathway to enable error suppression with an increasing code distance over the next few years. Additionally, full fault tolerance will be possible as the code distance becomes larger.