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Estimating Algorithmic Information Using Quantum Computing for Genomics Applications
2021
Applied Sciences
Inferring algorithmic structure in data is essential for discovering causal generative models. In this research, we present a quantum computing framework using the circuit model, for estimating algorithmic information metrics. The canonical computation model of the Turing machine is restricted in time and space resources, to make the target metrics computable under realistic assumptions. The universal prior distribution for the automata is obtained as a quantum superposition, which is further
doi:10.3390/app11062696
fatcat:2qxlge24avgm3n6eu3ww7bqava