Mandi, 29 September 2025 – Why do birds flock, fish school, or humans synchronize their movements without a designated leader? This age-old question has fascinated scientists across disciplines for decades. Now, researchers at the Indian Institute of Technology (IIT) Mandi have put forward a groundbreaking explanation: the secret may lie in quantum-inspired perception.
The study, led by Prof. Laxmidhar Behera and his team, Dr. Jyotiranjan Beuria and Mayank Chaurasiya, recently published in the prestigious journal Proceedings of the Royal Society A (2025), introduces a radical mathematical framework to explain how coordination emerges in nature. In classical models of collective motion, such as the well-known Vicsek model, agents align their movement based on their neighbors’ directions. While these models capture some aspects of swarming and flocking, they often fall short in accounting for real-world complexities, such as noisy environments, delays in response, or ambiguous information. The IIT Mandi team approached the puzzle from a different angle. Drawing inspiration from quantum mechanics, they proposed that each agent’s perception does not collapse immediately into a definite decision. Instead, it exists in a superposition of possibilities, much like a particle in quantum physics can exist in multiple states until observed.
Commenting on the study, Prof. Laxmidhar Behera, Director of IIT Mandi and co-author of the paper, said, “Our work shows that quantum-inspired ideas can move beyond physics and provide fresh insight into one of nature’s oldest mysteries: how collective order arises out of local perception. The implications range from understanding the mind and brain to engineering next-generation intelligent systems.” His words reflect a growing trend in modern science: borrowing principles from quantum theory to enrich disciplines far beyond fundamental physics. By bridging cognitive science, biology, and engineering, the IIT Mandi study points to a unifying framework for perception and coordination.
A Unified Framework for Nature and Machines
In this framework, agents perceive their neighbors not through fixed snapshots but through entangled perceptual states that evolve dynamically. Coordination arises naturally as these states resolve, balancing uncertainty and alignment in a way that mirrors quantum behavior. A central contribution of the study is the introduction of two novel quantities: perception strength, which measures how strongly agents align their perceptual states, and perceptual energy, which measures the stability of collective perception within the group. Together, these measures allow scientists to quantify how coordination emerges even in noisy or incomplete conditions. Importantly, the researchers demonstrated that classical flocking models are special cases of their broader, quantum-inspired theory. This means that existing frameworks for collective motion can be seen as approximations within a more general, perception-driven model.
New Metrics for Measuring Group Intelligence
The implications of this work extend far beyond theory. In biology, the framework offers a new perspective on how swarms of animals remain cohesive despite disturbances. Instead of relying solely on physical interaction rules, the study suggests that perception itself plays a fundamental role in generating order in living systems.
Game-Changing Implications
In robotics, swarm robotics such as coordinated drones used in search-and-rescue operations, environmental monitoring, or planetary exploration could adopt quantum-inspired perception to achieve more flexible and adaptive coordination. In neuroscience and psychology, the study resonates with how human perception often involves ambiguity, sudden switching, and context-dependent interpretation. By offering a rigorous mathematical model for perceptual dynamics, the framework provides a fresh way to understand brain function and cognitive processes. In artificial intelligence, future AI systems could harness quantum-inspired perceptual operators to handle uncertainty with greater robustness, avoiding brittle or premature decisions when confronted with ambiguous or incomplete data.