Predict the three-dimensional structure of a protein from its amino-acid sequence, addressed here by parameterizing the protein in terms of the torsion angles of its amino acids rather than by a lattice-model simplification.
protein foldingquantum walkmetropolis algorithmtorsion angleshybrid algorithm
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Predict the three-dimensional structure of a protein from its amino-acid sequence, addressed here by parameterizing the protein in terms of the torsion angles of its amino acids rather than by a lattice-model simplification. Casares, Campos and Martin-Delgado present QFold, a hybrid quantum algorithm for predicting the three-dimensional structure of proteins, by combining recent deep learning advances with the well known technique of quantum walks applied to a Metropolis algorithm. The authors describe QFold as fully scalable, and state that, in contrast to previous quantum approaches, it does not require a lattice model simplification, relying instead on what they call the much more realistic assumption of parameterizing the protein in terms of torsion angles of the amino acids. They compare QFold against its classical analog for different annealing schedules and report finding a polynomial quantum advantage. The authors also report implementing a minimal realization of the quantum Metropolis step of QFold on the IBMQ Casablanca quantum system.
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What this takes and returns
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How it works
Casares, Campos and Martin-Delgado present QFold, a hybrid quantum algorithm for predicting the three-dimensional structure of proteins, by combining recent deep learning advances with the well known technique of quantum walks applied to a Metropolis algorithm. The authors describe QFold as fully scalable, and state that, in contrast to previous quantum approaches, it does not require a lattice model simplification, relying instead on what they call the much more realistic assumption of parameterizing the protein in terms of torsion angles of the amino acids. They compare QFold against its classical analog for different annealing schedules and report finding a polynomial quantum advantage. The authors also report implementing a minimal realization of the quantum Metropolis step of QFold on the IBMQ Casablanca quantum system. The Classiq library carries this subject under applications · chemistry/protein_folding. Reported cost: A polynomial quantum advantage over the classical analog algorithm, found by comparing the two across different annealing schedules. The abstract states no exponent, no big-O expression, and no other resource count for either algorithm..
Implementation
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protein-folding-quantum-walk.txt
ALGORITHM: QFold: protein folding by quantum walk
PROBLEM: Predict the three-dimensional structure of a protein from its amino-acid sequence, addressed here by parameterizing the protein in terms of the torsion angles of its amino acids rather than by a lattice-model simplification.
IDEA: Casares, Campos and Martin-Delgado present QFold, a hybrid quantum algorithm for predicting the three-dimensional structure of proteins, by combining recent deep learning advances with the well known technique of quantum walks applied to a Metropolis algorithm. The authors describe QFold as fully scalable, and state that, in contrast to previous quantum approaches, it does not require a lattice model simplification, relying instead on what they call the much more realistic assumption of parameterizing the protein in terms of torsion angles of the amino acids. They compare QFold against its classical analog for different annealing schedules and report finding a polynomial quantum advantage. The authors also report implementing a minimal realization of the quantum Metropolis step of QFold on the IBMQ Casablanca quantum system.
REPORTED COST: A polynomial quantum advantage over the classical analog algorithm, found by comparing the two across different annealing schedules. The abstract states no exponent, no big-O expression, and no other resource count for either algorithm.
BASIS: The abstract of arXiv:2101.10279, the only source read for this record, states: "We compare it with its classical analog for different annealing schedules and find a polynomial quantum advantage". It names no exponent, no big-O expression, and no other quantitative bound — not a qubit count, not a gate count, not a query count — for either algorithm; that clause is the entirety of the abstract's complexity content. The Classiq index entry for applications/chemistry/protein_folding/protein_folding_with_quantum_walk gives a directory path and a file list (qfold.ipynb, qfold.qmod) and states no bound either.
DEMONSTRATED BY: the Classiq library entry applications/chemistry/protein_folding/protein_folding_with_quantum_walk
PRIMARY SOURCE: P A M Casares, Roberto Campos, M A Martin-Delgado (2021), QFold: Quantum Walks and Deep Learning to Solve Protein Folding — https://arxiv.org/abs/2101.10279
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Quantum vs classical
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Literature & references
QFold: Quantum Walks and Deep Learning to Solve Protein Folding2021 · P A M Casares, Roberto Campos, M A Martin-Delgado
Primary source: it names QFold, states that it combines deep learning with quantum walks applied to a Metropolis algorithm, that it parameterizes the protein by torsion angles rather than a lattice model, and reports a polynomial quantum advantage over its classical analog together with a minimal quantum-Metropolis realization on IBMQ Casablanca. Consult it for how QFold is constructed — the deep-learning component, the quantum-walk step, the annealing schedules compared, and the size of the IBMQ Casablanca realization — none of which the abstract states.