Digitized Counterdiabatic Quantum Sampling

• URL: http://arxiv.org/abs/2510.26735v1
• Date: Thu, 30 Oct 2025 17:32:49 GMT
• Title: Digitized Counterdiabatic Quantum Sampling
• Authors: Narendra N. Hegade, Nachiket L. Kortikar, Balaganchi A. Bhargava, Juan F. R. Hernández, Alejandro Gomez Cadavid, Pranav Chandarana, Sebastián V. Romero, Shubham Kumar, Anton Simen, Anne-Maria Visuri, Enrique Solano, Paolo A. Erdman,
• Abstract summary: We propose a hybrid quantum-classical algorithm for efficient sampling from energy-based models, such as low-temperature Boltzmann distributions.<n>We show that classical sampling algorithms, including Metropolis-Hastings and the state-of-the-art low-temperature technique parallel tempering, require up to three orders of magnitude more samples to match the quality of DCQS.
• Score: 29.85893589594055
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose digitized counterdiabatic quantum sampling (DCQS), a hybrid quantum-classical algorithm for efficient sampling from energy-based models, such as low-temperature Boltzmann distributions. The method utilizes counterdiabatic protocols, which suppress non-adiabatic transitions, with an iterative bias-field procedure that progressively steers the sampling toward low-energy regions. We observe that the samples obtained at each iteration correspond to approximate Boltzmann distributions at effective temperatures. By aggregating these samples and applying classical reweighting, the method reconstructs the Boltzmann distribution at a desired temperature. We define a scalable performance metric, based on the Kullback-Leibler divergence and the total variation distance, to quantify convergence toward the exact Boltzmann distribution. DCQS is validated on one-dimensional Ising models with random couplings up to 124 qubits, where exact results are available through transfer-matrix methods. We then apply it to a higher-order spin-glass Hamiltonian with 156 qubits executed on IBM quantum processors. We show that classical sampling algorithms, including Metropolis-Hastings and the state-of-the-art low-temperature technique parallel tempering, require up to three orders of magnitude more samples to match the quality of DCQS, corresponding to an approximately 2x runtime advantage. Boltzmann sampling underlies applications ranging from statistical physics to machine learning, yet classical algorithms exhibit exponentially slow convergence at low temperatures. Our results thus demonstrate a robust route toward scalable and efficient Boltzmann sampling on current quantum processors.

Related papers

• Partition Function Estimation Using Analog Quantum Processors [4.239775815863115]
  We evaluate using programmable superconducting flux qubit D-Wave quantum annealers to approximate the partition function of Ising models.<n>We find that fast quench-like anneals can quickly generate ensemble distributions that are very good estimates of the true partition function of the classical Ising model.
  arXiv  Detail & Related papers  (2025-12-22T18:58:46Z)
• FALCON: Few-step Accurate Likelihoods for Continuous Flows [78.37361800856583]
  We propose Few-step Accurate Likelihoods for Continuous Flows (FALCON), which allows for few-step sampling with a likelihood accurate enough for importance sampling applications.<n>We show FALCON outperforms state-of-the-art normalizing flow models for molecular Boltzmann sampling and is two orders of magnitude faster than the equivalently performing CNF model.
  arXiv  Detail & Related papers  (2025-12-10T18:47:25Z)
• Diabatic quantum annealing for training energy-based generative models [0.19116784879310023]
  Energy-based generative models, such as restricted Boltzmann machines (RBMs), require unbiased Boltzmann samples for effective training.<n>We address this bottleneck by applying the analytic relation between annealing schedules and effective inverse temperature.<n>By implementing this prescription on a quantum annealer, we obtain temperature-controlled Boltzmann samples that enable RBM training with faster convergence and lower validation error.
  arXiv  Detail & Related papers  (2025-09-11T11:47:33Z)
• Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities [93.13866975467549]
  We propose Progressive Inference-Time Annealing (PITA) to learn diffusion-based samplers.<n>PITA combines two complementary techniques: Annealing of the Boltzmann distribution and Diffusion smoothing.<n>It enables equilibrium sampling of N-body particle systems, Alanine Dipeptide, and tripeptides in Cartesian coordinates.
  arXiv  Detail & Related papers  (2025-06-19T17:14:22Z)
• Scalable Equilibrium Sampling with Sequential Boltzmann Generators [60.00515282300297]
  We extend the Boltzmann generator framework with two key contributions.<n>The first is a highly efficient Transformer-based normalizing flow operating directly on all-atom Cartesian coordinates.<n>In particular, we perform inference-time scaling of flow samples using a continuous-time variant of sequential Monte Carlo.
  arXiv  Detail & Related papers  (2025-02-25T18:59:13Z)
• 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)
• Boltzmann sampling with quantum annealers via fast Stein correction [1.37736442859694]
  A fast and approximate method is developed to compute the sample weights, and used to correct the samples generated by D-Wave quantum annealers. In benchmarking problems, it is observed that the residual error of thermal average calculations is reduced significantly.
  arXiv  Detail & Related papers  (2023-09-08T04:47:10Z)
• 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)
• 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)
• Exploring the neighborhood of 1-layer QAOA with Instantaneous Quantum Polynomial circuits [0.0]
  We embed 1-layer QAOA circuits into the larger class of parameterized Instantaneous Quantum Polynomial circuits. The use of analytic expressions to find optimal parameters makes our protocol robust against barren plateaus and hardware noise. Our protocol outperforms 1-layer QAOA on the recently released Quantinuum H2 trapped-ion quantum hardware and emulator.
  arXiv  Detail & Related papers  (2022-10-11T15:16:44Z)
• Probing finite-temperature observables in quantum simulators of spin systems with short-time dynamics [62.997667081978825]
  We show how finite-temperature observables can be obtained with an algorithm motivated from the Jarzynski equality. We show that a finite temperature phase transition in the long-range transverse field Ising model can be characterized in trapped ion quantum simulators.
  arXiv  Detail & Related papers  (2022-06-03T18:00:02Z)

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.