Quantum approximate optimization of bosonic finite-state systems

• URL: http://arxiv.org/abs/2510.05576v1
• Date: Tue, 07 Oct 2025 04:44:13 GMT
• Title: Quantum approximate optimization of bosonic finite-state systems
• Authors: Shakib Daryanoosh,
• Abstract summary: Formulating problems in nature inherently described by finite $D$-dimensional states requires mapping the qudit Hilbert space to that of multiqubit.<n>Here we propose to employ the Hamiltonian-based quantum approximate optimization algorithm (QAOA) through devising appropriate mixing Hamiltonians.<n>We apply this framework to quantum approximate thermalization and find the ground state of the repulsive Bose-Hubbard model in the strong and weak interaction regimes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There exist numerous problems in nature inherently described by finite $D$-dimensional states. Formulating these problems for execution on qubit-based quantum hardware requires mapping the qudit Hilbert space to that of multiqubit which may be exponentially larger. To exclude the infeasible subspace, one common approach relies on penalizing the objective function. However, this strategy can be inefficient as the size of the illegitimate subspace grows. Here we propose to employ the Hamiltonian-based quantum approximate optimization algorithm (QAOA) through devising appropriate mixing Hamiltonians such that the infeasible configuration space is ruled out. We investigate this idea by employing binary, symmetric, and unary mapping techniques. It is shown that the standard mixing Hamiltonian (sum of the bit-flip operations) is the optimal option for symmetric mapping, where the controlled-NOT gate count is used as a measure of implementation cost. In contrast, the other two encoding schemes witness a $p$-fold increase in this figure for a $p$-layer QAOA. We apply this framework to quantum approximate thermalization and find the ground state of the repulsive Bose-Hubbard model in the strong and weak interaction regimes.

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