Applied Physics/Physics Colloquium: Robert Huang - "High-rate qLDPC processors"

Abstract: Despite significant progress on quantum low-density parity-check (qLDPC) codes, building qLDPC processors that are high-rate, high-throughput, hardware-friendly, and fast-to-decode remains a challenge. We introduce mitten codes, a family of qLDPC processor codes of encoding rate 20% and check weight 9, based on non-abelian groups. Their non-abelian structure evades distance bounds constraining abelian counterparts, allowing mitten codes to reach distance 18 and beyond with just a few hundred data qubits. The logical operators of a mitten code are related by the group action, yielding a modular, low-overhead logical toolkit: full Clifford operations follow from bridging two reusable seed surgery gadgets or from a single fixed extractor. Furthermore, qLDPC processors based on mitten codes support high-rate surgery that executes many logical measurements in parallel, and parallel magic-state injection into all logical qubits at once. Under circuit-level noise, with our fast decoder, the [[300,60,14]] mitten code achieves, without extrapolation, a block logical error rate of ~10^-11per round at 0.1% physical error rate (PER), while the [[975,195,≤24]] code reaches ~10^-8 at 0.4% PER. Decoding 15 billion surgery experiments on the [[540,108,18]] code at 0.1% PER, we observe only two logical failures, demonstrating a qLDPC processor capable of running ~10¹⁰ logical operations. Our decoder is compatible with sub-millisecond average latency per logical cycle, sufficient for real-time decoding on neutral atom hardware. Discovered by an end-to-end design pipeline built on sQetch, a distance estimator orders of magnitude faster than existing tools, and mapping efficiently onto near-term neutral atom and superconducting hardware, mitten codes open a practical path toward fault-tolerant quantum computation.

 

Hsin-Yuan Huang (Robert) is the Chief Technology Officer of Oratomic. He is currently on leave from being an Assistant Professor of Theoretical Physics at Caltech. He completed his Ph.D. at Caltech under John Preskill and Thomas Vidick. His research leverages learning theory and learning algorithms to advance quantum computation, physics, and information science, with contributions including classical shadow tomography, machine learning for quantum many-body problems, and quantum advantages in learning from experiments. He is a recipient of the Milton and Francis Clauser Doctoral Prize for the most original Caltech doctoral thesis of 2024 and the William H. Hurt Scholar endowed professorship.