Quantum Technology Initiative Journal Club

Thursday 30 Apr 2026, 16:00 → 17:00 Europe/Zurich

513/R-070 - Openlab Space (CERN)

Michele Grossi (CERN)

Weekly Journal Club meetings organised in the framework of the CERN Quantum Technology Initiative (QTI) to present and discuss scientific papers in the field of quantum science and technology. The goal is to help researchers keep track of current findings and walk away with ideas for their own research. Some previous knowledge of quantum physics would be helpful, but is not required to follow the talks.

To propose a paper for discussion, contact: michele.grossi@cern.ch

16:00 → 17:00 CERN QTI Journal CLUB

Convener: Dr Michele Grossi (CERN)

16:00 Reinforcement Learning Control of Quantum Error Correction

Abstract:
The promise of fault-tolerant quantum computing is challenged by environmental drift that relentlessly degrades the quality of quantum operations. The contemporary solution, halting the entire quantum computation for recalibration, is unsustainable for the long runtimes of the future algorithms. We address this challenge by unifying calibration with computation, granting the quantum error correction process a dual role: its error detection events are not only used to correct the logical quantum state, but are also repurposed as a learning signal, teaching a reinforcement learning (RL) agent to continuously steer the physical control parameters and stabilize the quantum system during the computation. We experimentally demonstrate this framework on a Willow superconducting processor, improving the logical stability of the surface code 3.5-fold against injected drift. By synthesizing our full suite of technological advances, including RL fine-tuning of the entire system and near-optimal decoding, we achieve record performance of the surface and color codes, with average logical error per cycle of εL=7.72(9)×10−4 and εL=8.19(14)×10−3 respectively. Simulations of surface codes up to distance-15 with tens of thousands control parameters confirm the scalability of our RL framework, revealing an optimization speed that is independent of the system size. This work thus enables a new paradigm: a quantum computer that learns from its errors and never stops computing.odels, although its source must be distinct from anticoncentration.

Speaker: Allegra Cuzzocrea (University Federico II and INFN, Naples (IT))