Entanglement-induced provable and robust quantum learning advantages

• URL: http://arxiv.org/abs/2410.03094v2
• Date: Thu, 31 Jul 2025 01:32:55 GMT
• Title: Entanglement-induced provable and robust quantum learning advantages
• Authors: Haimeng Zhao, Dong-Ling Deng,
• Abstract summary: We make a step forward by rigorously establishing a noise-robust, unconditional quantum learning advantage.<n>Our proof is information-theoretic and pinpoints the origin of this advantage.<n>We show that the quantum model is trainable with constant resources and robust against constant noise.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum computing holds unparalleled potentials to enhance machine learning. However, a demonstration of quantum learning advantage has not been achieved so far. We make a step forward by rigorously establishing a noise-robust, unconditional quantum learning advantage in expressivity, inference speed, and training efficiency, compared to commonly-used classical models. Our proof is information-theoretic and pinpoints the origin of this advantage: entanglement can be used to reduce the communication required by non-local tasks. In particular, we design a task that can be solved with certainty by quantum models with a constant number of parameters using entanglement, whereas commonly-used classical models must scale linearly to achieve a larger-than-exponentially-small accuracy. We show that the quantum model is trainable with constant resources and robust against constant noise. Through numerical and trapped-ion experiments on IonQ Aria, we demonstrate the desired advantage. Our results provide valuable guidance for demonstrating quantum learning advantages with current noisy intermediate-scale devices.

Related papers

• Quantum learning advantage on a scalable photonic platform [5.788945534225034]
  quantum advantage in learning physical systems remains a largely untapped frontier.<n>We present a photonic implementation of a quantum-enhanced protocol for learning the probability distribution of a bosonic displacement process.<n>Our results demonstrate that even with non-ideal, noisy entanglement, a significant quantum advantage can be realized in continuous-variable quantum systems.
  arXiv  Detail & Related papers  (2025-02-11T18:52:43Z)
• Quantum Latent Diffusion Models [65.16624577812436]
  We propose a potential version of a quantum diffusion model that leverages the established idea of classical latent diffusion models. This involves using a traditional autoencoder to reduce images, followed by operations with variational circuits in the latent space. The results demonstrate an advantage in using a quantum version, as evidenced by obtaining better metrics for the images generated by the quantum version.
  arXiv  Detail & Related papers  (2025-01-19T21:24:02Z)
• Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems [77.88054335119074]
  We use FNOs to model the evolution of random quantum spin systems. We apply FNOs to a compact set of Hamiltonian observables instead of the entire $2n$ quantum wavefunction.
  arXiv  Detail & Related papers  (2024-09-05T07:18:09Z)
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
  Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties? We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• Classical Verification of Quantum Learning [42.362388367152256]
  We develop a framework for classical verification of quantum learning. We propose a new quantum data access model that we call "mixture-of-superpositions" quantum examples. Our results demonstrate that the potential power of quantum data for learning tasks, while not unlimited, can be utilized by classical agents.
  arXiv  Detail & Related papers  (2023-06-08T00:31:27Z)
• Shadows of quantum machine learning [2.236957801565796]
  We introduce a new class of quantum models where quantum resources are only required during training, while the deployment of the trained model is classical. We prove that this class of models is universal for classically-deployed quantum machine learning.
  arXiv  Detail & Related papers  (2023-05-31T18:00:02Z)
• Quantumness and Learning Performance in Reservoir Computing with a Single Oscillator [0.0]
  We show that the quantum nonlinear model is more effective in terms of learning performance compared to a classical non-linear oscillator. We examine the relationship between quantumness and performance by examining a broad range of initial states.
  arXiv  Detail & Related papers  (2023-04-07T03:37:55Z)
• Anticipative measurements in hybrid quantum-classical computation [68.8204255655161]
  We present an approach where the quantum computation is supplemented by a classical result. Taking advantage of its anticipation also leads to a new type of quantum measurements, which we call anticipative. In an anticipative quantum measurement the combination of the results from classical and quantum computations happens only in the end.
  arXiv  Detail & Related papers  (2022-09-12T15:47:44Z)
• Classical surrogates for quantum learning models [0.7734726150561088]
  We introduce the concept of a classical surrogate, a classical model which can be efficiently obtained from a trained quantum learning model. We show that large classes of well-analyzed re-uploading models have a classical surrogate.
  arXiv  Detail & Related papers  (2022-06-23T14:37:02Z)
• Quantum algorithms for quantum dynamics: A performance study on the spin-boson model [68.8204255655161]
  Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator. variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware. We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
  arXiv  Detail & Related papers  (2021-08-09T18:00:05Z)
• Enhancing Generative Models via Quantum Correlations [1.6099403809839032]
  Generative modeling using samples drawn from the probability distribution constitutes a powerful approach for unsupervised machine learning. We show theoretically that such quantum correlations provide a powerful resource for generative modeling. We numerically test this separation on standard machine learning data sets and show that it holds for practical problems.
  arXiv  Detail & Related papers  (2021-01-20T22:57:22Z)
• Power of data in quantum machine learning [2.1012068875084964]
  We show that some problems that are classically hard to compute can be easily predicted by classical machines learning from data. We propose a projected quantum model that provides a simple and rigorous quantum speed-up for a learning problem in the fault-tolerant regime.
  arXiv  Detail & Related papers  (2020-11-03T19:00:01Z)
• Experimental Quantum Generative Adversarial Networks for Image Generation [93.06926114985761]
  We experimentally achieve the learning and generation of real-world hand-written digit images on a superconducting quantum processor. Our work provides guidance for developing advanced quantum generative models on near-term quantum devices.
  arXiv  Detail & Related papers  (2020-10-13T06:57:17Z)
• Machine learning transfer efficiencies for noisy quantum walks [62.997667081978825]
  We show that the process of finding requirements on both a graph type and a quantum system coherence can be automated. The automation is done by using a convolutional neural network of a particular type that learns to understand with which network and under which coherence requirements quantum advantage is possible. Our results are of importance for demonstration of advantage in quantum experiments and pave the way towards automating scientific research and discoveries.
  arXiv  Detail & Related papers  (2020-01-15T18:36:53Z)

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.