Quantum Error Correction

Quantum error correction is the key element to implement fault-tolerant quantum computers. Without the ability to detect and correct errors that inevitably occur during quantum computations, the promise of large-scale, useful quantum computing would remain out of reach. The main focus of the research is implementing practical quantum error correction code for near-term hardwares.

Hardware optimized code

The goal of the research is to investigate and design hardware-optimized quantum error correction (QEC) codes. Current quantum computing platforms, such as superconducting and ion trap systems, face strict limitations on which qubits can interact. While QEC codes often achieve better performance with increased connectivity, this is not always feasible given hardware restrictions. Our focus is on studying and developing QEC codes that effectively balance performance with the specific connectivity constraints encountered in contemporary quantum hardware, with a particular emphasis on ion trap systems.

Error correction circuit optimization

The objective of the research is to optimize error correction circuits for fault-tolerant quantum computing. A key aspect of error correction involves measuring various syndromes within each QEC cycle. This syndrome measurement process typically accounts for a substantial number of physical quantum gate operations. To address this, we will devise syndrome measurement circuits tailored to specific quantum error correction codes and perform simulations to evaluate their performance and efficiency.

Implementing syndrome decoder

The research focuses on the implementation of syndrome decoders for real-time feedback in quantum error correction. The rapid cycle times required for real-time feedback in quantum error correction necessitate efficient decoding algorithms to meet performance targets. We are developing classical algorithms for syndrome decoding. Specifically, we are investigating graph-based decoders for topological codes and exploring machine-learning-based decoding approaches.