QRSOR - QuantumRoute Smart Optimized Routing

QRSOR

💡 Click anywhere on map to set Origin (Point A)

New Route Request

Interactive Map Active

Origin (Point A)

Destination (Point B)

Presets:

Vehicle Fleet

Ready

Delivery Van 04 - John Doe Active, 85%

›

Truck 07 - Mike Smith En Route, 92%

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Delivery Van 11 - Sarah Chen Idle

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Traffic Congestion

Peak Traffic: 4-6 PM

Interactive Multi-Modal Route Planner

Compare Quantum-behaved Particle Swarm Optimization (QRSOR) with Classical Metaheuristics and Dijkstra/A*

Click "Run Comparative Solver" to benchmark QRSOR against Standard Classical PSO and Static Dijkstra routing.

Dynamic Environmental Edge Weighting

C(e_ij, t) = (d_ij / v_free) * Φ(W_ij(t)) * Ψ(ρ_ij(t)) + Ω_risk Where: • Φ(W) = 1.0 + 0.65 * (p / 100)^1.8 [Weather friction] • Ψ(ρ) = 1.0 + 0.15 * (V / K)^4.0 [BPR congestion penalty] • Ω_risk = ∞ if water_depth ≥ 150 mm [Flood barrier pruning]

Schrödinger Delta-Potential Well Equation

x_ij(t+1) = p_ij(t) ± β * |mbest_j(t) - x_ij(t)| * ln(1 / u) • Local Attractor: p_ij = φ * pbest_ij + (1-φ) * gbest_j • Center-of-Mass: mbest = (1/M) * Σ pbest_i • Quantum Tunneling: ln(1/u) heavy-tail avoids local traps • SPV Rule: Decodes continuous hyperspace into visiting nodes

Fleet Telematics & Dispatch Control

Real-time vehicle battery telemetry, payload capacity, and synchromodal transit integration

4 Vehicles Online

Vehicle Name	Model	Driver	Status	Battery / Fuel	Payload	Current Location	Action

Empirical Benchmarking & Performance Metrics

Verification against Solomon TD-CVRP benchmark instances and SUMO traffic simulations (SIH26137 Section 5)

Metric Category	Baseline Approach	QRSOR Target Result	Performance Threshold

35.5%

Fewer Iterations

Compared to standard Newtonian PSO on Solomon benchmarks

< 45 ms

Execution Latency

Sub-second recalculation across 100-node network

21.4%

CO₂ Emissions Cut

Validated via HBEFA environmental standards

Quantum Hyperparameter & Environmental Configuration

Fine-tune delta-potential well contraction parameters, weather friction factor, and BPR congestion exponents

Swarm Size (M particles) 30 particles

Number of quantum-behaved search agents exploring the route permutation landscape.

Initial Contraction-Expansion (β_max) 1.0

Initial global exploration radius for Schrödinger delta-potential well updates.

Final Contraction-Expansion (β_min) 0.4

Exploitation parameter controlling fine-tuning convergence around global minimum.

Weather Friction Multiplier (α_1) 0.65

Calibration parameter for road deceleration caused by precipitation and hydroplaning.

Critical Inundation Barrier (h_crit) 150 mm

Water depth threshold beyond which roads are pruned from graph search (Ω_risk = ∞).

QRSOR Architecture & Technical Documentation

Problem Statement ID: SIH26137 • Organization: Egreen Quanta

Quantum-Inspired Paradigm Shift vs NISQ Bottlenecks

While physical NISQ quantum hardware (IBM Quantum, D-Wave) suffers from limited qubit counts, high decoherence noise, and readout latencies exceeding 3–10 seconds, QRSOR embeds quantum mechanical concepts (delta-potential well wave-functions, center-of-mass attractors, and heavy-tailed quantum tunneling) into classical CPU/GPU runtimes. This enables sub-second real-time replanning for urban transit networks under severe weather shocks and traffic gridlocks.