Q-Embroidery: A Study on Weaving Quantum Error Correction into the Fabric of Quantum Classifiers

• URL: http://arxiv.org/abs/2402.11127v3
• Date: Mon, 4 Mar 2024 01:18:05 GMT
• Title: Q-Embroidery: A Study on Weaving Quantum Error Correction into the Fabric of Quantum Classifiers
• Authors: Avimita Chatterjee, Debarshi Kundu and Swaroop Ghosh
• Abstract summary: This study makes a pioneering contribution by applying quantum error correction codes (QECCs) for complex, multi-qubit classification tasks. We implement 1-qubit and 2-qubit quantum classifiers with QECCs, specifically the Steane code, and the distance 3 & 5 surface codes to analyze 2-dimensional and 4-dimensional datasets. Results emphasize that the effectiveness of a QECC in practical scenarios depends on various factors, including qubit availability, desired accuracy, and the specific types and levels of physical errors.
• Score: 2.348041867134616
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Quantum computing holds transformative potential for various fields, yet its practical application is hindered by the susceptibility to errors. This study makes a pioneering contribution by applying quantum error correction codes (QECCs) for complex, multi-qubit classification tasks. We implement 1-qubit and 2-qubit quantum classifiers with QECCs, specifically the Steane code, and the distance 3 & 5 surface codes to analyze 2-dimensional and 4-dimensional datasets. This research uniquely evaluates the performance of these QECCs in enhancing the robustness and accuracy of quantum classifiers against various physical errors, including bit-flip, phase-flip, and depolarizing errors. The results emphasize that the effectiveness of a QECC in practical scenarios depends on various factors, including qubit availability, desired accuracy, and the specific types and levels of physical errors, rather than solely on theoretical superiority.

Related papers

• Extending Quantum Perceptrons: Rydberg Devices, Multi-Class Classification, and Error Tolerance [67.77677387243135]
  Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML) At the core of QNC is the quantum perceptron (QP), which leverages the analog dynamics of interacting qubits to enable universal quantum computation.
  arXiv  Detail & Related papers  (2024-11-13T23:56:20Z)
• Towards Distributed Quantum Error Correction for Distributed Quantum Computing [15.824983694947573]
  A new qubit-based Distributed Quantum Error Correction (DQEC) architecture is proposed in which three physical qubits residing on three Quantum Processing Units (QPU) are used to form a logical qubit. This paper illustrates how three QPUs collaboratively generate a joint quantum state in which single bit-flip and phase-flip errors can be properly resolved. The functional correctness of the proposed architecture is evaluated through the Qiskit tool and stabilizer generators.
  arXiv  Detail & Related papers  (2024-09-08T23:10:00Z)
• Quantum Imitation Learning [74.15588381240795]
  We propose quantum imitation learning (QIL) with a hope to utilize quantum advantage to speed up IL. We develop two QIL algorithms, quantum behavioural cloning (Q-BC) and quantum generative adversarial imitation learning (Q-GAIL) Experiment results demonstrate that both Q-BC and Q-GAIL can achieve comparable performance compared to classical counterparts.
  arXiv  Detail & Related papers  (2023-04-04T12:47:35Z)
• Variational Quantum Eigensolver for Classification in Credit Sales Risk [0.5524804393257919]
  We take into consideration a quantum circuit which is based on the Variational Quantum Eigensolver (VQE) and so-called SWAP-Test. In the utilized data set, two classes may be observed -- cases with low and high credit risk. The solution is compact and requires only logarithmically increasing number of qubits.
  arXiv  Detail & Related papers  (2023-03-05T23:08:39Z)
• Deep Quantum Error Correction [73.54643419792453]
  Quantum error correction codes (QECC) are a key component for realizing the potential of quantum computing. In this work, we efficiently train novel emphend-to-end deep quantum error decoders. The proposed method demonstrates the power of neural decoders for QECC by achieving state-of-the-art accuracy.
  arXiv  Detail & Related papers  (2023-01-27T08:16:26Z)
• Automatic Implementation and Evaluation of Error-Correcting Codes for Quantum Computing: An Open-Source Framework for Quantum Error Correction [2.1801327670218855]
  Real quantum computers are plagued by frequent noise effects that cause errors during computations. Quantum error-correcting codes address this problem by providing means to identify and correct corresponding errors. We propose an open-source framework that automatically applies error-correcting codes for a given application followed by an automatic noise-aware quantum circuit simulation.
  arXiv  Detail & Related papers  (2023-01-13T19:12:22Z)
• Measuring NISQ Gate-Based Qubit Stability Using a 1+1 Field Theory and Cycle Benchmarking [50.8020641352841]
  We study coherent errors on a quantum hardware platform using a transverse field Ising model Hamiltonian as a sample user application. We identify inter-day and intra-day qubit calibration drift and the impacts of quantum circuit placement on groups of qubits in different physical locations on the processor. This paper also discusses how these measurements can provide a better understanding of these types of errors and how they may improve efforts to validate the accuracy of quantum computations.
  arXiv  Detail & Related papers  (2022-01-08T23:12:55Z)
• Crosstalk Suppression for Fault-tolerant Quantum Error Correction with Trapped Ions [62.997667081978825]
  We present a study of crosstalk errors in a quantum-computing architecture based on a single string of ions confined by a radio-frequency trap, and manipulated by individually-addressed laser beams. This type of errors affects spectator qubits that, ideally, should remain unaltered during the application of single- and two-qubit quantum gates addressed at a different set of active qubits. We microscopically model crosstalk errors from first principles and present a detailed study showing the importance of using a coherent vs incoherent error modelling and, moreover, discuss strategies to actively suppress this crosstalk at the gate level.
  arXiv  Detail & Related papers  (2020-12-21T14:20:40Z)
• Quantum One-class Classification With a Distance-based Classifier [1.316309856358873]
  existing errors in the current quantum hardware and the low number of qubits available make it necessary to use solutions that use fewer qubits and fewer operations. We present a new classifier based on named Quantum One-class Quantum computers (QOCC) that consists of a minimal quantum machine learning model with fewer operations and qubits.
  arXiv  Detail & Related papers  (2020-07-31T17:53:00Z)
• Deterministic correction of qubit loss [48.43720700248091]
  Loss of qubits poses one of the fundamental obstacles towards large-scale and fault-tolerant quantum information processors. We experimentally demonstrate the implementation of a full cycle of qubit loss detection and correction on a minimal instance of a topological surface code.
  arXiv  Detail & Related papers  (2020-02-21T19:48:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.