Forging ahead for fault-tolerant quantum computing

Quantum computing has come a long way in the last 20 years, and, thanks to its compatibility with semiconductor technology, superconducting quantum computing (SQC) represents enormous potential. But, as with classical computing, an ability to self-correct is necessary since noise and decoherence cause routine qubit errors. Continuous, rapid, and clear resetting of qubits to the ground state is therefore required for “fault-tolerant” quantum computing.

As an alternative to the dominant qubit initialization method of natural relaxation, which is relatively slow and inefficient, qubit active reset has recently emerged as a promising solution. Han et al. demonstrated a superconducting qubit active reset experiment using feedback electronics based on RF switches.

“This can be viewed as a simple demonstration of a Maxwell’s demon experiment on the superconducting quantum computing platform,” said author Sheng-Kai Liao. “Active reset by feedback control is achieved by changing the state of the RF switch. As a result, the system only needs to transmit switch control signals, eliminating the need for transmitting complete readout results. Additionally, our system retains the capability for full-information feedback. And the use of RF switches can simplify hardware design and reduce time consumption without compromising system integration.”

The study yielded impressive results. Compared to results from the natural relaxation methodology, the time consumption was reduced by an order of magnitude and the error rate was cut in half.

“This active reset method provides a flexible approach for qubit reset, and the demonstrated active reset method based on RF switches is expected to be widely used in future large-scale quantum computers,” says Liao.

Source: “Active reset of superconducting qubits using the electronics based on RF switches,” by Lian-Chen Han, Yu Xu, Jin Lin, Fu-Sheng Chen, Shao-Wei Li, Cheng Guo, Na Li, Dong-Dong Li, Yu-Huai Li, Ming Gong, Sheng-Kai Liao, and Cheng-Zhi Peng, AIP Advances (2023). The article can be accessed at http://doi.org/10.1063/5.0166535 .