The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here:

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Photo of Lucas Knuthson

Lucas Knuthson

Doctoral student

Photo of Lucas Knuthson

Using quantum annealing to design lattice proteins


  • Anders Irbäck
  • Lucas Knuthson
  • Sandipan Mohanty
  • Carsten Peterson

Summary, in English

Quantum annealing has shown promise for finding solutions to difficult optimization problems, including protein folding. Recently, we used the D-Wave Advantage quantum annealer to explore the folding problem in a coarse-grained lattice model, the HP model, in which amino acids are classified into two broad groups: hydrophobic (H) and polar (P). Using a set of 22 HP sequences with up to 64 amino acids, we demonstrated the fast and consistent identification of the correct HP model ground states using the D-Wave hybrid quantum-classical solver. An equally relevant biophysical challenge, called the protein design problem, is the inverse of the above, where the task is to predict protein sequences that fold to a given structure. Here, we approach the design problem by a two-step procedure implemented and executed on a D-Wave machine. In the first step, we perform a pure sequence-space search by varying the type of amino acid at each sequence position, and seek sequences which minimize the HP-model energy of the target structure. After mapping this task onto an Ising spin-glass representation, we employ a hybrid quantum-classical solver to deliver energy-optimal sequences for structures with 30–64 amino acids, with a 100% success rate. In the second step, we filter the optimized sequences from the first step according to their ability to fold to the intended structure. In addition, we try solving the sequence optimization problem using only the quantum processing unit (QPU), which confines us to sizes ≤20, due to exponentially decreasing success rates. To shed light on the pure QPU results, we investigate the effects of control errors caused by an imperfect implementation of the intended Hamiltonian on the QPU, by numerically analyzing the Schrödinger equation. We find that the simulated success rates in the presence of control noise semiquantitatively reproduce the modest pure QPU results for larger chains.


  • Computational Science for Health and Environment
  • Centre for Environmental and Climate Science (CEC)
  • eSSENCE: The e-Science Collaboration

Publishing year





Physical Review Research





Document type

Journal article


American Physical Society


  • Other Physics Topics
  • Biophysics
  • Other Computer and Information Science


  • protein design
  • quantum computing
  • quantum annealing
  • biophysical
  • Ising model



Research group

  • Computational Science for Health and Environment


  • ISSN: 2643-1564