Neural and Tensor Networks in the Study of Quantum Annealing Processors

• URL: http://arxiv.org/abs/2604.26534v2
• Date: Thu, 30 Apr 2026 07:53:36 GMT
• Title: Neural and Tensor Networks in the Study of Quantum Annealing Processors
• Abstract summary: dissertation develops a benchmarking framework for D-Wave quantum annealers.<n>It combines strong classical baselines, sampling and diversity metrics, and thermodynamic cost.<n>Overall, it argues for quantum-annealing benchmarks that jointly measure algorithmic performance and physical expenditure.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum annealing targets low-energy solutions of Ising/QUBO problems, but reliable assessment requires more than best-energy comparisons. This dissertation develops a benchmarking framework for D-Wave quantum annealers that combines strong classical baselines, sampling and diversity metrics, and thermodynamic cost. Its first contribution, SpinGlassPEPS$.$jl, is a topology-aware tensor-network heuristic for optimization and sampling on Pegasus/Zephyr-like graphs. It maps Ising instances to local Potts clusters, represents the partition function with PEPS, and performs branch-and-bound search in probability space. Benchmarks show that it is a physically interpretable reference solver, though approximate contractions limit its competitiveness on the largest instances. The second contribution treats quantum annealers as effective thermal machines, relating success probability and solution quality to dissipation, entropy production, and effective temperature. Carefully placed pauses can improve performance while reducing thermodynamic cost, although longitudinal fields may become harmful in paused schedules. The thesis also introduces reinforcement-learning post-processing to improve returned samples and exact small-system simulations to probe annealing dynamics. Overall, it argues for quantum-annealing benchmarks that jointly measure algorithmic performance and physical expenditure.

Related papers

• Quantum annealers as programmable thermal machines [0.0]
  We characterize the D-Wave quantum annealer as a closed thermodynamic cycle.<n>From initial and final Ising energies and an effective temperature fitted to the output distribution, we obtain lower bounds on entropy production, environment energy exchange, work, and power.
  arXiv  Detail & Related papers  (2026-08-05T07:57:20Z)
• 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)
• Scalable Hardware Maturity Probe for Quantum Accelerators via Harmonic Analysis of QAOA [0.2578242050187029]
  We present a hardware-maturity probe that quantifies a device's reliability.<n>We derive closed-form upper bounds on the number of stationary points in the p=1OA cost landscape for broad classes of verifiable-optimization problems.
  arXiv  Detail & Related papers  (2025-09-14T21:48:34Z)
• QB Ground State Energy Estimation Benchmark [30.045562070102537]
  Ground State Energy Estimation is a central problem in quantum chemistry and condensed matter physics.<n>This work introduces a structured benchmarking framework for evaluating the performance of both classical and quantum solvers.
  arXiv  Detail & Related papers  (2025-08-14T17:45:28Z)
• Low Cost Bayesian Experimental Design for Quantum Frequency Estimation with Decoherence [45.74830585715129]
  We introduce WES: a Window Expansion Strategy for low cost adaptive Bayesian experimental design.<n>We employ empirical cost-reduction techniques to keep the optimization overhead low, curb scaling problems, and enable high degrees of parallelism.<n> Numerical simulations show that WES delivers the most reliable performance and fastest learning rate, saturating the Heisenberg limit.
  arXiv  Detail & Related papers  (2025-08-09T23:41:58Z)
• Hybrid quantum-classical algorithm for near-optimal planning in POMDPs [39.682133213072554]
  Reinforcement learning (RL) provides a principled framework for decision-making in partially observable environments.<n>Recent advances demonstrate that inference on sparse Bayesian networks can be accelerated using quantum rejection sampling combined with amplitude amplification.<n>We introduce Quantum Bayesian Reinforcement Learning (QBRL), a hybrid quantum-classical look-ahead algorithm for model-based RL in partially observable environments.
  arXiv  Detail & Related papers  (2025-07-24T17:42:30Z)
• Calibration of Quantum Devices via Robust Statistical Methods [45.464983015777314]
  We numerically analyze advanced statistical methods for Bayesian inference against the state-of-the-art in quantum parameter learning.<n>We show advantages of these approaches over existing ones, namely under multi-modality and high dimensionality.<n>Our findings have applications in challenging quantumcharacterization tasks namely learning the dynamics of open quantum systems.
  arXiv  Detail & Related papers  (2025-07-09T15:22:17Z)
• QAMA: Scalable Quantum Annealing Multi-Head Attention Operator for Deep Learning [48.12231190677108]
  Quantum Annealing Multi-Head Attention (QAMA) is proposed, a novel drop-in operator that reformulates attention as an energy-based Hamiltonian optimization problem.<n>In this framework, token interactions are encoded into binary quadratic terms, and quantum annealing is employed to search for low-energy configurations.<n> Empirically, evaluation on both natural language and vision benchmarks shows that, across tasks, accuracy deviates by at most 2.7 points from standard multi-head attention.
  arXiv  Detail & Related papers  (2025-04-15T11:29:09Z)
• A Practical Framework for Assessing the Performance of Observable Estimation in Quantum Simulation [0.0]
  We introduce a framework for evaluating the performance of quantum simulation algorithms.<n>Our framework provides end-to-end demonstrations of algorithmic optimizations.<n>We show a 27.1% error reduction through Pauli grouping methods, with an additional 37.6% improvement from the optimized shot distribution strategy.
  arXiv  Detail & Related papers  (2025-04-14T02:23:01Z)
• 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)
• Load Balancing For High Performance Computing Using Quantum Annealing [0.6144680854063939]
  We investigate the application of quantum annealing to load balance two paradigmatic algorithms in high performance computing. In a grid based context, quantum annealing is found to outperform classical methods such as the round robin protocol but lacks a decisive advantage over more advanced methods. This signals a noteworthy advancement in solution quality which can have a large impact on effective CPU usage.
  arXiv  Detail & Related papers  (2024-03-08T12:58:12Z)
• Quantum Thermal State Preparation [39.91303506884272]
  We introduce simple continuous-time quantum Gibbs samplers for simulating quantum master equations. We construct the first provably accurate and efficient algorithm for preparing certain purified Gibbs states. Our algorithms' costs have a provable dependence on temperature, accuracy, and the mixing time.
  arXiv  Detail & Related papers  (2023-03-31T17:29:56Z)
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage [43.3054117987806]
  We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits. We show this method significantly improves the trainability and performance of PQCs on a variety of problems. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
  arXiv  Detail & Related papers  (2022-08-29T15:24:03Z)
• Assessment of image generation by quantum annealer [0.0]
  A quantum annealer may also serve as a fast sampler for the Ising spin-glass problem. In this study, we focused on the performance of a quantum annealer as a generative model.
  arXiv  Detail & Related papers  (2021-03-15T13:24:05Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)

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.