machine learning quantum error correction

MACHINE LEARNING QUANTUM ERROR CORRECTION PDF
MACHINE LEARNING QUANTUM ERROR CORRECTION CODE

An in situ implementation of such a machine learning-assisted quantum error correction will be a decisive step to push the entanglement frontier beyond the NISQ horizon. We propose a machine learning algorithm for continuous quantum error correction that is based on the use of a recurrent neural network to identify bit-flip. compilation and mapping of quantum algorithms quantum error correction fault tolerant. In standard convolution neural networks (CNNs) the data is presented on a. Interests: 5G-NR quantum communication machine learning. Research on machine learning-based decoders is increasingly popular. Focusing on geometric machine learning, we first want to resonate the purpose of it. We show that such a preprocessing step allows to effectively reduce the error rate by up to 2 orders of magnitude in practical applications and, by detecting correlation effects, shifts the actual error threshold up to fifteen percent higher than the threshold of conventional error correction algorithms such as union find or minimum weight perfect matching, even in the presence of measurement errors. while low-level decoding uses the error syndrome to correct individual data qubits.

The key innovation is to autodecode error syndromes on small scales by shifting a preprocessing window over the underlying code, akin to a convolutional neural network in pattern recognition approaches. Here, we present a neural network based decoder that, for a family of stabilizer codes subject to depolarizing noise and syndrome measurement errors, is scalable to tens of thousands of qubits (in contrast to other recent machine learning inspired decoders) and exhibits faster decoding times than the state-of-the-art union find decoder for a wide range of error rates (down to 1%). However, to go beyond proof-of-principle calculations, the current processing architectures will need to scale up to larger quantum circuits which will require fast and scalable algorithms for quantum error correction. Show that agents trained on one setting are able to successfully transfer theirĮxperience to different settings.With the advent of noisy intermediate-scale quantum (NISQ) devices, practical quantum computing has seemingly come into reach. The number of data qubits, for various error models of interest. Reinforcement learning agent can determine near-optimal solutions, in terms of Using efficient simulations with aboutħ0 data qubits with arbitrary connectivity, we demonstrate that such a

MACHINE LEARNING QUANTUM ERROR CORRECTION CODE

We consider a reinforcement learningĪgent tasked with modifying a family of surface code quantum memories until aĭesired logical error rate is reached. The intern will work on new approaches for improving the performance (accuracy and speed) of quantum error correction algorithms. Present a reinforcement learning framework for optimizing and fault-tolerantlyĪdapting quantum error correction codes. Machine learning for quantum error correction Summary Quantum error correction is necessary to run large scale quantum computations and are an active research area with quantum information processing. However, determining the most suited encoding for unknownĮrror channels or specific laboratory setups is highly challenging. error correcting codes (EAQECCs) with good error Wiebe, and S.

MACHINE LEARNING QUANTUM ERROR CORRECTION PDF

Briegel, Nicolai Friis Download PDF Abstract: Quantum error correction is widely thought to be the key to fault-tolerant Quantum Error Correction Using Shors Bit-Flip Code.

Authors: Hendrik Poulsen Nautrup, Nicolas Delfosse, Vedran Dunjko, Hans J.

Machine learning quantum error correction