Researcher from Google Quantum AI Achieves Breakthrough in Leakage Management for Scalable Quantum Error Correction


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In a recent publication in Nature Physics, researchers from Google Quantum AI and other Institutes addressed a critical challenge in quantum computing: the susceptibility of qubits, specifically those in Google’s quantum devices, to errors, particularly bit-flip and phase-flip errors. These errors hinder the creation of a reliable quantum computer. Quantum error correction (QEC) has been a promising approach, but it faces obstacles due to various error mechanisms beyond bit-flip and phase-flip errors.

The paper identifies an additional source of errors arising from higher energy levels, known as leakage states, in transmon qubits, the superconducting qubits forming the basis of Google’s quantum processors. These leakage states can corrupt nearby qubits during quantum operations, particularly during the widely used CZ gate operation, causing operational errors and hindering algorithm execution.

To overcome this challenge, the researchers introduced a novel quantum operation called Data Qubit Leakage Removal (DQLR). DQLR specifically targets leakage states in data qubits and efficiently converts them into computational states. This process involves a two-qubit gate, Leakage iSWAP, inspired by the CZ gate, followed by a rapid reset of the measure qubit to remove errors.

The study demonstrates that DQLR significantly reduces the average leakage state populations across all qubits, from nearly 1% to about 0.1%. Importantly, DQLR prevents a gradual rise in leakage of data qubits that was observed before its implementation.

However, the researchers emphasize that leakage removal alone is not sufficient. They conducted Quantum Error Correction (QEC) experiments with DQLR interleaved at the end of each cycle, ensuring compatibility with the preservation of a logical quantum state. The results showed a notable improvement in the detection probability metric, indicating successful QEC execution. Moreover, DQLR outperformed a method called Measure Leakage Removal (MLR), which, while effective in reducing leakage, also erased the stored quantum state.

In conclusion, DQLR holds promise for large-scale QEC experiments, anticipating improved error mechanisms outside of leakage and increased sensitivity to leakage in larger transmon grids. The researchers believe that understanding and effectively addressing leakage and its associated errors represent a significant step forward in realizing a surface code QEC protocol on a large grid of transmon qubits. The researchers have identified and addressed a critical challenge in quantum computing by introducing the DQLR operation, which efficiently removes leakage states and improves the stability of QEC processes. The results offer a promising pathway toward achieving a reliable and functional quantum computer.

Check out the Paper. All credit for this research goes to the researchers of this project. Also, don’t forget to join our 32k+ ML SubReddit, 41k+ Facebook Community, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more.

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Pragati Jhunjhunwala is a consulting intern at MarktechPost. She is currently pursuing her B.Tech from the Indian Institute of Technology(IIT), Kharagpur. She is a tech enthusiast and has a keen interest in the scope of software and data science applications. She is always reading about the developments in different field of AI and ML.

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