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This research introduces PDBQITE, a probabilistic variant of the Double-Bracket quantum imaginary-time evolution algorithm that significantly reduces circuit depth using only a single ancilla qubit. While the method trades total success probability for efficiency, simulations indicate per-step probabilities remain high enough to enable practical iterations for ground-state preparation and optimization tasks like MaxCut. The work advances near-term quantum algorithms by offering a more resource-efficient pathway for solving complex Hamiltonian problems compared to existing probabilistic approaches.
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A quantum double-bracket algorithm for imaginary-time evolution with exponentially shorter depth

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arXiv:2609.37095v1 Announce Type: new Abstract: Imaginary-time evolution (ITE) is a widely used technique for preparing the ground state of a target Hamiltonian. Building on the recently proposed framework of Double-Bracket quantum ITE (DB-QITE), we introduce a probabilistic variant (PDBQITE) that achieves an exponential reduction in circuit depth. Our algorithm requires only a single ancilla qubit and a quantum circuit that consists of one controlled real-time evolution and one mid-circuit measurement per iteration. The trade-off is an exponential decay of the total success probability with the number of iterations, for which we derive an analytical lower bound that is independent of system size. Our numerical simulations show that the per-step success probability remains close to unity, making it realistic to execute many iterations, thus providing a substantial improvement over prior constructions. We show that PDBQITE outperforms the near-term probabilistic algorithm PITE for ground-state preparation of molecular Hamiltonians, and that a hybrid QAOA-PDBQITE scheme achieves higher approximation ratios than QAOA at the same circuit depth on random regular graphs for the MaxCut problem.

Abstract:Imaginary-time evolution (ITE) is a widely used technique for preparing the ground state of a target Hamiltonian. Building on the recently proposed framework of Double-Bracket quantum ITE (DB-QITE), we introduce a probabilistic variant (PDBQITE) that achieves an exponential reduction in circuit depth. Our algorithm requires only a single ancilla qubit and a quantum circuit that consists of one controlled real-time evolution and one mid-circuit measurement per iteration. The trade-off is an exponential decay of the total success probability with the number of iterations, for which we derive an analytical lower bound that is independent of system size. Our numerical simulations show that the per-step success probability remains close to unity, making it realistic to execute many iterations, thus providing a substantial improvement over prior constructions. We show that PDBQITE outperforms the near-term probabilistic algorithm PITE for ground-state preparation of molecular Hamiltonians, and that a hybrid QAOA-PDBQITE scheme achieves higher approximation ratios than QAOA at the same circuit depth on random regular graphs for the MaxCut problem.

arXiv:2609.37095 [quant-ph]

(or arXiv:2609.37095v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2609.37095 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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