Online calibration scheme for training restricted Boltzmann machines with quantum annealing

• URL: http://arxiv.org/abs/2307.09785v2
• Date: Mon, 17 Feb 2025 11:00:32 GMT
• Title: Online calibration scheme for training restricted Boltzmann machines with quantum annealing
• Authors: Takeru Goto, Masayuki Ohzeki,
• Abstract summary: We propose a scheme to calibrate the internal parameters of a quantum annealer to obtain well-approximated samples for training a restricted Boltzmann machine (RBM)<n>Our results indicate that our scheme demonstrates performance on par with Gibbs sampling.
• Score: 0.552480439325792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a scheme to calibrate the internal parameters of a quantum annealer to obtain well-approximated samples for training a restricted Boltzmann machine (RBM). Empirically, samples from quantum annealers obey the Boltzmann distribution, making them suitable for RBM training. Quantum annealers utilize physical phenomena to generate a large number of samples in a short time. However, hardware imperfections make it challenging to obtain accurate samples. Existing research often estimates the inverse temperature for the compensation. Our scheme efficiently utilizes samples for RBM training also to estimate internal parameters. Furthermore, we consider additional parameters and demonstrate that they improve sample quality. We evaluate our approach by comparing the obtained samples with classical Gibbs sampling, which theoretically generates accurate samples. Our results indicate that our scheme demonstrates performance on par with Gibbs sampling. In addition, the training results with our estimation scheme outperform those of the contrastive divergence algorithm, a standard training algorithm for RBM.

