Algorithms · Quantum walk
QFold: protein folding by quantum walk
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.
Attested & literatureUpdated
A small worked example, computed in your browser. It is not a result from this record's papers.
A discrete-time quantum walk on a 4-cycle
One coin qubit, two position qubits (4 positions on a cycle), 3 steps starting at position 0.
Step 1 of 3 · Quantum walk step (4-cycle)
Probability of each outcome after this step
- 01150%
- 11050%
Flips the coin with a Hadamard, then shifts the position by +1 if the coin is |1⟩ or -1 mod 4 if the coin is |0⟩; with the coin in superposition, both shifts happen at once, entangling coin and position.
Probability leaves the outcome it was on and appears on 2 entirely different ones — not one outcome is populated both before and after. This is the step that entangles the qubits: from here on no single qubit has a state of its own, only the register as a whole does.
The same coin flip and shift, applied again.
Probability leaves all 2 outcomes it was on and appears on 4 entirely different ones — not one outcome is populated both before and after.
The same coin flip and shift, a third time.
Probability leaves all 4 outcomes it was on and appears on 2 entirely different ones — not one outcome is populated both before and after. The entanglement comes apart here: each qubit has a state of its own again.
Readout The position always reads 3, for either value of the coin: constructive interference on this small cycle concentrates the walk onto a single position after exactly 3 steps.
Open in Studio- Record typeLiterature reference
- Reference implementationprotein_folding_with_quantum_walk
- Reported costA 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.
- Papers1
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.
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 →
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..
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_quantum_walk
- 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.
- Complexity 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 demonstration this record covers states no bound either.
- Circuit
- Not supplied
This record is not a concrete circuit.
Quantum vs classical
Classical baseline
Compare Quantum walk with the strongest classical method for the same instance, input budget, and output metric.
Quantum claim
This reference exposes a quantum circuit pattern; it does not imply an application-level speedup without a matched benchmark.
How to compare
Report input loading, circuit depth, repetitions, classical preprocessing, post-processing, and wall-clock time together.
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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 demonstration this record covers states no bound either.
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
This is a literature reference record, not an executable circuit.A reference record, not runnable source. Leona cannot execute it, so it cannot be saved to your Library as a circuit.
Related entries
References
- QFold: Quantum Walks and Deep Learning to Solve Protein Folding
P A M Casares, Roberto Campos, M A Martin-Delgado · 2021
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.