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Machine learning quantum error correction12/30/2023 Discovering hardware-adapted strategies automatically using artificial neural networks may provide a solution to this problem, but it is a formidable task because of the huge search space of strategies. While generic quantum error correction schemes have been invented, it is a critical open problem to find efficient strategies to protect qubits against noise in the specific hardware setting of an arbitrary real-world device. In particular, we demonstrate that artificial neural networks can discover novel strategies to improve the performance of quantum computing devices.īuilding large-scale quantum computers is an outstanding challenge because of the fragility of qubits, the fundamental units of a quantum computer. Here, we apply advanced techniques from the field of deep-reinforcement learning to solve formidable challenges in quantum physics. Beyond its immediate impact on quantum computation, our work more generally demonstrates the promise of neural-network-based reinforcement learning in physics.Īrtificial neural networks are revolutionizing science and technology, from image recognition and language processing to drug discovery and playing complex games. 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. Finding them from scratch without human guidance and tailored to different hardware resources is a formidable challenge due to the combinatorially large search space. These strategies require feedback adapted to measurement outcomes. Here, we show how a network-based “agent” can discover complete quantum-error-correction strategies, protecting a collection of qubits against noise. 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. In the domain of reinforcement learning, control strategies are improved according to a reward function. The most advanced challenges require discovering answers autonomously. Literature for the ] code, the ] code, and the ] code.Machine learning with artificial neural networks is revolutionizing science. Additionally, it generates logical gates not found in the current Simulation on classical computers on small quantum codes of four qubits toįifteen qubits and show that it finds most logical gates known in the current It enables automatic discovery of logical gates fromĪnalytically designed error correcting codes and can be extended to errorĬorrecting codes found by numerical optimizations. This procedure can be implemented on near-term quantum computers via quantum Learning both the logical gates and the physical operations implementing them. Our technique is to use variational circuits for Procedure which generates logical operations given known encoding andĬorrecting procedures. Operations for quantum error correcting codes. Here we study the problem of designing logical Breuckmann, Edward Grant Download PDF Abstract: Quantum error correcting codes protect quantum computation from errors causedīy decoherence and other noise. Authors: Hongxiang Chen, Michael Vasmer, Nikolas P.
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