Predicting a protein's three-dimensional structure from its primary amino acid sequence constitutes the protein folding problem, a pivotal challenge within computational biology. This task has been identified as a...
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Predicting a protein's three-dimensional structure from its primary amino acid sequence constitutes the protein folding problem, a pivotal challenge within computational biology. This task has been identified as a fitting domain for applying quantum annealing, an algorithmic technique posited to be faster than its classical counterparts. Nevertheless, the utility of quantum annealing is intrinsically contingent upon the spectral gap associated with the Hamiltonian of lattice proteins. This critical dependence introduces a limitation to the efficacy of these techniques, particularly in the context of simulating the intricate folding processes of proteins. In this paper, we address lattice protein folding as a polynomial unconstrained binary optimization problem, devising a hybrid quantum-classical algorithm to determine the minimum energy conformation effectively. Our method is distinguished by its logarithmic scaling with the spectral gap, conferring a significant edge over the conventional quantum annealing algorithms. The present findings indicate that the folding of lattice proteins can be achieved with a resource consumption that is polynomial in the lattice protein length, provided an ansatz state that encodes the target conformation is utilized. We also provide a simple and scalable method for preparing such states and further explore the adaptation of our method for extension to off-lattice protein models. This work paves a new avenue for surmounting complex computational biology problems via the utilization of quantum computers.
Quantum computing provides powerful algorithmic tools that have been shown to outperform established classical solvers in specific optimization tasks. A core step in solving optimization problems with known quantum al...
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ISBN:
(纸本)9798400701207
Quantum computing provides powerful algorithmic tools that have been shown to outperform established classical solvers in specific optimization tasks. A core step in solving optimization problems with known quantum algorithms such as the Quantum Approximate optimization Algorithm (QAOA) is the problem formulation. While quantum optimization has historically centered around Quadratic unconstrainedoptimization (QUBO) problems, recent studies show, that many combinatorial problems such as the TSP can be solved more efficiently in their native polynomialunconstrainedoptimization (PUBO) forms. As many optimization problems in practice also contain continuous variables, our contribution investigates the performance of the QAOA in solving continuous optimization problems when using PUBO and QUBO formulations. Our extensive evaluation on suitable benchmark functions, shows that PUBO formulations generally yield better results, while requiring less qubits. As the multi-qubit interactions needed for the PUBO variant have to be decomposed using the hardware gates available, i.e., currently single- and two-qubit gates, the circuit depth of the PUBO approach outscales its QUBO alternative roughly linearly in the order of the objective function. However, incorporating the planned addition of native multi-qubit gates such as the global Molmer-Sorenson gate, our experiments indicate that PUBO outperforms QUBO for higher order continuous optimization problems in general.
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