Qubit-to-Motion: Traceable Action Provenance in Hybrid QRL for Robotic Bin-Packing Sequencing
Overview
This research introduces Qubit-to-Motion, an evidence-centered framework that links quantum circuit outputs to constrained robotic actions in bin-packing tasks. The system uses four Pauli-Z readouts as input to actor and critic networks, enforces admissible object selection via a support mask, and records each decision with step indices and SHA-256 fingerprints tied to deterministic waypoints and MuJoCo contact replays. Evaluated across five 30-object scene variants, all 150 placements met support, containment, and settling criteria. A fixed-state zero-readout intervention altered 90.0% of decisions, confirming dependence on the joint circuit representation. Mean macro travel was 34.537 m, inference took 8.60 ms per route, and replay achieved a mean final position error of 8.764 μm with a maximum interpenetration of 0.356 mm. The implementation is available via an anonymous repository.
Generated from attributed reports · Updated 9 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Environment: simulation
“d sequencing and simulated robotic placement. Four Pauli-$Z$ readouts provide the sole learned input to the actor and critic, while a support mask enforces admissible object selection. Each decision is recorded and linked via step indices and SHA-256 fingerprints to a sealed route, deterministic Cartesian and inverse-kinematics waypoints, prescribed joint motion, and MuJoCo contact replay. A frozen checkpoint is eval”
Exact source · revision 1Source owner not reported
Reported quantity · mean macro travel distance: 34.537 other · Basis not reported
“uated across five deterministic 30-object scene variants. All 150 placements satisfy support, containment, and settling criteria. A fixed-state zero-readout intervention changes $90.0\pm0.0\%$ of decisions, demonstrating dependence on the joint circuit representation. Mean macro travel is $34.537\pm0.206$~m, inference requires $8.60\pm1.27$~ms per route, and replay achieves $8.764\pm0.220~\mu$m mean final position er”
Exact source · revision 1Source owner not reported
Reported quantity · inference time per route: 8.6 other · Basis not reported
“uated across five deterministic 30-object scene variants. All 150 placements satisfy support, containment, and settling criteria. A fixed-state zero-readout intervention changes $90.0\pm0.0\%$ of decisions, demonstrating dependence on the joint circuit representation. Mean macro travel is $34.537\pm0.206$~m, inference requires $8.60\pm1.27$~ms per route, and replay achieves $8.764\pm0.220~\mu$m mean final position er”
Exact source · revision 1Source owner not reported
Reported quantity · mean final position error: 8.764 other · Basis not reported
“uated across five deterministic 30-object scene variants. All 150 placements satisfy support, containment, and settling criteria. A fixed-state zero-readout intervention changes $90.0\pm0.0\%$ of decisions, demonstrating dependence on the joint circuit representation. Mean macro travel is $34.537\pm0.206$~m, inference requires $8.60\pm1.27$~ms per route, and replay achieves $8.764\pm0.220~\mu$m mean final position er”
Exact source · revision 1Source owner not reported
Reported quantity · number of scene variants: 5 other · Basis not reported
“uated across five deterministic 30-object scene variants. All 150 placements satisfy support, containment, and settling criteria. A fixed-state zero-readout intervention changes $90.0\pm0.0\%$ of decisions, demonstrating dependence on the joint circuit representation. Mean macro travel is $34.537\pm0.206$~m, inference requires $8.60\pm1.27$~ms per route, and replay achieves $8.764\pm0.220~\mu$m mean final position er”
Exact source · revision 1Source owner not reported
Reported quantity · number of placements: 150 other · Basis not reported
“uated across five deterministic 30-object scene variants. All 150 placements satisfy support, containment, and settling criteria. A fixed-state zero-readout intervention changes $90.0\pm0.0\%$ of decisions, demonstrating dependence on the joint circuit representation. Mean macro travel is $34.537\pm0.206$~m, inference requires $8.60\pm1.27$~ms per route, and replay achieves $8.764\pm0.220~\mu$m mean final position er”
Exact source · revision 1Source owner not reported
Reported quantity · number of Pauli-Z readouts: 4 other · Basis not reported
“d sequencing and simulated robotic placement. Four Pauli-$Z$ readouts provide the sole learned input to the actor and critic, while a support mask enforces admissible object selection. Each decision is recorded and linked via step indices and SHA-256 fingerprints to a sealed route, deterministic Cartesian and inverse-kinematics waypoints, prescribed joint motion, and MuJoCo contact replay. A frozen checkpoint is eval”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalQubit-to-Motion: Traceable Action Provenance in Hybrid QRL for Robotic Bin-Packing Sequencing
This research introduces Qubit-to-Motion, an evidence-centered framework that links quantum circuit outputs to constrained robotic actions in bin-packing tasks. The system uses four Pauli-Z readouts as input to actor and critic networks, enforces admissible object selection via a support mask, and records each decision with step indices and SHA-256 fingerprints tied to deterministic waypoints and MuJoCo contact replays. Evaluated across five 30-object scene variants, all 150 placements met support, containment, and settling criteria. A fixed-state zero-readout intervention altered 90.0% of decisions, confirming dependence on the joint circuit representation. Mean macro travel was 34.537 m, inference took 8.60 ms per route, and replay achieved a mean final position error of 8.764 μm with a maximum interpenetration of 0.356 mm. The implementation is available via an anonymous repository.
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