Dr. Pravir Malik is the founder and technologist of QIQuantum and the Forbes Technology Council Community leader for Quantum Computing.
Agentic AI and quantum technologies are each advancing the boundaries of what machines can perceive, compute and accomplish. Yet their convergence could open possibilities that neither technology can realize effectively on its own. By pairing autonomous AI systems with the unique capabilities of quantum computing and sensing, organizations may be able to approach increasingly complex problems in fundamentally different ways.
To help leaders identify emerging opportunities, I asked members of the Quantum Computing Group, a community that I lead through Forbes Technology Council, to share one capability they believe could emerge from this convergence and explain why it could become consequential.
1. Real-Time Optimization Across Complex Operations
Combining agentic AI with quantum computing could enable real-time optimization across complex operations, from telecom networks to supply chains and energy grids. AI agents can respond to changing conditions, while quantum computing can optimize the decisions they make, helping organizations react faster, use resources more efficiently, and operate at greater scale. – Alan Baratz, D-Wave
2. Adaptive Sensing That Discovers And Locates Signals
Combining agentic AI and Rydberg sensing can enable unique and compelling capabilities. Rydberg sensors provide broad sensitivity to the EM environment. AI agents can decide what frequencies to examine, which signals matter, and how to configure the sensor. Neither is as effective alone. AI is limited by what classical sensors can detect, and quantum sensors benefit from intelligent signal analysis, interpretation and tasking. Together, they could create systems that discover, classify and locate signals in real time, with broad applications for national security and telecommunications. – Paul Lipman, Infleqtion
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3. Accelerating Scientific Discovery Through Autonomous Experimentation
Agentic AI paired with quantum computing transforms scientific discovery into quick and iterative experimentation. While classical agents manage workflow orchestration, quantum processors compute high-dimensional molecular interactions that non-quantum systems approximate. This closed-loop pipeline allows research agents to hypothesize, test quantum-level physical constraints, and iterate autonomously. The result is a fundamental collapse in time-to-market for advanced materials and biopharmaceuticals verticals. – Anil Pantangi, Capgemini America Inc
4. Compressing Discovery Cycles In Complex Physical Systems
One consequential capability could be autonomous discovery in complex physical systems. Quantum computing or sensing could expose patterns and possibilities that are impractical to model classically, while agentic AI continuously decides what to test, learns from the results and redirects the search. Together, they could compress discovery cycles in areas such as materials, energy and medicine from years of experimentation into far more adaptive, targeted exploration. – Gouri Sankar Dash, Tata Consultancy Services
5. Turning Quantum Computing Into Continuous Financial Decisions
In financial services, combining agentic AI and quantum computing could eventually create autonomous financial decision systems that continuously simulate vast numbers of possible future states, select an optimal response, and act within defined risk and governance boundaries. Quantum expands what can feasibly be explored; agents turn that computational advantage into continuous decisions and action. – Lakshmanan Alagappan, Genpact UK
6. Creating An Autonomous Discovery-To-Decision Loop
At FutureProof CXO™, I see the defining capability as autonomous discovery: Governed AI agents could frame hypotheses and adapt experiments, while quantum sensing detects previously inaccessible signals and quantum computing evaluates complex possibilities. Neither technology closes this loop alone. Together, they could compress discovery-to-decision cycles across medicine, materials, energy and national security—turning quantum insight into accountable action. – Rajjie Sarmey, FutureProof CXO™
7. Turning Invisible Signals Into Autonomous Decisions
One consequential capability is closed-loop quantum decision systems. Quantum sensors could detect signals beyond classical sensitivity, while agentic AI interprets them, selects follow-up measurements and adapts actions in real time. Neither sensing without autonomous reasoning nor AI without quantum-grade observations closes that loop. The result could transform navigation, infrastructure monitoring and scientific discovery by turning previously invisible signals into autonomous decisions. – Dr. Aditya Vikram Kashyap
8. Using Precision Sensing To Predict Failures Before They Happen
Closed-loop quantum autonomy could turn precision sensing into adaptive action. Quantum sensors catch subtle physical shifts invisible to classical tools; agentic AI reads these signals and instantly adjusts industrial controls within safety limits. This convergence could predict equipment instability before failure ever surfaces, transforming reactive maintenance into truly autonomous foresight. – Vinod Bijlani, HPE
9. Giving Autonomous Systems a Computationally Grounded Reason To Pause
One capability could be a real-time counterfactual governor. For suitable problems, quantum computation could surface intervention paths impractical to explore classically; an AI agent could test them against human-set constraints before acting. Neither closes the loop alone. This could give autonomy something rare: a computationally grounded reason to hesitate. – Mani Padisetti, Almost Magic Tech Lab
10. Accelerating Drug And Materials Discovery Through Closed-Loop Experimentation
Combining agentic AI and quantum computing can deliver autonomous, closed-loop discovery in complex physics and biology. Quantum systems model nature at the molecular level with extreme precision, but they lack reasoning and cannot decide what to test next. Agentic AI can plan and hypothesize, but it struggles with complex physical calculations. Combined, AI agents can formulate scientific hypotheses, run quantum simulations, interpret the molecular results, and adapt the next experiment in real time—drastically accelerating drug design and materials science. – Mahendran Chinnaiah
11. Detecting Underground Infrastructure Failures Before Visible Damage Appears
A city could gain a subsurface steward. Quantum gravimeters would notice density changes caused by leaking pipes, washout or voids; an agent would choose where to rescan, correlate permits and weather, estimate failure paths, and schedule the least disruptive repair. Sensors alone produce ambiguous anomalies, while agents lack reliable sight beneath concrete. Together, they could prevent sinkholes and water loss before visible damage appears. Leaders should begin with audited, advisory pilots, because excavation and public-safety actions require accountable human approval. – Jagadish Gokavarapu, Wissen Infotech