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 errors and phase. These errors make it difficult to create a reliable quantum computer. Quantum error correction (QEC) has been a promising approach, but faces obstacles due to several error mechanisms beyond bit-reversal and phase errors.
The paper identifies an additional source of errors arising from higher energy levels, known as leak states, in transmon qubits, the superconducting qubits that form the basis of Google’s quantum processors. These leak states can corrupt nearby qubits during quantum operations, particularly during operation of the widely used CZ gate, causing operational errors and hampering algorithm execution.
To overcome this challenge, the researchers introduced a new quantum operation called Data Qubit Leakage Elimination (DQLR). DQLR specifically targets leak 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 fast reset of the measurement qubit to eliminate errors.
The study demonstrates that DQLR significantly reduces the average populations of leak states across all qubits, from nearly 1% to about 0.1%. Importantly, DQLR avoids a gradual increase in data qubit leakage that was observed before its implementation.
However, researchers emphasize that eliminating leaks alone is not enough. They performed quantum error correction (QEC) experiments with entangled DQLR at the end of each cycle, ensuring compatibility with preserving a logical quantum state. The results showed a notable improvement in the detection probability metric, indicating successful QEC execution. Additionally, DQLR outperformed a method called Measurement Leakage Elimination (MLR), which, while effective at reducing leakage, also erased the stored quantum state.
In conclusion, DQLR shows promise for large-scale QEC experiments, anticipating improved error mechanisms outside of leakage and increased sensitivity to leakage in larger transmon networks. The researchers believe that understanding and effectively addressing leakage and its associated errors represents an important step forward in realizing a surface code QEC protocol on a large network of transmon qubits. Researchers have identified and addressed a critical challenge in quantum computing by introducing the DQLR operation, which efficiently eliminates leak states and improves the stability of QEC processes. The results offer a promising path toward achieving a reliable and functional quantum computer.
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Pragati Jhunjhunwala is a Consulting Intern at MarktechPost. She is currently pursuing B.tech from the Indian Institute of technology (IIT), Kharagpur. She is a technology enthusiast and has a keen interest in the scope of data science software and applications. She is always reading about the advancements in different fields of ai and ML.
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