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Completed TRL 3 (started at 1, targeting 3)
Distributed estimation algorithms (Bayesian inference, Markov decision process) and distributed machine learning/artificial intelligence algorithms; involve computation along multiple feasible solution paths and selecting an optimal solution. Due to curse of dimensionality, the number of feasible solution paths grows exponentially with the number of states in the system. We present a novel quantum-based framework to remedy this problem.
We will develop a new capability and lay the theoretical foundations for future quantum-computing and quantum-sensing capabilities, which could be used in future missions that involve distributed estimation. Quantum-based methodology will significantly enhance the autonomy capabilities of future multi-agent missions, thereby enabling daring missions in difficult/hazardous environments (e.g. ocean worlds) that will produce high-quality science.
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