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Industry Use Case

Quantum-Optimized Call Routing

Real-world simulation results: how QUBO-based quantum annealing outperforms static BPO routing rules at combinatorial scale.

The Problem

Traditional BPO and telecalling platforms still route calls using rigid round robin or first available logic without truly understanding who the best telecaller is for that customer. Factors like language fluency, product expertise, conversion history, customer intent and real time telecaller availability are often ignored.

When scaled to 100+ telecallers with multiple skill and performance attributes, the routing challenge becomes a massive combinatorial optimization problem with nearly 2ⁿ possible assignment combinations. This is far beyond what classical brute force systems can evaluate in real time.

The outcome is slower resolutions, lower conversions, telecaller overload and lost revenue.

Problem Scale
Variables: n+ per QUBO instance
Search space: 2ⁿ combinations
Model: Simulated Annealing (D-Wave)

Methodology

01
Call Ingress
Incoming call attributes (language, domain, callback preference) are captured and streamed via API.
02
QUBO Formulation
Call-telecaller match scores are encoded into a binary optimization matrix with language, domain, and proficiency weights.
03
Quantum Annealing
D-Wave Simulated Annealing solver explores the 2ⁿ energy landscape to find the global minimum — the optimal telecaller.
04
Routing Decision
Matched telecaller ID is returned via webhook and compared against the classical static-rule result.
>50%
Calls Better Routed
vs classical static rules
n+
QUBO Variables
per routing decision
2ⁿ
Search Space
explored by quantum solver
~3s
Avg Solve Time
per call via D-Wave
CriteriaQuantum (QUBO)Classical (Static Rules)
Routing AlgorithmQUBO Simulated AnnealingRound Robin / FCFS
Language Match Rate60%+ Match15.4% Match
Domain Match RateOptimized per callIgnored in routing
Variables Consideredn+ simultaneously1–2 (queue position)
Search Space Explored2ⁿO(n) linear scan
Handles Combinatorial Explosion✓ Native capability✗ Not feasible
AdaptabilityDynamic per call featuresStatic rule-based
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