Machine Learning-based Quantum Error Mitigation for Variational Algorithms

• URL: http://arxiv.org/abs/2606.02697v1
• Date: Mon, 01 Jun 2026 18:00:00 GMT
• Title: Machine Learning-based Quantum Error Mitigation for Variational Algorithms
• Abstract summary: Machine Learning-based quantum error mitigation (ML-QEM) has emerged as a promising approach for improving the performance of noisy quantum algorithms.<n>We propose a practical ML-QEM protocol tailored to variational quantum algorithms, which generates training data by simulating (near-)Clifford circuits.<n>We benchmark the proposed method on the Variational Quantum Eigensolver (VQE) task for the Sherrington-Kirkpatrick Hamiltonian of up to $n=12$ qubits under various noise models, analyzing its effect on trainability and comparing its performance against standard Zero-Noise Extrapolation (ZNE
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine Learning-based quantum error mitigation (ML-QEM) has emerged as a promising approach for improving the performance of noisy quantum algorithms. However, existing ML-QEM methods often have restricted applicability to variational circuits and rely on inaccessible noiseless training data. In this work, we propose a practical ML-QEM protocol tailored to variational quantum algorithms, which generates training data by simulating (near-)Clifford circuits. This data is used for model selection and training, producing a mitigation model that can correct variational circuits with arbitrary parameters and transfer across different target Hamiltonians of similar structure. We benchmark the proposed method on the Variational Quantum Eigensolver (VQE) task for the Sherrington-Kirkpatrick Hamiltonian of up to $n=12$ qubits under various noise models, analyzing its effect on trainability and comparing its performance against standard Zero-Noise Extrapolation (ZNE). The results demonstrate consistent several-fold error suppression across all tested settings and superior performance over ZNE in the high-noise regime, providing evidence for the applicability of the proposed protocol to present-day NISQ processors.

Related papers

• RhoDARTS: Differentiable Quantum Architecture Search with Density Matrix Simulations [44.13836547616739]
  Variational Quantum Algorithms (VQAs) are a promising approach to leverage Noisy Intermediate-Scale Quantum (NISQ) computers.<n> choosing optimal quantum circuits that efficiently solve a given VQA problem is a non-trivial task.<n>Quantum Architecture Search (QAS) algorithms enable automatic generation of quantum circuits tailored to the provided problem.
  arXiv  Detail & Related papers  (2025-06-04T08:30:35Z)
• Statistical Signal Processing for Quantum Error Mitigation [12.804941908319792]
  We present a statistical signal processing approach to quantum error mitigation (QEM)<n>Our model assumes that circuit depth is sufficient for depolarizing noise, producing corrupted observations.<n>We show that our method scales to larger qubit counts using synthetically generated data consistent with our noise model.
  arXiv  Detail & Related papers  (2025-05-31T19:34:19Z)
• 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)
• An Efficient Quantum Classifier Based on Hamiltonian Representations [50.467930253994155]
  Quantum machine learning (QML) is a discipline that seeks to transfer the advantages of quantum computing to data-driven tasks.<n>We propose an efficient approach that circumvents the costs associated with data encoding by mapping inputs to a finite set of Pauli strings.<n>We evaluate our approach on text and image classification tasks, against well-established classical and quantum models.
  arXiv  Detail & Related papers  (2025-04-13T11:49:53Z)
• RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models [53.571195477043496]
  We propose an algorithm named Rotated Straight-Through-Estimator (RoSTE)<n>RoSTE combines quantization-aware supervised fine-tuning (QA-SFT) with an adaptive rotation strategy to reduce activation outliers.<n>Our findings reveal that the prediction error is directly proportional to the quantization error of the converged weights, which can be effectively managed through an optimized rotation configuration.
  arXiv  Detail & Related papers  (2025-02-13T06:44:33Z)
• Quantum Diffusion Models for Few-Shot Learning [13.13788757618812]
  We propose three new frameworks employing quantum diffusion model (QDM) as a solution for the few-shot learning. Experimental results demonstrate that our proposed algorithms significantly outperform existing methods.
  arXiv  Detail & Related papers  (2024-11-06T19:25:06Z)
• Learning Density Functionals from Noisy Quantum Data [0.0]
  noisy intermediate-scale quantum (NISQ) devices are used to generate training data for machine learning (ML) models. We show that a neural-network ML model can successfully generalize from small datasets subject to noise typical of NISQ algorithms. Our findings suggest a promising pathway for leveraging NISQ devices in practical quantum simulations.
  arXiv  Detail & Related papers  (2024-09-04T17:59:55Z)
• 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)
• 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)
• 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)
• Modeling Noisy Quantum Circuits Using Experimental Characterization [0.40611352512781856]
  Noisy intermediate-scale quantum (NISQ) devices offer unique platforms to test and evaluate the behavior of non-fault-tolerant quantum computing. We present a test-driven approach to characterizing NISQ programs that manages the complexity of noisy circuit modeling.
  arXiv  Detail & Related papers  (2020-01-23T16:45:49Z)

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.