Variational Quantum Machine Learning with Quantum Error Detection

• URL: http://arxiv.org/abs/2504.06775v1
• Date: Wed, 09 Apr 2025 10:56:21 GMT
• Title: Variational Quantum Machine Learning with Quantum Error Detection
• Authors: Eromanga Adermann, Hajime Suzuki, Muhammad Usman,
• Abstract summary: 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.
• Score: 0.6435156676256051
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum machine learning (QML) is an emerging field that promises advantages such as faster training, improved reliability and superior feature extraction over classical counterparts. However, its implementation on quantum hardware is challenging due to the noise inherent in these systems, necessitating the use of quantum error correction (QEC) codes. Current QML research remains primarily theoretical, often assuming noise-free environments and offering little insight into the integration of QEC with QML implementations. To address this, we investigate the performance of a simple, parity-classifying Variational Quantum Classifier (VQC) implemented with the [[4,2,2]] error-detecting stabiliser code in a simulated noisy environment, marking the first study into the implementation of a QML algorithm with a QEC code. We invoke ancilla qubits to logically encode rotation gates, and classically simulate the logically-encoded VQC under two simple noise models representing gate noise and environmental noise. We demonstrate that the stabiliser code improves the training accuracy at convergence compared to noisy implementations without QEC. However, we find that the effectiveness and reliability of error detection is contingent upon keeping the ancilla qubit error rates below a specific threshold, due to the propagation of ancilla errors to the physical qubits. Our results provide an important insight: for QML implementations with QEC codes that both require ancilla qubits for logical rotations and cannot fully correct errors propagated between ancilla and physical qubits, the maximum achievable accuracy of the QML model is limited. This highlights the need for additional error correction or mitigation strategies to support the practical implementation of QML algorithms with QEC on quantum devices.

Related papers

• Learning to Measure Quantum Neural Networks [10.617463958884528]
  We introduce a novel approach that makes the observable of the quantum system-specifically, the Hermitian matrix-learnable. Our method features an end-to-end differentiable learning framework, where the parameterized observable is trained alongside the ordinary quantum circuit parameters. Using numerical simulations, we show that the proposed method can identify observables for variational quantum circuits that lead to improved outcomes.
  arXiv  Detail & Related papers  (2025-01-10T02:28:19Z)
• NAC-QFL: Noise Aware Clustered Quantum Federated Learning [9.752814421987246]
  This paper introduces a noise-aware clustered quantum federated learning system. It addresses noise mitigation, limited quantum device capacity, and high quantum communication costs. It enhances distributed QML performance and reduces communication costs.
  arXiv  Detail & Related papers  (2024-06-20T12:00:17Z)
• Logical Error Rates for a [[4,2,2]]-Encoded Variational Quantum Eigensolver Ansatz [0.0]
  We develop a framework to estimate the computational accuracy of near-term noisy, intermediate scale quantum computing devices.<n>Results indicate that current quantum computers can achieve error rates that yield useful outcomes for chemical applications.
  arXiv  Detail & Related papers  (2024-05-05T19:02:58Z)
• Benchmarking Quantum Generative Learning: A Study on Scalability and Noise Resilience using QUARK [0.3624329910445628]
  This paper investigates the scalability and noise resilience of quantum generative learning applications. We employ rigorous benchmarking techniques to track progress and identify challenges in scaling QML algorithms. We show that QGANs are not as affected by the curse of dimensionality as QCBMs and to which extent QCBMs are resilient to noise.
  arXiv  Detail & Related papers  (2024-03-27T15:05:55Z)
• 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)
• Dynamical subset sampling of quantum error correcting protocols [0.0]
  We show the capabilities of dynamical subset sampling with examples from fault-tolerant (FT) QEC. We show that, in a typical stabilizer simulation with incoherent Pauli noise of strength $p = 10-3$, our method can reach a required sampling accuracy on the logical failure rate.
  arXiv  Detail & Related papers  (2023-09-22T10:32:20Z)
• Compilation of a simple chemistry application to quantum error correction primitives [44.99833362998488]
  We estimate the resources required to fault-tolerantly perform quantum phase estimation on a minimal chemical example. We find that implementing even a simple chemistry circuit requires 1,000 qubits and 2,300 quantum error correction rounds.
  arXiv  Detail & Related papers  (2023-07-06T18:00:10Z)
• Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits [70.97518416003358]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning on noisy intermediate-scale quantum (NISQ) devices. While tensor-train networks (TTNs) can enhance VQC representation and generalization, the resulting hybrid model, TTN-VQC, faces optimization challenges due to the Polyak-Lojasiewicz (PL) condition. To mitigate this challenge, we introduce Pre+TTN-VQC, a pre-trained TTN model combined with a VQC.
  arXiv  Detail & Related papers  (2023-05-18T03:08:18Z)
• 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)
• 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)
• 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)
• Noise-robust ground state energy estimates from deep quantum circuits [0.0]
  We show how the underlying energy estimate explicitly filters out incoherent noise in quantum algorithms. We implement QCM for a model of quantum magnetism on IBM Quantum hardware. We find that QCM maintains a remarkably high degree of error robustness where VQE completely fails.
  arXiv  Detail & Related papers  (2022-11-16T09:12:55Z)
• Improved decoding of circuit noise and fragile boundaries of tailored surface codes [61.411482146110984]
  We introduce decoders that are both fast and accurate, and can be used with a wide class of quantum error correction codes. Our decoders, named belief-matching and belief-find, exploit all noise information and thereby unlock higher accuracy demonstrations of QEC. We find that the decoders led to a much higher threshold and lower qubit overhead in the tailored surface code with respect to the standard, square surface code.
  arXiv  Detail & Related papers  (2022-03-09T18:48:54Z)
• Quantum Approximate Optimization Algorithm Based Maximum Likelihood Detection [80.28858481461418]
  Recent advances in quantum technologies pave the way for noisy intermediate-scale quantum (NISQ) devices. Recent advances in quantum technologies pave the way for noisy intermediate-scale quantum (NISQ) devices.
  arXiv  Detail & Related papers  (2021-07-11T10:56:24Z)
• 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.