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Turning noisy physical qubits into reliable logical qubits — the core problem of scalable quantum computing.
Quantum error correction protects quantum information by encoding it across many physical qubits. Instead of storing one logical qubit in a single atom or circuit, we spread it over a redundant code state.
Unlike classical bits, qubits cannot be copied [1]W. Wootters & W. Zurek (1982). A single quantum cannot be cloned. Nature 299, 802–803.. QEC therefore uses entanglement and stabilizer measurements to detect errors without directly measuring the protected quantum data.
The leading family of codes — topological codes such as surface codes and color codes — arranges qubits on lattices and checks local parity constraints [2]A. Kitaev (2003). Fault-tolerant quantum computation by anyons. Ann. Phys. 303, 2–30. [3]A. Fowler et al. (2012). Surface codes: Towards practical large-scale quantum computation. Phys. Rev. A 86, 032324..
When the physical error rate is below a threshold, increasing the code distance reduces the logical error rate exponentially — the hallmark of fault-tolerant quantum computing [3]A. Fowler et al. (2012). Surface codes: Towards practical large-scale quantum computation. Phys. Rev. A 86, 032324. [4]E. Dennis et al. (2002). Topological quantum memory. J. Math. Phys. 43, 4452–4505..
QEC is moving from theory to experiment. Below-threshold error correction has now been demonstrated [5]R. Acharya et al. (Google) (2024). Quantum error correction below the surface code threshold. arXiv:2408.13687., and the race is on to scale to logical qubits with longer lifetimes than any physical qubit.
We are building QEC tools that are both rigorous and practical. Our focus is on decoders that can run in real time, and on codes that match the constraints of near-term hardware.
We publish open-source decoder benchmarks and work closely with hardware teams to validate our methods on real error data.