When is global evolutionary search useful for variational quantum algorithms? A landscape-first study

• URL: http://arxiv.org/abs/2609.14594v1
• Date: Sun, 13 Sep 2026 15:22:20 GMT
• Title: When is global evolutionary search useful for variational quantum algorithms? A landscape-first study
• Abstract summary: Two mechanisms make local unreliable: parameter traps across circuit layers and competition between 2-local and 3-local cost terms.<n> adaptive differential evolution (DE) achieves lower median error than both multistart BFGS and multistart Powell on difficult constructions.<n>Findings motivate whether quantum circuit complexity can be traded for harder classical optimization when robust global search is available.
• Score: 0.06875312133832077
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Variational quantum algorithms recast state preparation as classical nonconvex optimization, but it is often unclear when multistart local search suffices and when population-based global search justifies its evaluation cost. Using a landscape-first design, controlled QAOA experiments identify two mechanisms making local search unreliable: parameter reuse across circuit layers and competition between 2-local and 3-local cost terms. Increasing QAOA depth alone does not replicate this effect. We test these mechanisms across eight unseen spin glasses, fresh $N=10$ and $N=12$ instances, and standard MaxCut, transverse-field Ising, and Heisenberg models. On all eight unseen spin glasses, adaptive differential evolution (DE) achieves lower median error than both multistart BFGS and multistart Powell on the two difficult constructions, whereas the independent-depth control does not. The parameter-reuse effect transfers to MaxCut at both sizes, while standard VQE models remain favorable to local search. A pre-benchmark landscape score combining random-start local outcomes and 1D parameter slices is fixed from confirmation data before evaluating new optimizers. It predicts whether adaptive DE outperforms multistart BFGS by over one spectral-range percentage point on 50 new quantum objectives with 80--86% condition-level accuracy. The clearest indicator of global-search utility is that local search frequently traps in inferior basins, rather than circuit depth or curvature anisotropy. The findings motivate whether quantum circuit complexity can be traded for harder classical optimization when robust global search is available.

Related papers

• DE-2LS: Differential Evolution with Late-Stage local-search for Unconstrained Single-Objective Numerical Optimization [0.8122270502556375]
  We present DE-2LS, a late-stage, local-search-enhanced differential evolution framework built on RDEx for unconstrained single-objective optimization with variable bounds.<n>The proposed method preserves the original RDEx evolutionary search engine and introduces two conservative refinements.<n> Experimental results show that DE-2LS consistently improves the original RDEx in direct head-to-head comparison.
  arXiv  Detail & Related papers  (2026-06-26T06:43:08Z)
• Nonvariational quantum optimisation approaches to pangenome-guided sequence assembly [0.42970700836450487]
  We develop near-term quantum optimisation approaches for genome assembly problems.<n>We use the Iterative-QAOA framework, which combines a fixed linear-ramp QAOA schedule with iterative warm-start bias updates.<n>A custom circuit compilation strategy reduces hardware gate overhead by up to 67% compared with standard tools.
  arXiv  Detail & Related papers  (2026-04-07T17:17:34Z)
• Neural Nonmyopic Bayesian Optimization in Dynamic Cost Settings [73.44599934855067]
  LookaHES is a nonmyopic BO framework designed for dynamic, history-dependent cost environments.<n>LookaHES combines a multi-step variant of $H$-Entropy Search with pathwise sampling and neural policy optimization.<n>Our innovation is the integration of neural policies, including large language models, to effectively navigate structured, domain-specific action spaces.
  arXiv  Detail & Related papers  (2026-01-10T09:49:45Z)
• FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search [50.07268323597872]
  We propose the first one-shot mixed-precision quantization search that eliminates the need for retraining in both integer and low-precision floating point models. With integer models, we increase the accuracy of ResNet-18 on ImageNet by 1.31% and ResNet-50 by 0.90% with equivalent model cost over previous methods. For the first time, we explore a novel mixed-precision floating-point search and improve MobileNetV2 by up to 0.98% compared to prior state-of-the-art FP8 models.
  arXiv  Detail & Related papers  (2023-08-07T04:17:19Z)
• Using Differential Evolution to avoid local minima in Variational Quantum Algorithms [0.0]
  Variational Quantum Algorithms (VQAs) are among the most promising NISQ-era algorithms for harnessing quantum computing. Our goal in this paper is to study alternative optimization methods that can avoid or reduce the effect of local minima and barren plateau problems.
  arXiv  Detail & Related papers  (2023-03-21T20:31:06Z)
• Learning To Dive In Branch And Bound [95.13209326119153]
  We propose L2Dive to learn specific diving structurals with graph neural networks. We train generative models to predict variable assignments and leverage the duality of linear programs to make diving decisions.
  arXiv  Detail & Related papers  (2023-01-24T12:01:45Z)
• Quantum Approximate Optimization Algorithm Parameter Prediction Using a Convolutional Neural Network [0.0]
  We build a convolutional neural network to predict parameters of depth $p+1$ QAOA by the parameters from the depth $p$ QAOA counterpart. An average approximation ratio of 92735$ for Max-Cut over $800$ ErdHos-R'enyi is obtained.
  arXiv  Detail & Related papers  (2022-11-17T13:20:58Z)
• Recursive greedy initialization of the quantum approximate optimization algorithm with guaranteed improvement [1.720510639137902]
  Quantum approximate optimization algorithm (QAOA) is a variational quantum algorithm, where a quantum computer implements a variational ansatz consisting of $p$ layers of alternating unitary operators. We present an analytic construction of $2p+1$ transition states for QAOA with $p+1$ layers that use the local minimum of QAOA with $p$ layers.
  arXiv  Detail & Related papers  (2022-09-02T16:40:21Z)
• On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network [55.56019538079826]
  Bilevel optimization has been applied to a wide variety of machine learning models. Most existing algorithms restrict their single-machine setting so that they are incapable of handling distributed data. We develop novel decentralized bilevel optimization algorithms based on a gradient tracking communication mechanism and two different gradients.
  arXiv  Detail & Related papers  (2022-06-30T05:29:52Z)
• Local policy search with Bayesian optimization [73.0364959221845]
  Reinforcement learning aims to find an optimal policy by interaction with an environment. Policy gradients for local search are often obtained from random perturbations. We develop an algorithm utilizing a probabilistic model of the objective function and its gradient.
  arXiv  Detail & Related papers  (2021-06-22T16:07:02Z)
• APQ: Joint Search for Network Architecture, Pruning and Quantization Policy [49.3037538647714]
  We present APQ for efficient deep learning inference on resource-constrained hardware. Unlike previous methods that separately search the neural architecture, pruning policy, and quantization policy, we optimize them in a joint manner. With the same accuracy, APQ reduces the latency/energy by 2x/1.3x over MobileNetV2+HAQ.
  arXiv  Detail & Related papers  (2020-06-15T16:09:17Z)
• Learning to Accelerate Heuristic Searching for Large-Scale Maximum Weighted b-Matching Problems in Online Advertising [51.97494906131859]
  Bipartite b-matching is fundamental in algorithm design, and has been widely applied into economic markets, labor markets, etc. Existing exact and approximate algorithms usually fail in such settings due to either requiring intolerable running time or too much computation resource. We propose textttNeuSearcher which leverages the knowledge learned from previously instances to solve new problem instances.
  arXiv  Detail & Related papers  (2020-05-09T02:48:23Z)

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.