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-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
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.
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.