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Solving dense protein system challenges, this study recasts lattice protein energy minimization as a quadratic unconstrained binary optimization (QUBO) problem. Both classical and quantum annealing efficiently found the minimum energy configuration for a chain length of 48.

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Area of Science:

  • Computational biology
  • Quantum computing
  • Optimization problems

Background:

  • Steric clashes complicate modeling dense protein systems with explicit-chain methods.
  • Lattice protein energy minimization on a confined grid presents a complex optimization challenge.
  • This problem shares similarities with scheduling problems and can be formulated as a QUBO.

Purpose of the Study:

  • To investigate the efficacy of quadratic unconstrained binary optimization (QUBO) for solving lattice protein energy minimization.
  • To compare classical and quantum-classical annealing approaches for this optimization problem.
  • To benchmark QUBO-based methods against traditional programming techniques and exact enumeration.

Main Methods:

  • Formulating the lattice protein energy minimization problem as a QUBO.
  • Employing classical simulated annealing.
  • Utilizing hybrid quantum-classical annealing on a D-Wave system.
  • Testing linear and quadratic programming methods.

Main Results:

  • The QUBO formulation was successfully solved for a lattice protein chain length of 48 using both simulated annealing and quantum-classical annealing.
  • Hybrid quantum-classical annealing achieved solutions in approximately 10 seconds.
  • Linear and quadratic programming methods showed limitations with protein chain constraints.

Conclusions:

  • QUBO is a viable and efficient approach for solving dense protein system energy minimization problems.
  • Quantum-classical annealing offers a swift and consistent method for tackling these complex optimization tasks.
  • Further exploration of QUBO for protein modeling is warranted, especially for larger systems.