Quantum in Manufacturing
Quantum in Manufacturing
Solving the Hardest Problems on Factory Floor
Spindle brings Quantum-hybrid optimization and inference to the hardest problems on the factory floor — scheduling, maintenance, inspection, and rare-event detection — solving at a scale and precision where classical systems start to throttle.
Spindle brings Quantum-hybrid optimization and inference to the hardest problems on the factory floor — scheduling, maintenance, inspection, and rare-event detection — solving at a scale and precision where classical systems start to throttle.
Shop-Floor Mapping & Scheduling
Solve NP-hard job-shop scheduling across high-mix floors where classical solvers stall. Quantum-hybrid search finds globally-aware schedules and re-optimizes live on breakdowns, rework, and rush orders.
Shop-Floor Mapping & Scheduling
Solve NP-hard job-shop scheduling across high-mix floors where classical solvers stall. Quantum-hybrid search finds globally-aware schedules and re-optimizes live on breakdowns, rework, and rush orders.
Shop-Floor Mapping & Scheduling
Solve NP-hard job-shop scheduling across high-mix floors where classical solvers stall. Quantum-hybrid search finds globally-aware schedules and re-optimizes live on breakdowns, rework, and rush orders.
Predictive Maintenance
Predict machine and part-level failure from scarce failure data, with quantum feature maps and physics-informed models delivering calibrated time-to-failure where classical methods run short on signal.
Predictive Maintenance
Predict machine and part-level failure from scarce failure data, with quantum feature maps and physics-informed models delivering calibrated time-to-failure where classical methods run short on signal.
Predictive Maintenance
Predict machine and part-level failure from scarce failure data, with quantum feature maps and physics-informed models delivering calibrated time-to-failure where classical methods run short on signal.
Visual Inspection
Onboard new parts and defect types with fewer labelled images and smaller, cheaper-to-retrain models. Quantum-kernel training augments your existing cameras and CNNs, it doesn’t replace them.
Visual Inspection
Onboard new parts and defect types with fewer labelled images and smaller, cheaper-to-retrain models. Quantum-kernel training augments your existing cameras and CNNs, it doesn’t replace them.
Visual Inspection
Onboard new parts and defect types with fewer labelled images and smaller, cheaper-to-retrain models. Quantum-kernel training augments your existing cameras and CNNs, it doesn’t replace them.
Rare-Event Detection
Estimate one-in-a-million failure probabilities with quadratically fewer evaluations via quantum amplitude estimation, making tail-risk and FEM-based reliability sweeps affordable where classical Monte Carlo becomes prohibitive.
Rare-Event Detection
Estimate one-in-a-million failure probabilities with quadratically fewer evaluations via quantum amplitude estimation, making tail-risk and FEM-based reliability sweeps affordable where classical Monte Carlo becomes prohibitive.
Rare-Event Detection
Estimate one-in-a-million failure probabilities with quadratically fewer evaluations via quantum amplitude estimation, making tail-risk and FEM-based reliability sweeps affordable where classical Monte Carlo becomes prohibitive.
Spindle Quantum-Hybrid vs Classical
Spindle Quantum-Hybrid vs Classical
Vertical
Where classical throttles
Spindle quantum-hybrid
Shop-floor mapping
FJSP is NP-hard; cross-cell high-mix schedule space explodes; greedy heuristics stay locally optimal
QUBO + QAOA search schedules jointly; rolling-horizon re-optimizes live on disruptions
Predictive maintenance
Rare failure labels; subtle high-dimensional sensor correlations; expensive part-level physics
Quantum feature maps + physics-informed / Bayesian models give probabilistic, calibrated time-to-failure
Visual inspection
CNNs need large labelled sets per part; rare-defect class imbalance; costly retrains slow onboarding
Quantum-kernel training → fewer labelled images, smaller retrain; deploys on existing hardware
Rare-event detection
Monte Carlo needs ~1/ε² samples; 1-in-a-million events and FEM sweeps become prohibitive
Amplitude estimation reaches precision in ~1/ε — quadratically fewer evaluations
Vertical
Where classical throttles
Spindle quantum-hybrid
Most popular platforms
(may vary across regions)
Shop-floor mapping
FJSP is NP-hard; cross-cell high-mix schedule space explodes; greedy heuristics stay locally optimal
QUBO + QAOA search schedules jointly; rolling-horizon re-optimizes live on disruptions
Predictive maintenance
Rare failure labels; subtle high-dimensional sensor correlations; expensive part-level physics
Quantum feature maps + physics-informed / Bayesian models give probabilistic, calibrated time-to-failure
Visual inspection
CNNs need large labelled sets per part; rare-defect class imbalance; costly retrains slow onboarding
Quantum-kernel training → fewer labelled images, smaller retrain; deploys on existing hardware
Rare-event detection
Monte Carlo needs ~1/ε² samples; 1-in-a-million events and FEM sweeps become prohibitive
Amplitude estimation reaches precision in ~1/ε — quadratically fewer evaluations
Vertical
Where classical throttles
Spindle quantum-hybrid
Shop-floor mapping
FJSP is NP-hard; cross-cell high-mix schedule space explodes; greedy heuristics stay locally optimal
QUBO + QAOA search schedules jointly; rolling-horizon re-optimizes live on disruptions
Predictive maintenance
Rare failure labels; subtle high-dimensional sensor correlations; expensive part-level physics
Classical
Visual inspection
CNNs need large labelled sets per part; rare-defect class imbalance; costly retrains slow onboarding
Quantum-kernel training → fewer labelled images, smaller retrain; deploys on existing hardware
Rare-event detection
Monte Carlo needs ~1/ε² samples; 1-in-a-million events and FEM sweeps become prohibitive
Amplitude estimation reaches precision in ~1/ε — quadratically fewer evaluations
Vertical
Where classical throttles
Spindle quantum-hybrid
Shop-floor mapping
FJSP is NP-hard; cross-cell high-mix schedule space explodes; greedy heuristics stay locally optimal
QUBO + QAOA search schedules jointly; rolling-horizon re-optimizes live on disruptions
Predictive maintenance
Rare failure labels; subtle high-dimensional sensor correlations; expensive part-level physics
Classical
Visual inspection
CNNs need large labelled sets per part; rare-defect class imbalance; costly retrains slow onboarding
Quantum-kernel training → fewer labelled images, smaller retrain; deploys on existing hardware
Rare-event detection
Monte Carlo needs ~1/ε² samples; 1-in-a-million events and FEM sweeps become prohibitive
Amplitude estimation reaches precision in ~1/ε — quadratically fewer evaluations
Copyright © 2026 Spindle Pte. All rights reserved.
Copyright © 2026 Spindle Pte. All rights reserved.
Copyright © 2026 Spindle Pte. All rights reserved.
Copyright © 2026 Spindle Pte. All rights reserved.