Description
The proposal is to integrate quantum computing capabilities into Adobe Target’s personalization and optimization workflows. Quantum computing enables processing of exponentially complex datasets and can solve optimization problems faster and more accurately than classical AI. By embedding quantum-inspired or quantum-hardware-accelerated models into Adobe Target, businesses can achieve ultra-precise personalization and superior optimization outcomes.
Why is this feature important to you
Next-level personalization: Today’s AI-driven personalization is powerful but constrained by classical computing limits. Quantum computing can unlock new levels of accuracy and context-awareness.
Faster experimentation: Reduce time-to-confidence in A/B and multivariate testing, helping businesses act on insights faster.
Competitive edge for Adobe: Adobe would become the first mover in merging marketing technology with quantum innovation, increasing adoption of Adobe Target.
Revenue impact: Superior personalization drives higher conversions, customer engagement, and long-term loyalty, directly benefiting Adobe’s clients and Adobe itself.
How would you like the feature to work
Integration Layer: Introduce a “Quantum Optimization” mode within Adobe Target, where certain algorithms (e.g., multi-arm bandits, constrained offer orchestration, journey optimization) can run using quantum-inspired solvers.
Opt-in Pilot: Allow businesses to enable quantum acceleration as an experimental strategy for specific activities or recommendations.
KPIs for Measurement:
Accuracy lift in personalization
Reduction in regret/time-to-confidence for tests
Conversion rate improvements
Architecture Suggestion:
Start with quantum-inspired algorithms on classical hardware (D-Wave, IBM Qiskit simulators, etc.)
Provide extensibility to connect with actual quantum hardware backends in the future.
Current Behaviour
Currently, Adobe Target relies on AI and machine learning models running on classical infrastructure for personalization, testing, and optimization. These models are effective but face computational limitations when handling:
Very large audience segmentation
High-dimensional testing with multiple offers and constraints
Real-time, privacy-safe personalization across billions of signals
Quantum acceleration does not exist in the current system, leaving unexplored potential for ultra-precise personalization.
