Strategic perspectives on quantum computing, artificial intelligence, and emerging technology. Practical guidance for technology leaders navigating the quantum and AI landscape.
NIST published its post-quantum cryptographic standards in 2024. Organizations now face a 5-10 year migration timeline. Understanding the threat landscape and planning your PQC transition is critical.
Adversaries are harvesting encrypted data today, storing it until quantum computers can break current encryption. Any sensitive data with long-term value is already at risk.
QML promises exponential speedups for specific problems. Understanding where quantum advantage actually applies helps separate viable applications from marketing hype.
Most enterprise AI projects fail to move beyond pilot phase. The gap between POC and production requires addressing data quality, MLOps, governance, and organizational change.
Large Language Models offer powerful capabilities but require careful integration. RAG architectures, prompt engineering, and guardrails are essential for enterprise deployment.
Quantum algorithms excel at certain optimization problems. Portfolio optimization, supply chain logistics, and scheduling represent near-term quantum opportunities.
Core concepts in AI, machine learning, and quantum computing for technology leaders. See our complete glossary for detailed definitions.
Machines performing tasks requiring human intelligence. Modern AI leverages ML algorithms for pattern recognition, prediction, and continuous improvement.
Systems that learn from data without explicit programming. ML builds mathematical models from training data to make predictions on new inputs.
Processing information using quantum mechanical phenomena. Superposition and entanglement enable exploration of multiple computational paths simultaneously.