Coupling-Grouped XY-QAOA for Joint Anomaly-Feature Selection

• URL: http://arxiv.org/abs/2606.13244v1
• Date: Thu, 11 Jun 2026 11:56:54 GMT
• Title: Coupling-Grouped XY-QAOA for Joint Anomaly-Feature Selection
• Abstract summary: Coupling-Grouped XY-QAOA is a constraint-preserving grouped-angle variant for a constrained-optimization problem.<n>A hardware-aware implementation reduces circuit depth by 45.9%-61.3% and two-qubit gates by 2.6%-5.2%.<n>Across 36-64 qubits, fixed-angle runs yield lower-energy feasible samples than matched random-feasible sampling.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Selecting anomalous samples and explanatory features under fixed budgets defines a coupled constrained-optimization problem. Sequential feature-first selection ranks features before choosing samples, which can overlook features whose utility depends on which samples are selected, especially when scores are calibrated from reference data that may be limited, noisy, or drifting. We instead formulate the task as joint sample-feature selection under the same fixed counts. In the analyzed formal model, calibration-error sensitivity grows linearly with the number of samples for feature-first ordering but stays constant for joint selection. We introduce Coupling-Grouped XY-QAOA, a constraint-preserving grouped-angle variant for the resulting optimization problem. On matched sparse IBM Heron R3 benchmarks, a hardware-aware implementation reduces circuit depth by 45.9%-61.3% and two-qubit gates by 2.6%-5.2% relative to Qiskit optimization level 3 on the CZ-basis target. It enables, to our knowledge, the largest reported width-depth configurations for constraint-preserving bipartite-selection QAOA hardware executions with feasible-sector retention: 64 qubits at p=2 and 36 qubits at p=3. The 20-qubit p=5 runs retain 63% valid samples. Across 36-64 qubits, fixed-angle runs yield lower-energy feasible samples than matched random-feasible sampling. Warm starts reduce the gap to strict-feasible classical references by 57.5%-80.5%, and near-budget repair matches the sparse classical reference at 36 qubits. Benchmarks show gains in balanced fixed-budget regimes, and noiseless simulations show that problem-structured angle grouping improves over same-depth XY-QAOA and matched-parameter, type-preserving randomization controls. Overall, the results support calibrated joint selection and hardware-realizable constrained-mixer execution in the tested regimes.