Related papers

• Coherently mitigating boson samplers with stochastic errors [0.26388783516590225]
  Quantum devices such as boson samplers are susceptible to various errors, including fabrication imperfections. We propose a unitary averaging protocol which employs multiple boson samplers to generate a distribution that approximates the ideal boson sampler distribution. This results in a rigorous upper bound on the trace distance between the output probability induced by invertible vacuum-heralded networks.
  arXiv  Detail & Related papers  (2025-04-30T18:16:22Z)
• Predicting sampling advantage of stochastic Ising Machines for Quantum Simulations [0.0]
  We investigate the computational advantage of sIM for simulations of quantum magnets with neural-network quantum states (NQS) We study the sampling performance of sIM for NQS by comparing sampling on a software-emulated sIM with standard Metropolis-Hastings sampling for NQS. For the quantum Heisenberg models studied and experimental results on the runtime of sIMs, we project a possible speed-up of 100 to 10000.
  arXiv  Detail & Related papers  (2025-04-25T14:01:00Z)
• Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts [64.34482582690927]
  We provide an efficient and principled method for sampling from a sequence of annealed, geometric-averaged, or product distributions derived from pretrained score-based models. We propose Sequential Monte Carlo (SMC) resampling algorithms that leverage inference-time scaling to improve sampling quality.
  arXiv  Detail & Related papers  (2025-03-04T17:46:51Z)
• Iterated Denoising Energy Matching for Sampling from Boltzmann Densities [109.23137009609519]
  Iterated Denoising Energy Matching (iDEM) iDEM alternates between (I) sampling regions of high model density from a diffusion-based sampler and (II) using these samples in our matching objective. We show that the proposed approach achieves state-of-the-art performance on all metrics and trains $2-5times$ faster.
  arXiv  Detail & Related papers  (2024-02-09T01:11:23Z)
• Sample as You Infer: Predictive Coding With Langevin Dynamics [11.515490109360012]
  We present a novel algorithm for parameter learning in generic deep generative models. Our approach modifies the standard PC algorithm to bring performance on-par and exceeding that obtained from standard variational auto-encoder training.
  arXiv  Detail & Related papers  (2023-11-22T19:36:47Z)
• Certainty In, Certainty Out: REVQCs for Quantum Machine Learning [15.908051575681458]
  We discuss the statistical theory which enables highly accurate and precise sample inference. We show the effectiveness of this training method by assessing several effective variational quantum circuits.
  arXiv  Detail & Related papers  (2023-10-16T17:53:30Z)
• Entropy-based Training Methods for Scalable Neural Implicit Sampler [15.978655106034113]
  Efficiently sampling from un-normalized target distributions is a fundamental problem in scientific computing and machine learning. In this paper, we propose an efficient and scalable neural implicit sampler that overcomes these limitations. Our sampler can generate large batches of samples with low computational costs by leveraging a neural transformation that directly maps easily sampled latent vectors to target samples.
  arXiv  Detail & Related papers  (2023-06-08T05:56:05Z)
• Balanced Training of Energy-Based Models with Adaptive Flow Sampling [13.951904929884618]
  Energy-based models (EBMs) are versatile density estimation models that directly parameterize an unnormalized log density. We propose a new maximum likelihood training algorithm for EBMs that uses a different type of generative model, normalizing flows (NF) Our method fits an NF to an EBM during training so that an NF-assisted sampling scheme provides an accurate gradient for the EBMs at all times.
  arXiv  Detail & Related papers  (2023-06-01T13:58:06Z)
• A hybrid quantum-classical approach for inference on restricted Boltzmann machines [1.0928470926399563]
  A Boltzmann machine is a powerful machine learning model with many real-world applications. Statistical inference on a Boltzmann machine can be carried out by sampling from its posterior distribution. Quantum computers have the promise of solving some non-trivial problems in an efficient manner.
  arXiv  Detail & Related papers  (2023-03-31T11:10:31Z)
• Hard Sample Matters a Lot in Zero-Shot Quantization [52.32914196337281]
  Zero-shot quantization (ZSQ) is promising for compressing and accelerating deep neural networks when the data for training full-precision models are inaccessible. In ZSQ, network quantization is performed using synthetic samples, thus, the performance of quantized models depends heavily on the quality of synthetic samples. We propose HArd sample Synthesizing and Training (HAST) to address this issue.
  arXiv  Detail & Related papers  (2023-03-24T06:22:57Z)
• Sharp Calibrated Gaussian Processes [58.94710279601622]
  State-of-the-art approaches for designing calibrated models rely on inflating the Gaussian process posterior variance. We present a calibration approach that generates predictive quantiles using a computation inspired by the vanilla Gaussian process posterior variance. Our approach is shown to yield a calibrated model under reasonable assumptions.
  arXiv  Detail & Related papers  (2023-02-23T12:17:36Z)
• Importance sampling for stochastic quantum simulations [68.8204255655161]
  We introduce the qDrift protocol, which builds random product formulas by sampling from the Hamiltonian according to the coefficients. We show that the simulation cost can be reduced while achieving the same accuracy, by considering the individual simulation cost during the sampling stage. Results are confirmed by numerical simulations performed on a lattice nuclear effective field theory.
  arXiv  Detail & Related papers  (2022-12-12T15:06:32Z)
• Learning a Restricted Boltzmann Machine using biased Monte Carlo sampling [0.6554326244334867]
  We show that sampling the equilibrium distribution via Markov Chain Monte Carlo can be dramatically accelerated using biased sampling techniques. We also show that this sampling technique can be exploited to improve the computation of the log-likelihood gradient during the training too.
  arXiv  Detail & Related papers  (2022-06-02T21:29:01Z)
• Sample-Efficient Optimisation with Probabilistic Transformer Surrogates [66.98962321504085]
  This paper investigates the feasibility of employing state-of-the-art probabilistic transformers in Bayesian optimisation. We observe two drawbacks stemming from their training procedure and loss definition, hindering their direct deployment as proxies in black-box optimisation. We introduce two components: 1) a BO-tailored training prior supporting non-uniformly distributed points, and 2) a novel approximate posterior regulariser trading-off accuracy and input sensitivity to filter favourable stationary points for improved predictive performance.
  arXiv  Detail & Related papers  (2022-05-27T11:13:17Z)
• Distributionally Robust Models with Parametric Likelihood Ratios [123.05074253513935]
  Three simple ideas allow us to train models with DRO using a broader class of parametric likelihood ratios. We find that models trained with the resulting parametric adversaries are consistently more robust to subpopulation shifts when compared to other DRO approaches.
  arXiv  Detail & Related papers  (2022-04-13T12:43:12Z)
• Unrolling Particles: Unsupervised Learning of Sampling Distributions [102.72972137287728]
  Particle filtering is used to compute good nonlinear estimates of complex systems. We show in simulations that the resulting particle filter yields good estimates in a wide range of scenarios.
  arXiv  Detail & Related papers  (2021-10-06T16:58:34Z)
• Training Gaussian Boson Sampling Distributions [0.0]
  We derive analytical gradient formulas for the GBS distribution, which can be used to train devices using standard methods. In the case of training using a Kullback-Leibler divergence or log-likelihood cost function, we show that gradients can be computed classically, leading to fast training.
  arXiv  Detail & Related papers  (2020-04-09T18:53:55Z)

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.