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Lecture notes in computer science

A Method to Reduce Resources for Quantum Error Correction

Ritajit Majumdar, Saikat Basu, Susmita Sur‐Kolay

Published Jan 1, 2017• 9 citations
Publisher
DOI
10.1007/978-3-319-59936-6_12

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Error Correction for Quantum Computers

WORLD SCIENTIFIC eBooks•Jul 1, 1998•0 citations
Quantum Error Correction (QEC) codes are a necessary component of utility-scale quantum computers, and the discovery of new codes with desirable properties is one of the most promising avenues for reducing the estimated resources required to perform useful quantum computations. Most current methods for code discovery rely on analytical techniques and construct codes from algebraic or geometric structures, but there also exist some techniques for discovering codes via computational search. In this work we focus on one of the most promising of these methods where a reinforcement learning agent is trained to construct encoding circuits and evaluates the performance of the corresponding code. We seek to improve upon this method by reducing the computational resources required to discover codes of given parameters, primarily by utilizing a probabilistic approximation of the reward function which relies on randomly sampling error operators according to some specified distribution. We investigate the performance of our modified framework and compare the discovered codes and required computational resources to the original work, concluding that probabilistic methods such as our sampling technique are effective strategies for reducing the computational cost of the evaluation component of code searching without excessive penalties to performance.
At the intersection of quantum computing and machine learning, quantum machine learning (QML) is poised to revolutionize artificial intelligence. However, the vulnerability of the current generation of quantum computers to noise and computational error poses a significant barrier to this vision. Whilst quantum error correction (QEC) offers a promising solution for almost any type of hardware noise, its application requires millions of qubits to encode even a simple logical algorithm, rendering it impractical in the near term. In this chapter, we examine strategies for integrating QEC and quantum error detection (QED) into QML under realistic resource constraints. We first quantify the resource demands of fully error-corrected QML and propose a partial QEC approach that reduces overhead while enabling error correction. We then demonstrate the application of a simple QED method, evaluating its impact on QML performance and highlighting challenges we have yet to overcome before we achieve fully fault-tolerant QML.
This paper presents a novel approach to quantum error correction (QEC) utilizing redundancy coding, focusing on enhanced efficiency in quantum information transmission. The core claim revolves around designing multi-particle error-correcting codes to minimize the resource consumption associated with encoding and decoding. The proposed mechanism involves encoding information across multiple qubits, leveraging error detection and correction capabilities inherent in these codes. Crucially, the strategy optimizes the distribution of redundant qubits, significantly reducing unnecessary information redundancy. The research addresses the limitations of existing QEC schemes, which often rely on excessive redundancy, impacting transmission efficiency. This work demonstrates a pathway towards more practical and resource-conscious QEC, representing a key step toward reliable long-distance quantum communication. The primary goal is to reduce the overhead associated with error correction, making quantum information transfer more viable. This is achieved through a refined approach to redundancy, concentrating correction resources where they are most needed.