Google Deepmind recently released a paper titled ‘Learning high-accuracy error decoding for quantum processors’, introducing their new AI-based system, AlphaQubit. This neural network decoder is designed to accurately identify errors in quantum computers, specifically the surface code, and establish state-of-the-art error suppression. The Google Deepmind team announced the technology on LinkedIn, stating that it aims to make quantum computers more reliable. AlphaQubit was trained on data from 49 qubits within the Sycamore quantum processor, a core component of quantum computing, and achieved groundbreaking results while testing on new Sycamore data.
This initiative is a significant step towards addressing the inherent errors in quantum systems and advancing the reliability and scalability of quantum computers. AlphaQubit is part of Google DeepMind’s plan to combine AI with quantum computing, building on their previous breakthroughs in AI such as predicting protein structures and creating advanced game-playing systems. By applying their expertise to the challenge of fixing errors in quantum computing, Google DeepMind is making progress towards making quantum computing practical.
While AlphaQubit has shown promising results, there is still work to be done. The system needs to handle more complex quantum problems and seamlessly integrate with existing quantum hardware. To accelerate progress, Google DeepMind will collaborate with universities and industry partners to refine AlphaQubit and explore its use on different quantum computing platforms. This will bring us closer to solving problems that traditional computers cannot handle.
In testing, AlphaQubit was shown to reduce errors by 6% compared to tensor network methods, which are accurate but computationally slow, and by 30% compared to correlated matching, a faster but less precise approach. This demonstrates the system’s ability to improve error detection and correction in quantum computing. Quantum computers use qubits, which can exist in multiple states at once, giving them incredible computing power. However, this also makes them susceptible to errors caused by environmental factors and hardware imperfections. AlphaQubit uses machine learning to study large amounts of data on quantum errors, find patterns, and create strategies to fix them more effectively, making quantum computing more reliable and practical.
The researchers claim that AlphaQubit maintained superior accuracy when scaled up, making it a new standard for decoding accuracy in quantum computing. Typically, error correction in quantum computing requires many physical qubits to protect a single logical qubit, making systems bulky and complex. AlphaQubit aims to simplify this by reducing the number of physical qubits needed for error correction. This is a significant step towards making quantum computing more accessible and practical for real-world applications.
