Quantum neural network-based inverse kinematics of a six-jointed industrial robotic arm
Bibliographic record
Abstract
This research examines the potential of quantum-inspired neural networks (QNNs) for solving the inverse kinematics of robotic arms, focusing on the six-degree-of-freedom ABB IRB140 robot. Traditional inverse kinematics approaches face challenges such as non-unique solutions and computational complexity, especially with increasing degrees of freedom. While artificial neural networks (ANNs) have shown promise, they require further improvements, particularly in terms of quantum computing integration. This study introduces a quantum-inspired activation function to multi-layer perceptron neural networks. We compared ANNs and QNNs with and without singularity avoidance, finding that QNNs significantly outperformed ANNs in mean absolute error (MAE), achieving a 15.60% lower MAE in singularity-free models and a 16.67% lower MAE in singularity-avoidance models. The QNNs demonstrated superior precision, with a position error of 1.64 mm and an orientation error of 0.00179 radians when avoiding singularities. These results highlight the potential of QNNs to enhance the precision, efficiency, and performance of robotic arm manipulation. Quantum computing offers advantages including parallelism, quantum entanglement, and quantum annealing, which contribute to the QNNs’ superior performance. Overall, this study represents a practical contribution to robotics and quantum computing, paving the way for future research into applying quantum principles to neural network models for robotics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".