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Bulletin of the American Physical Society

The recoverable quantum information: A smart reward for reinforcement learning of quantum error correction

Thomas Fösel, Petru Tighineanu, Talitha Weiß, Florian Marquardt

Published Mar 5, 2019• 0 citations
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Abstract In the ongoing race towards experimental implementations of quantum error correction (QEC), finding ways to automatically discover codes and encoding strategies tailored to the qubit hardware platform is emerging as a critical problem. Reinforcement learning (RL) has been identified as a promising approach, but so far it has been severely restricted in terms of scalability. In this work, we significantly expand the power of RL approaches to QEC code discovery. Explicitly, we train an RL agent that automatically discovers both QEC codes and their encoding circuits for a given gate set, qubit connectivity and error model, from scratch. This is enabled by a reward based on the Knill-Laflamme conditions and a vectorized Clifford simulator, showing its effectiveness with up to 25 physical qubits and distance 5 codes, while presenting a roadmap to scale this approach to 100 qubits and distance 10 codes in the near future. We also introduce the concept of a noise-aware meta-agent, which learns to produce encoding strategies simultaneously for a range of noise models, thus leveraging transfer of insights between different situations. Our approach opens the door towards hardware-adapted accelerated discovery of QEC approaches across the full spectrum of quantum hardware platforms of interest.

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Quantum error correction (QEC) is needed for building reliable and scalable quantum computers because of the susceptibility of quantum states to decoherence and operational noise. Reinforcement learning (RL) has recently emerged as an adaptive approach for quantum control and error mitiga- tion;however, existing RL-based QEC methods are largely re- stricted to small qubit systems and are often dependent on specific hardware architectures. We present a RL–enabled QEC ap- proach for a five qubit quantum system. The proposed approach relies exclusively on syndrome measurements and reward feed- back, ensuring architecture independence and portability across quantum platforms. The framework is evaluated using Qiskit and Cirq simulators under realistic noise models. Experimental results demonstrate improved logical qubit lifetimes and reduced error rates compared to static and smaller qubit correction strategies.
Machine learning with artificial neural networks is revolutionizing science.The most advanced challenges require discovering answers autonomously.In the domain of reinforcement learning, control strategies are improved according to a reward function.The power of neural-network-based reinforcement learning has been highlighted by spectacular recent successes such as playing Go, but its benefits for physics are yet to be demonstrated.Here, we show how a network-based "agent" can discover complete quantum-error-correction strategies, protecting a collection of qubits against noise.These strategies require feedback adapted to measurement outcomes.Finding them from scratch without human guidance and tailored to different hardware resources is a formidable challenge due to the combinatorially large search space.To solve this challenge, we develop two ideas: two-stage learning with teacher and student networks and a reward quantifying the capability to recover the quantum information stored in a multiqubit system.Beyond its immediate impact on quantum computation, our work more generally demonstrates the promise of neural-network-based reinforcement learning in physics.
Abstract In the ongoing race towards experimental implementations of quantum error correction (QEC), finding ways to automatically discover codes and encoding strategies tailored to the qubit hardware platform is emerging as a critical problem. Reinforcement learning (RL) has been identified as a promising approach, but so far it has been severely restricted in terms of scalability. In this work, we significantly expand the power of RL approaches to QEC code discovery. Explicitly, we train an RL agent that automatically discovers both QEC codes and their encoding circuits for a given gate set, qubit connectivity and error model, from scratch. This is enabled by a reward based on the Knill-Laflamme conditions and a vectorized Clifford simulator, showing its effectiveness with up to 25 physical qubits and distance 5 codes, while presenting a roadmap to scale this approach to 100 qubits and distance 10 codes in the near future. We also introduce the concept of a noise-aware meta-agent, which learns to produce encoding strategies simultaneously for a range of noise models, thus leveraging transfer of insights between different situations. Our approach opens the door towards hardware-adapted accelerated discovery of QEC approaches across the full spectrum of quantum hardware platforms of interest.