Continuous-time quantum control across an exponentially small bottleneck in a frustrated Ising ring model

• URL: http://arxiv.org/abs/2606.07168v1
• Date: Fri, 05 Jun 2026 11:38:11 GMT
• Title: Continuous-time quantum control across an exponentially small bottleneck in a frustrated Ising ring model
• Abstract summary: Continuous-time Quantum Annealing (QA) is a strategy for preparing ground state of nontrivial manybody systems.<n>We show how to implement quantum control over the annealing schedule in a frustrated Ising ring.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Continuous-time Quantum Annealing (QA) is a strategy for preparing the ground state of nontrivial many-body systems. In its standard form, the dynamics is generated by a time-dependent interpolation between a simple driving Hamiltonian and the target problem Hamiltonian, usually implemented through a linear schedule. This approach faces the crucial bottleneck of small spectral gaps, which may require exponentially long annealing times to ensure adiabaticity. Here, we show how to implement quantum control over the annealing schedule in a frustrated Ising ring, one of the simplest models exhibiting an exponentially small bottleneck gap. By optimizing smooth continuous-time annealing schedules with a dressed-CRAB approach, and using a digitized representation of the dynamics to efficiently evaluate gradients, we construct protocols that strongly outperform standard fixed schedules. The optimized dynamics bypasses the bottleneck through a strongly nonadiabatic mechanism, leading to efficient ground-state preparation despite the exponentially small minimum gap. In particular, the annealing time required to reach a fixed residual-energy threshold is found to grow linearly with system size rather than exponentially. We further examine a lowest-order variational counter-diabatic correction and find that, once schedule optimization is allowed, it does not lead to any improvement.

Related papers

• Scalable Lindblad Noise Learning via Stochastic Tensor-Network Simulation [48.887627688666974]
  Learning dissipation rates in large-scale open quantum systems is a major obstacle for near-term quantum technologies.<n>Here, we propose a scalable noise-learning framework for Lindblad dissipation rates that combines a simulation method, the Jump Method (TJM)<n>We demonstrate the approach on two noise models in the Ising model, in which independent dissipation rates are learned for each site up to $N_mathrmsite=16$.<n>This combination of scalable numerics and rigorous theoretical guarantees positions TJM-based noise learning as a practical foundation for characterizing dissipation in large quantum devices.
  arXiv  Detail & Related papers  (2026-08-25T15:07:11Z)
• Fast Tensor Network Imaginary Time Evolution by Implicit Stepping on Logarithmic Grids [0.0]
  We show that logarithmic time grids are sufficient to resolve long imaginary time dynamics.<n>We then show that A-stable implicit time-stepping methods for ordinary differential equations allow stable propagation for any time step size.
  arXiv  Detail & Related papers  (2026-06-01T22:25:24Z)
• Dynamical Simulations of Schrödinger's Equation via Rank-Adaptive Tensor Decompositions [0.0]
  We study low-rank tensor methods for the numerical solution of Schrdinger's equation with time-independent and explicitly time-dependent Hamiltonians.<n>We outline the recent application of the "basis update and Galerkin" (Bug) method for tensor trains, and describe the established TDVP and TDVP-2 algorithms.<n> Numerical experiments on representative time-independent and time-dependent Hamiltonian models quantify the tradeoff between accuracy and compression across methods.
  arXiv  Detail & Related papers  (2026-03-14T15:34:24Z)
• Forecasting as Rendering: A 2D Gaussian Splatting Framework for Time Series Forecasting [79.37674445572462]
  Time series forecasting (TSF) remains a challenging problem due to the intricate entanglement of intraperiod-fluctuations and interperiod-trends.<n>Treating reshaped tensors as static images results in a topological mismatch.<n> relying on uniform fixed-size representations allocates modeling capacity inefficiently.<n>TimeGS is a novel framework that fundamentally shifts the forecasting paradigm from regression to 2D generative rendering.
  arXiv  Detail & Related papers  (2026-02-10T14:13:36Z)
• Neural Predictive Control to Coordinate Discrete- and Continuous-Time Models for Time-Series Analysis with Control-Theoretical Improvements [46.19047880604178]
  We recast time-series problems as the continuous ODE-based optimal control problem.<n>Rather than learning dynamics solely from data, we optimize control actions that steer ODE trajectories toward task objectives.<n>We show that, under mild assumptions, this multi-horizon optimization leads to exponential convergence to infinite-horizon solutions.
  arXiv  Detail & Related papers  (2025-08-03T16:41:00Z)
• MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation [74.34220141721231]
  We present MPQ-DMv2, an improved textbfMixed textbfPrecision textbfQuantization framework for extremely low-bit textbfDiffusion textbfModels.
  arXiv  Detail & Related papers  (2025-07-06T08:16:50Z)
• Model Order Reduction for Open Quantum Systems Based on Measurement-adapted Time-coarse Graining [9.507267560064669]
  We present a model order reduction technique to reduce the time complexity of open quantum systems. The method organizes corrections to the lowest-order model which aligns with the RWA Hamiltonian in certain limits. We derive the fourth-order EQME for a challenging problem related to the dynamics of a superconducting qubit.
  arXiv  Detail & Related papers  (2024-10-30T15:26:42Z)
• Convergence of mean-field Langevin dynamics: Time and space discretization, stochastic gradient, and variance reduction [49.66486092259376]
  The mean-field Langevin dynamics (MFLD) is a nonlinear generalization of the Langevin dynamics that incorporates a distribution-dependent drift. Recent works have shown that MFLD globally minimizes an entropy-regularized convex functional in the space of measures. We provide a framework to prove a uniform-in-time propagation of chaos for MFLD that takes into account the errors due to finite-particle approximation, time-discretization, and gradient approximation.
  arXiv  Detail & Related papers  (2023-06-12T16:28:11Z)
• Diabatic Quantum Annealing for the Frustrated Ring Model [0.7046417074932257]
  Adiabatic evolutions can lead to evolution times that scale exponentially with the system size. We show that non-adiabatic evolutions with optimized annealing schedules can bypass this exponential slowdown.
  arXiv  Detail & Related papers  (2022-12-05T22:16:17Z)
• Losing momentum in continuous-time stochastic optimisation [42.617042045455506]
  momentum-based optimisation algorithms have become particularly widespread. In this work, we analyse a continuous-time model for gradient descent with momentum. We also train a convolutional neural network in an image classification problem.
  arXiv  Detail & Related papers  (2022-09-08T10:46:05Z)
• Greedy parameter optimization for diabatic quantum annealing [0.0]
  A shorter processing time is desirable for quantum computation to minimize the effects of noise. We propose a simple procedure to variationally determine a set of parameters in the transverse-field Ising model. We test the idea in the ferromagnetic system with all-to-all couplings and spin-glass problems.
  arXiv  Detail & Related papers  (2021-11-26T01:31:01Z)
• Fast and differentiable simulation of driven quantum systems [58.720142291102135]
  We introduce a semi-analytic method based on the Dyson expansion that allows us to time-evolve driven quantum systems much faster than standard numerical methods. We show results of the optimization of a two-qubit gate using transmon qubits in the circuit QED architecture.
  arXiv  Detail & Related papers  (2020-12-16T21:43:38Z)

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.