Quantum-powered route optimization with AI traffic prediction for faster, more cost-efficient deliveries.
The Problem
Traditional routing systems are inefficient, leading to:
- 30% longer delivery times
- 25% higher fuel costs
- Poor customer satisfaction
- Manual planning bottlenecks
- Inability to adapt to real-time changes
The Solution
Q-Router™ combines quantum computing with AI to deliver:
- Exponentially faster optimization
- Real-time traffic prediction
- Dynamic route adjustments
- Multi-constraint optimization
- Seamless API integration
Quantum-Powered Features
Advanced capabilities that set Q-Router™ apart from traditional routing solutions
Quantum-Assisted Optimization
Leverage quantum computing principles for exponentially faster route calculations
Real-time Analytics
Get instant insights into your routes and operations
Enterprise Integration
Seamlessly integrate with your existing systems
No Savings, No Fee
Pay Only for Results
Q-Router™'s performance-based pricing means you only pay when we deliver real, measurable savings to your bottom line.
Performance-Based Pricing
Pay only 10% of the actual cost savings we generate for your business. No savings = No fee.
Quantum + AI Optimization
Our advanced algorithms combine quantum computing and AI traffic prediction to find the most efficient routes.
Measurable Results
Clear, transparent reporting shows exactly how much you're saving with Q-Router™'s optimization.
🚚 Case Study: See Your Savings with Q-Router™
Enter your fleet size and daily order volume to estimate how much Q-Router™ can save you in mileage, fuel costs, and delivery efficiency.
Interactive Route Optimization Demo
Fuel Cost Reduction
Lower mileage directly translates into reduced fuel spend and carbon footprint.
Scalable Optimization
Handles 100s–1000s of orders in a single batch run with quantum efficiency.
Operational Reliability
Balanced assignments mean faster deliveries and fewer SLA breaches.
Quantum Optimization Metrics
Quantum Cost Function
H(s) = A(s)H0 + B(s)H1
H0 = −∑iσix
H1 = ∑ihiσiz + ∑i<jJijσizσjz
Where: H(s) = Total Hamiltonian
H0 = Driver Hamiltonian
H1 = Problem Hamiltonian
A(s), B(s) = Annealing schedule functions
H0 = Driver Hamiltonian
H1 = Problem Hamiltonian
A(s), B(s) = Annealing schedule functions
Optimization Problem
minx∈{0,1} ∑i,jcijxij+ λ1∑j(∑ixij − 1)2+ λ2∑i(∑jxij − 1)2+ λ3(capacity penalties)
Where: cij = Travel cost
xij = Binary decision variable
λk = Penalty strengths
xij = Binary decision variable
λk = Penalty strengths
Annealing Schedule
A(0) ≫ B(0), A(1) ≈ 0, B(1) ≫ 0
s(t) = t/τ, t ∈ [0, τ]
Where: s = Normalized time
τ = Total annealing time
τ = Total annealing time
Performance
Q = (Cc − Cq)/Cc × 100%
Tq ≈ O(√N/M) (illustrative)
Where: Q = Relative improvement
Cc/q = Classical/Quantum cost
N = Problem size, M = Qubits
Cc/q = Classical/Quantum cost
N = Problem size, M = Qubits
Classical computers struggle — quantum annealing solves it with ease.