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ARXIV:2604.26435 · COMPUTER VISION · SUBMITTED 30 APR · 15:13 UTC · FRESHNESS STALE
ARXIV:2604.26435COMPUTER VISIONSUBMITTED 30 APR · 15:13 UTCFRESHNESS STALEGarvit Kumar Mittal · Sahil Tomar · Sandeep Kumar · arXiv
QYOLO is a lightweight object detection framework that uses quantum-inspired channel mixing to significantly reduce parameters and GFLOPs with minimal accuracy loss.
Opportunity summary
Pain QYOLO is a lightweight object detection framework that uses quantum-inspired channel mixing to significantly reduce parameters and GFLOPs with minimal accuracy loss.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
QYOLO is a lightweight object detection framework that uses quantum-inspired channel mixing to significantly reduce parameters and GFLOPs with minimal accuracy loss. A primary source of computational overhead in these models lies in the…
The rapid advancement of object detection architectures has positioned single stage detectors as the dominant solution for real-time visual perception. A primary source of computational overhead in these models lies in the deep backbone…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. This work introduces QYOLO, a quantum-inspired channel mixing framework that achieves genuine architectural compression by replacing the two deepest backbone C2f modules at P4/16…
Computer Vision moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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QYOLO is a lightweight object detection framework that uses quantum-inspired channel mixing to significantly reduce parameters and GFLOPs with minimal accuracy loss.
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10.48550/arXiv.2604.26435QYOLO is a lightweight object detection framework that uses quantum-inspired channel mixing to significantly reduce parameters and GFLOPs with minimal accuracy loss.
Abstract
The rapid advancement of object detection architectures has positioned single stage detectors as the dominant solution for real-time visual perception. A primary source of computational overhead in these models lies in the deep backbone stages, where C2f bottleneck modules at high stride levels accumulate a disproportionate share of parameters due to quadratic scaling with channel width. This work introduces QYOLO, a quantum-inspired channel mixing framework that achieves genuine architectural compression by replacing the two deepest backbone C2f modules at P4/16 (512 channels) and P5/32 (1024 channels) with a compact QMixBlock. The proposed block performs global channel recalibration through a sinusoidal mixing mechanism with shared learnable parameters across both backbone stages, enforcing consistent channel importance without requiring independent per-stage parameter sets. The neck and detection head remain fully classical and unchanged. Evaluation on the VisDrone2019 benchmark demonstrates that QYOLOv8n achieves a 20.2% reduction in parameter count (3.01M to 2.40M) and 12.3% GFLOPs reduction with only 0.4 pp mAP@50 degradation. QYOLOv8s achieves 21.8% reduction with 0.1 pp degradation. When combined with knowledge distillation, full accuracy parity is recovered at no cost to compression. An expanded backbone plus neck variant achieved 38 to 41% reduction at the cost of greater accuracy degradation, motivating the backbone-only final design.
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PROBLEM
QYOLO is a lightweight object detection framework that uses quantum-inspired channel mixing to significantly reduce parameters and GFLOPs with minimal accuracy loss. A primary source of computational overhead in these models lies in the deep backbone stages, where C2f bottleneck...
METHOD
The rapid advancement of object detection architectures has positioned single stage detectors as the dominant solution for real-time visual perception. A primary source of computational overhead in these models lies in the deep backbone stages, where C2f bottleneck modules at hi...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. This work introduces QYOLO, a quantum-inspired channel mixing framework that achieves genuine architectural compression by replacing the two deepest backbone C2f modules at P4/16 (512 channels) and P5/32...
WHY NOW
Computer Vision moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 13, "author": "Garvit Kumar Mittal; Sahil Tomar; Sandeep Kumar", "title": "QYOLO: Lightweight Object Detection via Quantum Inspired Shared Channel Mixing"
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QYOLO is a lightweight object detection framework that uses quantum-inspired channel mixing to significantly reduce parameters and GFLOPs with minimal accuracy loss.
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