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• W3097844894 abstract "The Markov decision process is the mathematical formalization underlying the modern field of reinforcement learning when transition and reward functions are unknown. We derive a pseudo-Boolean cost function that is equivalent to a K-spin Hamiltonian representation of the discrete, finite, discounted Markov decision process with infinite horizon. This K-spin Hamiltonian furnishes a starting point from which to solve for an optimal policy using heuristic quantum algorithms such as adiabatic quantum annealing and the quantum approximate optimization algorithm on near-term quantum hardware. In arguing that the variational minimization of our Hamiltonian is approximately equivalent to the Bellman optimality condition for a prevalent class of environments we establish an interesting analogy with classical field theory. Along with proof-of-concept calculations to corroborate our formulation by simulated and quantum annealing against classical Q-Learning, we analyze the scaling of physical resources required to solve our Hamiltonian on quantum hardware." @default.
• W3097844894 created "2020-11-09" @default.
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• W3097844894 date "2020-10-30" @default.
• W3097844894 modified "2023-09-26" @default.
• W3097844894 title "K-spin Hamiltonian for quantum-resolvable Markov decision processes" @default.
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• W3097844894 doi "https://doi.org/10.1007/s42484-020-00026-6" @default.
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