Benchmarking Quantum Extreme Learning based on Gaussian Boson Sampling

• URL: http://arxiv.org/abs/2606.15230v1
• Date: Sat, 13 Jun 2026 10:00:10 GMT
• Title: Benchmarking Quantum Extreme Learning based on Gaussian Boson Sampling
• Abstract summary: Reservoir models offer a hardware-efficient learning paradigm for noisy intermediate-scale quantum devices.<n>We propose a quantum extreme learning machine that achieves high classification accuracy while reducing optical resource requirements.
• Score: 0.5161531917413708
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reservoir models offer a hardware-efficient learning paradigm for noisy intermediate-scale quantum devices by exploiting untrained quantum dynamics as a fixed feature map and restricting optimization to a simple classical readout layer. We propose a quantum extreme learning machine implemented using gaussian boson sampling and an encoding strategy that achieves high classification accuracy while reducing optical resource requirements. Classical inputs are jointly encoded in the squeezing parameters and in the interferometer unitary, enabling sampling-based, highly nonlinear feature maps while leveraging large-scale GBS output statistics, which are conjectured to be classically intractable. We systematically compare multiple families of quantum features accessible in the same setup and find that photon-number sampling probabilities provide the best performance, consistent with their higher effective feature dimensionality. Finally, we benchmark against classical nonlinear baselines and analyse robustness under noisy scenarios, showing competitive performance with fewer trainable parameters and indicating practical promise for near-term photonic implementations.

Related papers

• A Quantum-Inspired Dequantization Method for Diagonally Weighted Matrix Functions: Application to Learning with Optimized Random Features [0.9558392439655014]
  We develop a classical algorithm to address this type of quantum-advantage candidate.<n>Applying this method dequantizes the sampler for optimized random features, giving a classical sampler with prescribed accuracy and runtime.
  arXiv  Detail & Related papers  (2026-09-09T18:23:55Z)
• Robust Quantum Machine Learning for Collider Event Selection under Detector Variability [0.0]
  We investigate whether parameterised quantum models provide a useful inductive bias for robust collider-event selection.<n>In the unsupervised study, quantum autoencoders trained on background events are compared with classical and variational autoencoders for anomaly detection.<n>Under smearing, the quantum models generally exhibit smaller shifts in their output scores and retain their discrimination more effectively than the expressive classical baselines.
  arXiv  Detail & Related papers  (2026-08-11T18:29:08Z)
• Generative modeling with Gaussian Boson Sampling: classically trainable Bosonic Born Machines [0.1449880056570423]
  We introduce a photonic quantum generative model built on parametrized Gaussian Boson Sampling circuits.<n>The training is based on the efficient classical evaluation of expectation values enabled by the Gaussian structure of the state.<n>We demonstrate the effectiveness of the approach through numerical experiments on photonic systems with up to 805 modes and over a million trainable parameters.
  arXiv  Detail & Related papers  (2026-03-11T18:04:11Z)
• Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing [68.35481158940401]
  CL-QAS is a continual quantum architecture search framework.<n>It mitigates challenges of costly encoding amplitude and forgetting in variational quantum circuits.<n>It achieves controllable robustness expressivity, sample-efficient generalization, and smooth convergence without barren plateaus.
  arXiv  Detail & Related papers  (2026-01-10T02:36:03Z)
• Quantum Implicit Neural Representations for 3D Scene Reconstruction and Novel View Synthesis [42.138439537056954]
  Implicit neural representations (INRs) have become a powerful paradigm for continuous signal modeling and 3D scene reconstruction.<n>We present Quantum Neural Radiance Fields (Q-NeRF), the first hybrid quantum-classical framework for neural radiance field rendering.
  arXiv  Detail & Related papers  (2025-12-14T13:24:11Z)
• Classically Prepared, Quantumly Evolved: Hybrid Algorithm for Molecular Spectra [41.99844472131922]
  We introduce a hybrid classical-quantum algorithm to compute dynamical correlation functions and excitation spectra in many-body quantum systems.<n>The method combines classical preparation of a perturbed ground state with short-time quantum evolution of product states sampled from it.
  arXiv  Detail & Related papers  (2025-10-28T19:27:12Z)
• Matrix product state approach to lossy boson sampling and noisy IQP sampling [0.7066293026438526]
  We develop classical algorithms for lossy boson sampling and noisy instantaneous quantum-time sampling.<n>Our algorithm offers significantly improved control over the accuracy-efficiency trade-off.<n>It further extends the applicability of MPS simulation to broader classes of noisy quantum sampling models.
  arXiv  Detail & Related papers  (2025-10-28T07:23:10Z)
• A purely Quantum Generative Modeling through Unitary Scrambling and Collapse [6.647966634235082]
  Quantum Scrambling and Collapse Generative Model (QGen) is a purely quantum paradigm that eliminates classical dependencies.<n>We introduce a measurement-based training principle that decomposes learning into tractable subproblems, mitigating barren plateaus.<n> Empirically, QGen outperforms classical and hybrid baselines under matched parameter budget, while maintaining robustness under finite-shot sampling.
  arXiv  Detail & Related papers  (2025-06-12T11:00:21Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• 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)
• Bayesian Quantum Amplitude Estimation [46.03321798937855]
  We present BAE, a problem-tailored and noise-aware Bayesian algorithm for quantum amplitude estimation.<n>In a fault tolerant scenario, BAE is capable of saturating the Heisenberg limit; if device noise is present, BAE can dynamically characterize it and self-adapt.<n>We propose a benchmark for amplitude estimation algorithms and use it to test BAE against other approaches.
  arXiv  Detail & Related papers  (2024-12-05T18:09:41Z)
• Optimizing quantum noise-induced reservoir computing for nonlinear and chaotic time series prediction [2.5427629797261297]
  We make advancements to the quantum noise-induced reservoir, in which reservoir noise is used as a resource to generate expressive, nonlinear signals. We show that with only a single noise model and small memory capacities, excellent simulation results were obtained on nonlinear benchmarks.
  arXiv  Detail & Related papers  (2023-03-09T18:40:03Z)
• Generalization Metrics for Practical Quantum Advantage in Generative Models [68.8204255655161]
  Generative modeling is a widely accepted natural use case for quantum computers. We construct a simple and unambiguous approach to probe practical quantum advantage for generative modeling by measuring the algorithm's generalization performance. Our simulation results show that our quantum-inspired models have up to a $68 times$ enhancement in generating unseen unique and valid samples.
  arXiv  Detail & Related papers  (2022-01-21T16:35:35Z)
• Optimal non-classical correlations of light with a levitated nano-sphere [34.82692226532414]
  Nonclassical correlations provide a resource for many applications in quantum technology. Optomechanical systems can generate nonclassical correlations between the mechanical mode and a mode of travelling light. We propose automated optimization of the production of quantum correlations in such a system.
  arXiv  Detail & Related papers  (2020-06-26T15:27:47Z)

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.