In the journal Physical Review Research, researchers from CSIRO have discovered that AI can be instrumental in managing and mitigating qubit noise, or quantum errors, which are inherent in quantum physics. This development is a significant advancement that could ultimately allow quantum computers to tackle complex real-world problems.
CSIRO research has found AI neural network syndrome decoder can detect errors and make appropriate corrections in quantum processors. Image Credit: CSIRO
Addressing these errors is generally seen as the primary hurdle in transitioning quantum computers from experimental setups to practical tools. In traditional computing, information is encoded and processed using "bits" that operate on binary principles, with each bit representing either a 0 or a 1. In contrast, quantum computers utilize quantum bits, or "qubits."
Qubits leverage the unique properties of quantum mechanics, enabling them to represent 0, 1, or both simultaneously. This capability is expected to significantly expand computational power, potentially solving problems that are currently beyond the reach of classical computers.
However, the sensitive nature of qubits makes quantum computers prone to generating 'noise' or errors in their outputs. Quantum error correction codes are essential for identifying and correcting these errors.
To this end, CSIRO has developed an AI-driven neural network syndrome decoder that effectively identifies errors and implements the necessary corrections. Dr. Muhammad Usman, leader of CSIRO's Data61 Quantum Systems Team, emphasized that this approach could efficiently handle complex errors arising from actual 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. 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.
Dr. Muhammad Usman, Team Leader, The Commonwealth Scientific and Industrial Research Organisation
Quantum error correction codes are essential for addressing the inherent physical noise in qubits by distributing logical information across multiple physical qubits. This process involves the interpretation of error data through the measurement of stabilizers in a lattice of qubits, known as syndrome measurement. Efficient, rapid, and scalable processing of this computationally intensive step is crucial for the effectiveness of quantum error correction codes.
To enhance this correction process, Dr. Usman developed and trained an artificial neural network syndrome decoder. This decoder's performance was rigorously tested on IBM quantum processors, proving its ability to efficiently process complex errors from real quantum hardware and make precise corrections.
The research indicates that as physical error rates continue to decrease in the next few years, AI could play a pivotal role in error suppression. This would be particularly effective as the code distance increases, potentially leading to complete fault tolerance when the code distance reaches a sufficient size.
Journal Reference:
Hall, B., et al. (2024) Artificial neural network syndrome decoding on IBM quantum processors. Physical Review Research. doi.org/10.1103/physrevresearch.6.l032004.