Saturday 07 June 2025
As robots and autonomous vehicles navigate complex environments, they need to make decisions quickly about where to go next. This is particularly challenging in unknown territories, where there’s no clear path or obstacle-free route. To tackle this problem, researchers have developed a new approach that combines classical computing with the power of quantum mechanics.
The traditional method for planning the next best view – the location from which to gather more information about the environment – relies on sampling-based algorithms. These methods work well in simple scenarios but struggle when faced with complex environments or limited computing resources. In contrast, the new hybrid approach, called Hybrid Quantum-Classic Next-Best View (HQC-NBV), uses a quantum computer to solve the problem.
The HQC-NBV system consists of two main components: a classical component that encodes the problem and a quantum component that solves it using a variational circuit. The classical component defines the environment, the robot’s current position, and the objectives – such as finding the shortest path or reconstructing a 3D model. The quantum component uses this information to generate a set of possible next best views, which are then evaluated based on their expected information gain.
The key innovation is the use of entanglement patterns in the quantum circuit, which enable the system to explore multiple possibilities simultaneously and efficiently. This allows HQC-NBV to outperform classical methods in complex environments, where the optimal solution may not be immediately apparent.
Experiments have demonstrated the effectiveness of HQC-NBV in various scenarios, including 3D object reconstruction, autonomous navigation, and fruit detection. In each case, the system was able to find the next best view more efficiently than traditional methods, often by a significant margin.
The implications are far-reaching, with potential applications in fields such as robotics, computer vision, and artificial intelligence. By leveraging the power of quantum mechanics, HQC-NBV can help robots and autonomous vehicles make better decisions in complex environments, paving the way for greater autonomy and improved performance.
One of the most exciting aspects of this research is its potential to enable robots to adapt more quickly to changing situations. In traditional approaches, the robot’s decision-making process is often limited by the complexity of the environment or the limitations of its sensors. HQC-NBV, however, can process large amounts of data in parallel and make decisions based on a broader range of possibilities.
Cite this article: “Quantum Leap for Robots: Hybrid Approach Solves Complex Navigation Problems”, The Science Archive, 2025.
Robots, Autonomous Vehicles, Quantum Mechanics, Classical Computing, Next-Best View, Sampling-Based Algorithms, Variational Circuit, Entanglement Patterns, Artificial Intelligence, Robotics
Reference: Xiaotong Yu, Chang Wen Chen, “HQC-NBV: A Hybrid Quantum-Classical View Planning Approach” (2025).
