Single-shot quantum neural networks with amplitude estimation

• URL: http://arxiv.org/abs/2604.19320v1
• Date: Tue, 21 Apr 2026 10:29:09 GMT
• Title: Single-shot quantum neural networks with amplitude estimation
• Abstract summary: Quantum neural networks (QNNs) suffer from a fundamental sampling bottleneck since quantum measurements are probabilistic.<n>This work introduces a "single-shot" QNN framework by integrating quantum amplitude estimation (AE) into the readout stage.<n>We demonstrate that AE-based QNN inference achieves an $mathcalO (1/N)$ error even with a single shot.
• Score: 2.7412662946127764
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum neural networks (QNNs) suffer from a fundamental sampling bottleneck since quantum measurements are probabilistic, requiring many circuit executions to estimate outputs with sufficient accuracy. Conventional Monte-Carlo (MC) inference exhibits an $\mathcal{O}(1/\sqrt{N})$ sampling error, rendering QNN inference and training costly on near-term quantum hardware, especially where each shot requires expensive qubit generation. This work introduces a "single-shot" QNN framework by integrating quantum amplitude estimation (AE) into the readout stage. By embedding a trained QNN as a state-preparation oracle within AE, outputs are estimated through coherent interference rather than repeated sampling. We demonstrate that AE-based QNN inference achieves an $\mathcal{O}(1/N)$ error even with a single shot. We further analyze noise robustness and training feasibility, showing that AE can be a powerful primitive for overcoming the sampling overhead of QNNs. This highlights that when the model itself is quantum, quantum algorithms can enhance the computation efficiency.

Related papers

• Quantum-Enhanced Neural Contextual Bandit Algorithms [50.880384999888044]
  This paper introduces the Quantum Neural Tangent Kernel-Upper Confidence Bound (QNTK-UCB) algorithm.<n>QNTK-UCB is a novel algorithm that leverages the Quantum Neural Tangent Kernel (QNTK) to address these limitations.
  arXiv  Detail & Related papers  (2026-01-06T09:58:14Z)
• QuanTest: Entanglement-Guided Testing of Quantum Neural Network Systems [45.18451374144537]
  Quantum Neural Network (QNN) combines the Deep Learning (DL) principle with the fundamental theory of quantum mechanics to achieve machine learning tasks with quantum acceleration. QNN systems differ significantly from traditional quantum software and classical DL systems, posing critical challenges for QNN testing. We propose QuanTest, a quantum entanglement-guided adversarial testing framework to uncover potential erroneous behaviors in QNN systems.
  arXiv  Detail & Related papers  (2024-02-20T12:11:28Z)
• 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)
• Randomness-enhanced expressivity of quantum neural networks [7.7991930692137466]
  We propose a novel approach to enhance the expressivity of QNNs by incorporating randomness into quantum circuits. We prove that our approach can accurately approximate arbitrary target operators using Uhlmann's theorem for majorization. We find the expressivity of QNNs is enhanced by introducing randomness for multiple learning tasks, which could have broad application in quantum machine learning.
  arXiv  Detail & Related papers  (2023-08-09T07:17:13Z)
• Error-tolerant quantum convolutional neural networks for symmetry-protected topological phases [0.0]
  Quantum neural networks based on parametrized quantum circuits, measurements and feed-forward can process large amounts of quantum data. We construct quantum convolutional neural networks (QCNNs) that can recognize different symmetry-protected topological phases. We show that the QCNN output is robust against symmetry-breaking errors below a threshold error probability.
  arXiv  Detail & Related papers  (2023-07-07T16:47:02Z)
• A Quantum Optical Recurrent Neural Network for Online Processing of Quantum Times Series [0.7087237546722617]
  We show that a quantum optical recurrent neural network (QORNN) can enhance the transmission rate of quantum channels. We also show that our model can counteract similar memory effects if they are unwanted. We run a small-scale version of this last task on the photonic processor Borealis.
  arXiv  Detail & Related papers  (2023-05-31T19:19:25Z)
• Towards Neural Variational Monte Carlo That Scales Linearly with System Size [67.09349921751341]
  Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors. The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems. We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
  arXiv  Detail & Related papers  (2022-12-21T19:00:04Z)
• Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlow [25.408147000243158]
  This paper aims to tackle the noise issue from another angle. Instead of creating perfect qubits for general quantum algorithms, we investigate the potential to mitigate the noise issue for dedicate algorithms. This paper targets quantum neural network (QNN), and proposes to learn the errors in the training phase, so that the identified QNN model can be resilient to noise.
  arXiv  Detail & Related papers  (2021-09-08T04:43:12Z)
• A quantum algorithm for training wide and deep classical neural networks [72.2614468437919]
  We show that conditions amenable to classical trainability via gradient descent coincide with those necessary for efficiently solving quantum linear systems. We numerically demonstrate that the MNIST image dataset satisfies such conditions. We provide empirical evidence for $O(log n)$ training of a convolutional neural network with pooling.
  arXiv  Detail & Related papers  (2021-07-19T23:41:03Z)
• Toward Trainability of Quantum Neural Networks [87.04438831673063]
  Quantum Neural Networks (QNNs) have been proposed as generalizations of classical neural networks to achieve the quantum speed-up. Serious bottlenecks exist for training QNNs due to the vanishing with gradient rate exponential to the input qubit number. We show that QNNs with tree tensor and step controlled structures for the application of binary classification. Simulations show faster convergent rates and better accuracy compared to QNNs with random structures.
  arXiv  Detail & Related papers  (2020-11-12T08:32:04Z)
• On the learnability of quantum neural networks [132.1981461292324]
  We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme. We show that if a concept can be efficiently learned by QNN, then it can also be effectively learned by QNN even with gate noise.
  arXiv  Detail & Related papers  (2020-07-24T06:34:34Z)

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.