Neural Networks Enhance Quantum Error Correction
Study: Learning high-accuracy error decoding for quantum processors . The AlphaQubit decoder maintained its edge on simulated data with realistic noise, showcasing its ability to adapt to complex error distributions. Trained on both synthetic and experimental data, it represents a significant step forward in leveraging machine learning (ML) to overcome the limitations of traditional, human-designed algorithms in quantum error correction. Related Work Quantum computing has shown huge potential over recent years to transform various applications, whether that be in material science, machine learning, and optimization. However, these possibilities are dependant on overcoming the intrinsic error rates of physical quantum devices. Error correction, achieved through redundancy using logical qubits, is essential for fault-tolerant quantum computation. The surface code stands out for its high error tolerance, making it a leading approach. Yet, decodin...