Related Experiment Video
Updated: Jun 13, 2026
10:58
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
Published on: July 25, 2013
Entropy quantum computing for fixed-backbone protein design
Babak Emami1, Wesley Dyk2, David Haycraft2
1Quantum Computing Inc. (QCi), 5 Marine View Plaza, Suite 214, Hoboken, NJ, 07030, USA. bemami@quantumcomputinginc.com.
Scientific Reports
|June 11, 2026
Summary
Computational protein design (CPD) is optimized using a novel quantum computing approach. This method finds low-energy protein configurations, approaching optimal solutions for biotechnology applications.
Area of Science:
- Biotechnology
- Computational Biology
- Quantum Computing
Background:
- Computational protein design (CPD) is crucial for enzyme engineering and therapeutics.
- The combinatorial complexity of CPD challenges traditional optimization methods.
Purpose of the Study:
- To formulate fixed-backbone CPD as a quadratic Hamiltonian for quantum computing.
- To evaluate the performance of Quantum Computing Inc.'s Dirac-3 photonic entropy computing platform for CPD.
Main Methods:
- Formulating fixed-backbone CPD as a quadratic Hamiltonian over rotamer variables.
- Utilizing Quantum Computing Inc.'s Dirac-3 photonic entropy computing platform.
- Benchmarking against an exact classical cost function network (CFN) solver for optimal baselines.
Main Results:
- Dirac-3 achieved best-observed solutions within 0.16-2.47% of optimal energies on benchmark proteins (493-943 variables).
- The quantum approach identified low-energy configurations for directly solvable CPD instances.
- Runtime analysis showed CFN faster in absolute terms, while Dirac-3 exhibited moderate runtime growth with problem size.
Conclusions:
- The proposed quantum formulation is effective for optimization in directly solvable CPD instances.
- Results establish a benchmark for entropy-based optimization in CPD.
- Further exploration of decomposition-based approaches for larger instances is noted.
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