Speaker
Description
Quantum computers have the potential to deliver extraordinary computational power by harnessing unique quantum phenomena such as superposition and entanglement. However, quantum systems are very susceptible to noise, and scalable quantum computers will likely require mechanisms to systematically and reliably detect and correct errors during computations. To tackle this bottleneck, a wide variety of quantum error protocols involving the encoding of quantum information into physical systems have been proposed. While widely studied error correcting codes, such as the surface code, offer excellent protection and a relatively straightforward physical implementation due to nearest-neighbour connectivity, they do not scale with the system size; even as the number of physical qubits on a device is increased, the amount of logical quantum information they encode stays constant. Recently, there has been an increasing amount of research investigating more efficient alternatives for quantum error correction, most notably the class of quantum low-density parity check (qLDPC) codes. Among these, quantum Tanner codes, a family of quantum error-correcting codes based on group structures and products of classical codes, stands out due to its excellent scaling properties, low resource overheads, and efficient decoding. In this work (arxiv:2508.05095), we construct explicit instances of quantum Tanner codes of experimentally relevant sizes and evaluate their error-correcting performance in the presence of hardware-inspired noise. We find that their performance approaches that of leading quantum-error correcting codes on similar scales while, in some cases, incurring a significantly reduced overhead cost in terms of physical resources required per logical qubit. This work represents a step towards the physical implementation of cost-efficient, effective, and scalable quantum error-correcting codes.
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