Scheduling of syndrome measurements with a few ancillary qubits
AI Breakdown
Get a structured breakdown of this paper — what it's about, the core idea, and key takeaways for the field.
Abstract
Quantum error-correcting codes are a vital technology for demonstrating reliable quantum computation. They require data qubits for encoding quantum information and ancillary qubits for taking error syndromes necessary for error correction. The need for a large number of ancillary qubits is an overhead specific to quantum computing, and it prevents the scaling of quantum computers to a useful size. In this work, we propose a framework for generating efficient syndrome measurement circuits with a few ancillary qubits in CSS codes and provide a method to minimize the total number of physical qubits in general settings. We demonstrated our proposal by applying it to surface codes, and we generated syndrome measurement circuits under several constraints of total qubit count. As a result, we find that balanced data and ancillary qubit counts achieve the lower logical error rates under a fixed total number of physical qubits. This result indicates that using fewer ancillary qubits than the number of stabilizers can be effective for reducing logical error rates in a practical noise model.