Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: K-spin Hamiltonian for quantum-resolvable Markov decision processes   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 proving
that the variational minimization of our Hamiltonian is equivalent to the
Bellman optimality condition 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.

27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph