Algorithms · Variational quantum algorithm
Protein folding on a tetrahedral lattice by a variational quantum algorithm
Predict the three-dimensional structure a protein takes from its primary sequence of amino acids, posed here on the model Hamiltonian the paper defines for a chain of N monomers placed on a tetrahedral lattice.
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- Record typeLiterature reference
- Reference implementationprotein_folding_with_qaoa
- Reported costO(N⁴) scaling for the model Hamiltonian of a polymer chain with N monomers on a tetrahedral lattice. The abstract states no running time for the variational algorithm, no query or gate count, and no speed-up factor against classical folding methods; its only concrete figures are qubit counts for two named instances, 22 qubits for the 10 amino acid Angiotensin peptide and 9 qubits for a 7 amino acid neuropeptide on an IBM Q 20-qubit quantum computer. On the classical side it states that the problem is intrinsically NP-hard even reduced to its simplest Hydrophobic-Polar model, while classical algorithms provide practical solutions by sampling the conformation space of small proteins.
- Papers1
Predict the three-dimensional structure a protein takes from its primary sequence of amino acids, posed here on the model Hamiltonian the paper defines for a chain of N monomers placed on a tetrahedral lattice. The paper presents a model Hamiltonian with O(N⁴) scaling, together with a corresponding quantum variational algorithm, for the folding of a polymer chain with N monomers on a tetrahedral lattice. The abstract states that the model reflects many physico-chemical properties of the protein, reducing the gap between coarse-grained representations and mere lattice models. The optimisation scheme, which the authors describe as robust and versatile, brings together variational quantum algorithms specifically adapted to classical cost functions and evolutionary strategies (genetic algorithms). The paper reports simulating the folding of the 10 amino acid Angiotensin peptide on 22 qubits, and applying the same method to the folding of a 7 amino acid neuropeptide using 9 qubits on an IBM Q 20-qubit quantum computer.
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The paper presents a model Hamiltonian with O(N⁴) scaling, together with a corresponding quantum variational algorithm, for the folding of a polymer chain with N monomers on a tetrahedral lattice. The abstract states that the model reflects many physico-chemical properties of the protein, reducing the gap between coarse-grained representations and mere lattice models. The optimisation scheme, which the authors describe as robust and versatile, brings together variational quantum algorithms specifically adapted to classical cost functions and evolutionary strategies (genetic algorithms). The paper reports simulating the folding of the 10 amino acid Angiotensin peptide on 22 qubits, and applying the same method to the folding of a 7 amino acid neuropeptide using 9 qubits on an IBM Q 20-qubit quantum computer. Reported cost: O(N⁴) scaling for the model Hamiltonian of a polymer chain with N monomers on a tetrahedral lattice. The abstract states no running time for the variational algorithm, no query or gate count, and no speed-up factor against classical folding methods; its only concrete figures are qubit counts for two named instances, 22 qubits for the 10 amino acid Angiotensin peptide and 9 qubits for a 7 amino acid neuropeptide on an IBM Q 20-qubit quantum computer. On the classical side it states that the problem is intrinsically NP-hard even reduced to its simplest Hydrophobic-Polar model, while classical algorithms provide practical solutions by sampling the conformation space of small proteins..
TakesNothingWhat joins here
No input port at this edge: the record publishes no gate sequence and no register, so there is nothing here to read one off — and unlike a declared hole, nothing has been recorded about what belongs here.
Nothing in the Atlas meets this end.
ReturnsNothingWhat joins here
No output port at this edge: the record publishes no gate sequence and no register, so there is nothing here to read one off — and unlike a declared hole, nothing has been recorded about what belongs here.
Nothing in the Atlas meets this end.
This record publishes no gate sequence and no register, so there is nothing here to read an interface off. Absent rather than empty. See all 152 →
- Record type
- Literature reference
- Reference implementation
- protein_folding_with_qaoa
- Reported cost
- O(N⁴) scaling for the model Hamiltonian of a polymer chain with N monomers on a tetrahedral lattice. The abstract states no running time for the variational algorithm, no query or gate count, and no speed-up factor against classical folding methods; its only concrete figures are qubit counts for two named instances, 22 qubits for the 10 amino acid Angiotensin peptide and 9 qubits for a 7 amino acid neuropeptide on an IBM Q 20-qubit quantum computer. On the classical side it states that the problem is intrinsically NP-hard even reduced to its simplest Hydrophobic-Polar model, while classical algorithms provide practical solutions by sampling the conformation space of small proteins.
- Complexity basis
- abstract of arXiv:1908.02163 (TeX rendered into Unicode: the abstract's script-O written O, its exponent as a superscript, and its italic N as plain N): "we present a model Hamiltonian with O(N⁴) scaling and a corresponding quantum variational algorithm for the folding of a polymer chain with N monomers on a tetrahedral lattice". The qubit figures are from the same abstract: "to simulate the folding of the 10 amino acid Angiotensin peptide on 22 qubits", and "The same method is also successfully applied to the study of the folding of a 7 amino acid neuropeptide using 9 qubits on an IBM Q 20-qubit quantum computer". The classical statements are from "Although classical algorithms provide practical solutions, sampling the conformation space of small proteins, they cannot tackle the intrinsic NP-hard complexity of the problem, even reduced to its simplest Hydrophobic-Polar model". The abstract states no running time and no comparison of running times, so none is recorded here.
- Circuit
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ALGORITHM: Protein folding on a tetrahedral lattice by a variational quantum algorithm
PROBLEM: Predict the three-dimensional structure a protein takes from its primary sequence of amino acids, posed here on the model Hamiltonian the paper defines for a chain of N monomers placed on a tetrahedral lattice.
IDEA: The paper presents a model Hamiltonian with O(N⁴) scaling, together with a corresponding quantum variational algorithm, for the folding of a polymer chain with N monomers on a tetrahedral lattice. The abstract states that the model reflects many physico-chemical properties of the protein, reducing the gap between coarse-grained representations and mere lattice models. The optimisation scheme, which the authors describe as robust and versatile, brings together variational quantum algorithms specifically adapted to classical cost functions and evolutionary strategies (genetic algorithms). The paper reports simulating the folding of the 10 amino acid Angiotensin peptide on 22 qubits, and applying the same method to the folding of a 7 amino acid neuropeptide using 9 qubits on an IBM Q 20-qubit quantum computer.
REPORTED COST: O(N⁴) scaling for the model Hamiltonian of a polymer chain with N monomers on a tetrahedral lattice. The abstract states no running time for the variational algorithm, no query or gate count, and no speed-up factor against classical folding methods; its only concrete figures are qubit counts for two named instances, 22 qubits for the 10 amino acid Angiotensin peptide and 9 qubits for a 7 amino acid neuropeptide on an IBM Q 20-qubit quantum computer. On the classical side it states that the problem is intrinsically NP-hard even reduced to its simplest Hydrophobic-Polar model, while classical algorithms provide practical solutions by sampling the conformation space of small proteins.
BASIS: abstract of arXiv:1908.02163 (TeX rendered into Unicode: the abstract's script-O written O, its exponent as a superscript, and its italic N as plain N): "we present a model Hamiltonian with O(N⁴) scaling and a corresponding quantum variational algorithm for the folding of a polymer chain with N monomers on a tetrahedral lattice". The qubit figures are from the same abstract: "to simulate the folding of the 10 amino acid Angiotensin peptide on 22 qubits", and "The same method is also successfully applied to the study of the folding of a 7 amino acid neuropeptide using 9 qubits on an IBM Q 20-qubit quantum computer". The classical statements are from "Although classical algorithms provide practical solutions, sampling the conformation space of small proteins, they cannot tackle the intrinsic NP-hard complexity of the problem, even reduced to its simplest Hydrophobic-Polar model". The abstract states no running time and no comparison of running times, so none is recorded here.
PRIMARY SOURCE: Anton Robert, Panagiotis Kl. Barkoutsos, Stefan Woerner, Ivano Tavernelli (2019), Resource-Efficient Quantum Algorithm for Protein Folding — https://arxiv.org/abs/1908.02163
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- Estimate a Hamiltonian's ground-state energy Slot
Takes A Hermitian reachable as a sum of terms, as sparse-access oracles or as a block-encoding; a way to prepare trial states, and — for the methods that need it — a trial state whose overlap with the ground state is not negligible; a target additive error and a confidence . Returns A scalar estimate of the lowest eigenvalue with a stated additive-error guarantee, plus the run or query budget it consumed. Whether that estimate is also a rigorous upper bound is a property of the method and is not promised by the slot.
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References
- Resource-Efficient Quantum Algorithm for Protein Folding
Anton Robert, Panagiotis Kl. Barkoutsos, Stefan Woerner, Ivano Tavernelli · 2019
Primary source: it states the O(N⁴) model Hamiltonian for a polymer chain of N monomers on a tetrahedral lattice, the corresponding quantum variational algorithm, and the optimisation scheme that combines variational quantum algorithms adapted to classical cost functions with evolutionary strategies. It also reports the two experiments quoted here, the 10 amino acid Angiotensin peptide on 22 qubits and the 7 amino acid neuropeptide on 9 qubits of an IBM Q 20-qubit machine. Consult it for the encoding of a conformation into qubits, the ansatz and optimiser settings, the platform the 22-qubit result was obtained on, and the accuracy of the reported folds, none of which the abstract states.