Almost fault--tolerant quantum machine learning with drastic overhead reduction

• URL: http://arxiv.org/abs/2507.18954v1
• Date: Fri, 25 Jul 2025 04:43:37 GMT
• Title: Almost fault--tolerant quantum machine learning with drastic overhead reduction
• Authors: Haiyue Kang, Younghun Kim, Eromanga Adermann, Martin Sevior, Muhammad Usman,
• Abstract summary: This work proposes the idea of partial quantum error correction (QEC) for quantum machine learning (QML) models.<n>By assuming error-corrected two-qubit CNOTs (Clifford operations), we demonstrate that the QML models remain trainable even when single-qubit gates are subjected to depolarizing noise.
• Score: 1.481137211036747
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Errors in the current generation of quantum processors pose a significant challenge towards practical-scale implementations of quantum machine learning (QML) as they lead to trainability issues arising from noise-induced barren plateaus, as well as performance degradations due to the noise accumulation in deep circuits even when QML models are free from barren plateaus. Quantum error correction (QEC) protocols are being developed to overcome hardware noise, but their extremely high spacetime overheads, mainly due to magic state distillation, make them infeasible for near-term practical implementation. This work proposes the idea of partial quantum error correction (QEC) for quantum machine learning (QML) models and identifies a sweet spot where distillations are omitted to significantly reduce overhead. By assuming error-corrected two-qubit CNOTs (Clifford operations), we demonstrate that the QML models remain trainable even when single-qubit gates are subjected to $\approx0.2\%$ depolarizing noise, corresponding to a gate error rate of $\approx0.13\%$ under randomized benchmarking. Further analysis based on various noise models, such as phase-damping and thermal-dissipation channels at low temperature, indicates that the QML models are trainable independent of the mean angle of over-rotation, or can even be improved by thermal damping that purifies a quantum state away from depolarizations. While it may take several years to build quantum processors capable of fully fault-tolerant QML, our work proposes a resource-efficient solution for trainable and high-accuracy QML implementations in noisy environments.

Related papers

• Prospects of Quantum Error Mitigation for Quantum Signal Processing [0.0]
  This work explores the performance of zero-noise-extrapolation (ZNE) on a Hamiltonian simulation algorithm designed within quantum signal processing (QSP)<n>We quantify for which noise and depth regimes our ZNE protocol can recover an approximation of the noiseless expectation value.<n>We briefly discuss and present a numerical study on the region where ZNE is unusable, even given an unlimited sample budget.
  arXiv  Detail & Related papers  (2025-05-08T19:49:54Z)
• Variational Quantum Machine Learning with Quantum Error Detection [0.6435156676256051]
  Quantum machine learning (QML) is an emerging field that promises advantages such as faster training, improved reliability and superior extraction over classical counterparts.<n>Its implementation on quantum hardware is challenging due to the noise inherent in these systems, necessitating the use of quantum error correction (QEC) codes.<n>Current QML research remains primarily theoretical, often assuming noise-free environments and offering little insight into the integration of QEC with QML.
  arXiv  Detail & Related papers  (2025-04-09T10:56:21Z)
• 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)
• Lindblad-like quantum tomography for non-Markovian quantum dynamical maps [46.350147604946095]
  We introduce Lindblad-like quantum tomography (L$ell$QT) as a quantum characterization technique of time-correlated noise in quantum information processors.<n>We discuss L$ell$QT for the dephasing dynamics of single qubits in detail, which allows for a neat understanding of the importance of including multiple snapshots of the quantum evolution in the likelihood function.
  arXiv  Detail & Related papers  (2024-03-28T19:29:12Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Fault-tolerant quantum architectures based on erasure qubits [49.227671756557946]
  We exploit the idea of erasure qubits, relying on an efficient conversion of the dominant noise into erasures at known locations. We propose and optimize QEC schemes based on erasure qubits and the recently-introduced Floquet codes. Our results demonstrate that, despite being slightly more complex, QEC schemes based on erasure qubits can significantly outperform standard approaches.
  arXiv  Detail & Related papers  (2023-12-21T17:40:18Z)
• Real-time error mitigation for variational optimization on quantum hardware [45.935798913942904]
  We define a Real Time Quantum Error Mitigation (RTQEM) algorithm to assist in fitting functions on quantum chips with VQCs. Our RTQEM routine can enhance VQCs' trainability by reducing the corruption of the loss function.
  arXiv  Detail & Related papers  (2023-11-09T19:00:01Z)
• Variational Denoising for Variational Quantum Eigensolver [0.28675177318965045]
  The variational quantum eigensolver (VQE) is a hybrid algorithm that has the potential to provide a quantum advantage in practical chemistry problems. VQE faces challenges in task-specific design and machine-specific architecture, particularly when running on noisy quantum devices. We propose variational denoising, an unsupervised learning method that employs a parameterized quantum neural network to improve the solution of VQE.
  arXiv  Detail & Related papers  (2023-04-02T14:56:15Z)
• Adaptive quantum error mitigation using pulse-based inverse evolutions [0.0]
  We introduce a QEM method termed Adaptive KIK' that adapts to the noise level of the target device. The implementation of the method is experimentally simple -- it does not involve any tomographic information or machine-learning stage. We demonstrate our findings in the IBM quantum computers and through numerical simulations.
  arXiv  Detail & Related papers  (2023-03-09T02:50:53Z)
• Error Mitigation-Aided Optimization of Parameterized Quantum Circuits: Convergence Analysis [42.275148861039895]
  Variational quantum algorithms (VQAs) offer the most promising path to obtaining quantum advantages via noisy processors. gate noise due to imperfections and decoherence affects the gradient estimates by introducing a bias. Quantum error mitigation (QEM) techniques can reduce the estimation bias without requiring any increase in the number of qubits. QEM can reduce the number of required iterations, but only as long as the quantum noise level is sufficiently small.
  arXiv  Detail & Related papers  (2022-09-23T10:48:04Z)
• Simulating the Mott transition on a noisy digital quantum computer via Cartan-based fast-forwarding circuits [62.73367618671969]
  Dynamical mean-field theory (DMFT) maps the local Green's function of the Hubbard model to that of the Anderson impurity model. Quantum and hybrid quantum-classical algorithms have been proposed to efficiently solve impurity models. This work presents the first computation of the Mott phase transition using noisy digital quantum hardware.
  arXiv  Detail & Related papers  (2021-12-10T17:32:15Z)
• Sampling Overhead Analysis of Quantum Error Mitigation: Uncoded vs. Coded Systems [69.33243249411113]
  We show that Pauli errors incur the lowest sampling overhead among a large class of realistic quantum channels. We conceive a scheme amalgamating QEM with quantum channel coding, and analyse its sampling overhead reduction compared to pure QEM.
  arXiv  Detail & Related papers  (2020-12-15T15:51:27Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)

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.