Quantum Error Detection Achieves 99.3% Leakage Conversion with Biased-Erasure Cavity Qubit
Researchers are increasingly focused on developing more robust quantum bits for scalable quantum computation. Jiasheng Mai from Southern University of Science and Technology, Qiyu Liu and Xiaowei Deng from the International Quantum Academy, and colleagues demonstrate a significant advance in this field with a hardware-efficient biased-erasure qubit. Their work realises this qubit using a single microwave cavity, exhibiting a substantial erasure bias ratio of over 265 and achieving logical state assignment errors below 1%. Crucially, this research establishes a strong error hierarchy, with postselected error rates exceeding the erasure rate by factors of 31 and 15, and a coherence gain of approximately 6.0, paving the way for concatenations into outer-level stabilizer codes and ultimately, fault-tolerant quantum computing. The team achieved this breakthrough by realizing a qubit exhibiting an erasure bias ratio exceeding 265, meaning erasures originate predominantly from one logical basis state. This research utilizes a transmon ancilla to perform logical measurements and mid-circuit erasure detections, enabling logical state assignment errors below 1% and converting over 99.3% of leakage errors into detectable erasures. By employing this method, the researchers established a strong error hierarchy within the logical subspace, exceeding the erasure error rate by factors of 31 and 15 with effective logical relaxation and dephasing rates of (6.2ms)−1 and (3.1ms)−1, respectively. This work builds upon the principle that biased-erasure qubits offer relaxed threshold requirements for quantum error correction, surpassing the capabilities of traditional dual-rail approaches. The implemented 02 qubit, leveraging the vacuum and two-photon Fock states, minimizes hardware overhead compared to previous encoding schemes requiring multiple qubits or cavities. Experiments show that the ancilla-assisted measurements and mid-circuit erasure detections are crucial for efficiently iden