Related papers

• Quantum-informed surrogate sampling for combinatorial optimization [0.0]
  We introduce Quantum-Informed Surrogate Sampling (QISS), a post-processing framework that generates candidate solutions to optimization problems from low-weight correlations of shallow quantum circuits.<n>We show that only $O(N)$ low-order correlators from shallow circuits suffice to produce competitive solutions that surpass vanilla QAOA.
  arXiv  Detail & Related papers  (2026-07-24T14:58:57Z)
• Formulation-Level Auto-Tuning for QUBO-Based Machine Learning: A Case Study Across Multiple Quantum-Inspired Annealers [0.0]
  This paper presents an Optuna-based formulation-level auto-tuning framework for support vector machines (SVMs)<n> continuous dual variables are discretized and converted into a quadratic unconstrained binary optimization (QUBO) model.<n>Experiments on linear and nonlinear classification tasks with 0-20% label noise show mean gains over grid search of approximately 0.8 and 2.1 percentage points, respectively.
  arXiv  Detail & Related papers  (2026-07-21T06:56:43Z)
• RIMS: Preference Optimization via Smoothed Multi-pair Aggregation for Small-Scale LLM Retrieval-Augmented Generation [19.76481955873559]
  Small-scale language models (SLMs) are attractive for retrieval-augmented generation (RAG) in resource-constrained settings.<n>We propose RIMS, a three-stage preference optimization framework.<n>We show that our approach outperforms state-of-the-art baselines across multiple SLM backbones.
  arXiv  Detail & Related papers  (2026-07-17T18:24:19Z)
• A Reproducible Software Workflow for Unanchored Approximate MUB Optimization: A Case Study in Dimension Six [5.114953719334754]
  The workflow is designed for portable execution across CPU, Apple MPS, and HPC backends.<n>We optimize unanchored configurations across 100 random seeds for basis counts n = 3, 4, 5, 6 in complex128 and complex64 arithmetic.<n>We embed the representative d = 6, n = 4 transition unitaries into three-qubit 8x8 unitaries and execute the resulting circuits on the 156-qubit Heron processor ibm-marrakesh.
  arXiv  Detail & Related papers  (2026-07-12T07:24:25Z)
• COMET: Combinatorial Optimization for Multiplex Editing Targets Via Constraint-Preserving QAOA [0.010923877073891442]
  Multiplex CRISPR-Cas9 gene editing requires selecting one guide RNA per target gene subject to cross-gene interactions.<n>The one-hot per-gene constraint is conventionally enforced by adding penalty terms to the cost Hamiltonian.<n>An alternative is to enforce the constraint structurally via the XY-mixer, which preserves feasibility by construction.
  arXiv  Detail & Related papers  (2026-07-02T08:37:59Z)
• Efficient Visual Pointing for Embodied AI:Agent-Driven Data Synthesis, Cross-Block Attention, and Iterative Correction [55.11480729304395]
  We describe our PointArena 2026 solution, which achieves 77.2% overall accuracy and ranks second on the benchmark.<n>The ap proach targets three failure modes. First, agent-driven syn thesis builds large semantic and anchor-relative candidate pools.<n>Second, a determinis tic steerable-data pipeline creates a verified 10,000-sample main set, plus reserve samples, using masks, templates, and path verification.
  arXiv  Detail & Related papers  (2026-06-29T06:39:03Z)
• Towards Robust Training in NNGPT AutoML Pipeline: A Loss-Optimizer Pairing Selection Study [48.83701310501069]
  This paper investigates whether a single recipe is sufficient for heterogeneous architecture pools, or whether the optimal pairing varies across structurally diverse models.<n>We conduct a systematic empirical study of all $3 times 6 = 18$ combinations of six classifiers paired with three loss functions: Cross-Entropy (CEL), Negative Log-Likelihood (NLL), and the recently introduced genetically evolved NGL loss across the base models presented in LEMUR heterogeneous architecture pool on six image classification datasets.<n>Our results confirm that no single pairing is universally optimal. Cross-Entropy with Adam or AdamW is the most robust choice across
  arXiv  Detail & Related papers  (2026-06-18T20:51:42Z)
• Off-the-Shelf LLMs as Process Scorers: Training-Free Alternative to PRMs for Mathematical Reasoning [51.88950852117154]
  Chunk-Level Guided Generation is a training-free alternative that uses an off-the-shelf large language model as a process scorer.<n>We show that scoring variable-length reasoning steps with large-model likelihoods is unreliable due to a systematic length bias.<n>Chunk-Level Guided Generation produces substantially shorter reasoning traces than PRM guided search.
  arXiv  Detail & Related papers  (2026-06-01T04:43:36Z)
• optimize_anything: A Universal API for Optimizing any Text Parameter [98.42497715725356]
  We show a single AI-based optimization system-supporting single-task search, multi-task search with cross-problem transfer, and generalization to unseen inputs.<n>We show for the first time that text optimization with LLM-based search is a general-purpose problem-solving paradigm.
  arXiv  Detail & Related papers  (2026-05-19T10:18:12Z)
• Evaluating Small Language Models for Front-Door Routing: A Harmonized Benchmark and Synthetic-Traffic Experiment [0.05586191108738562]
  Small language models (SLMs) have sufficient reasoning capability for sub-second, zero-marginal-cost, self-hosted task classification.<n>Study 1 is a harmonized offline benchmark of Phi-3.5-mini, Qwen2.5-1.5B, and Qwen-2.5-3B on identical Azure T4 hardware, serving stack, quantization, and a fixed 60-case corpus.<n>Study 2 is a pre-registered four-arm randomized experiment under synthetic traffic with an effective sample size of 60 unique cases per arm.
  arXiv  Detail & Related papers  (2026-03-26T15:57:46Z)
• Diffusion-MPC in Discrete Domains: Feasibility Constraints, Horizon Effects, and Critic Alignment: Case study with Tetris [0.0]
  We study diffusion-based model predictive control (Diffusion-MPC) in discrete domains using Tetris as a case study.<n>Our planner samples candidate placement sequences with a MaskGIT-style discrete denoiser and selects actions via reranking.<n>We analyze three key factors: (1) feasibility-constrained sampling via logit masking over valid placements, (2) reranking strategies using a score, a pretrained DQN critic, and a hybrid combination, and (3) compute scaling in candidate count and planning horizon.
  arXiv  Detail & Related papers  (2026-03-02T19:35:38Z)
• BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models [56.504879072674015]
  We propose Bit-Plane Decomposition Quantization (BPDQ), which constructs a variable quantization grid via bit-planes and scalar coefficients.<n>BPDQ enables serving Qwen2.5-72B on a single GTX 3090 with 83.85% GSM8K accuracy (vs. 90.83% at 16-bit)
  arXiv  Detail & Related papers  (2026-02-04T02:54:37Z)
• Ensemble Threshold Calibration for Stable Sensitivity Control [0.0]
  We present an end-to-end framework that achieves exact recall with sub-percent variance over tens of millions of geometry pairs.<n>Our approach consistently hits a recall target within a small error, decreases redundant verifications relative to other calibrations, and runs end-to-end on a single TPU v3 core.
  arXiv  Detail & Related papers  (2025-10-02T15:22:28Z)
• Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty [49.19257648205146]
  We propose an unsupervised conformal inference framework for generation.<n>Our gates achieve close-to-nominal coverage and provide tighter, more stable thresholds than split UCP.<n>The result is a label-free, API-compatible gate for test-time filtering.
  arXiv  Detail & Related papers  (2025-09-26T23:40:47Z)
• Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification [1.4120905648647635]
  We formulate malware classification as a Markov Decision Process with episodic feature acquisition.<n>We propose a Dueling Double Deep Q-Network (D3QN) framework for adaptive sequential feature selection.<n>We evaluate our approach on Microsoft Big2015 (9-class, 1,795 features) and BODMAS (binary, 2,381 features) datasets.
  arXiv  Detail & Related papers  (2025-07-06T12:37:50Z)
• A Gradient Meta-Learning Joint Optimization for Beamforming and Antenna Position in Pinching-Antenna Systems [63.213207442368294]
  We consider a novel optimization design for multi-waveguide pinching-antenna systems.<n>The proposed GML-JO algorithm is robust to different choices and better performance compared with the existing optimization methods.
  arXiv  Detail & Related papers  (2025-06-14T17:35:27Z)
• Reverse Preference Optimization for Complex Instruction Following [61.39734201711077]
  We propose a simple yet effective method called Reverse Preference Optimization (RPO)<n>It mitigates noise in preference pairs by dynamically reversing the constraints within the instruction to ensure the chosen response is perfect.<n>RPO scales effectively across model sizes, with the 70B RPO model surpassing GPT-4o.
  arXiv  Detail & Related papers  (2025-05-28T09:44:27Z)

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.