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WIREs Comput Mol Sci
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The prospects of quantum computing in computational molecular biology

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Abstract Quantum computers can in principle solve certain problems exponentially more quickly than their classical counterparts. We have not yet reached the advent of useful quantum computation, but when we do, it will affect nearly all scientific disciplines. In this review, we examine how current quantum algorithms could revolutionize computational biology and bioinformatics. There are potential benefits across the entire field, from the ability to process vast amounts of information and run machine learning algorithms far more efficiently, to algorithms for quantum simulation that are poised to improve computational calculations in drug discovery, to quantum algorithms for optimization that may advance fields from protein structure prediction to network analysis. However, these exciting prospects are susceptible to “hype,” and it is also important to recognize the caveats and challenges in this new technology. Our aim is to introduce the promise and limitations of emerging quantum computing technologies in the areas of computational molecular biology and bioinformatics. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Computer Algorithms and Programming Electronic Structure Theory > Ab Initio Electronic Structure Methods
(a) Comparison between a classical bit and a quantum bit or “qubit.” While the classical bit can only take one of two states, 0 or 1, the quantum bit can take any state of the form . Single qubits are often depicted using the Bloch sphere representation, where θ and ϕ are understood as the azimuthal and polar angles in a sphere of unit radius. (b) Scheme of an ion trap qubit, one of the most common approaches to experimental quantum computing. An ion (often 43Ca+) is confined in high vacuum using electromagnetic fields, and is subjected to a strong magnetic field. The hyperfine structure levels are split according to the Zeeman effect, and two selected levels are chosen as the states |0〉 and |1〉. Quantum gates are implemented by appropriate laser pulses, often involving other electronic levels. This diagram has been adapted from [46]. (c) Diagram of a quantum circuit implementing the X or quantum‐NOT gate. We show the matrix representation, and the change in the Bloch sphere. (d) Quantum circuit to generate a Bell state using the Hadamard H gate and the controlled‐NOT gate. The dotted line in the middle of the circuit indicates the state after applying the Hadamard gate
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(a) Simulation of an adiabatic quantum computer implementing a simplified protein folding problem, described in [201]. Color encodes the decimal log probability of a particular binary string. At the end of the computation, the two lowest‐energy solutions have a probability of measurement close to 0.5. In finite time, the evolution is never entirely adiabatic, and other binary strings have residual probabilities of measurement. (b) Depiction of the adiabatic quantum computing process. The potential governing the qubits is slowly modified, causing them to rotate. Note that the Bloch sphere representation is incomplete, as it does not depict the correlations between different qubits, which are necessary for quantum advantage. At the end of the evolution, the system of qubits is in a classical state (or a superposition of classical states) representing the lowest‐energy solution. (c) Energy levels during the adiabatic quantum evolution. When other levels are close to the ground state, the population at the ground state can leak towards excited states. The amount of time required to ensure quasi‐adiabatic evolution is governed by the minimum energy difference between the levels Δ, which is indicated by a dotted line
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(a) Algorithm for quantum simulation in a fault‐tolerant quantum computer. The qubits are divided in two registers: one is prepared in a state |ψ〉 that resembles the objective wavefunction, while the other is left in the |0…0〉 state. The quantum phase estimation (QPE) algorithm is used to find the eigenvalues of the time evolution operator eiHt, which is prepared using Hamiltonian simulation techniques. After QPE, a measurement of the quantum computer yields the energy of the ground state with probability |〈Ψ0|ψ〉|2, hence the importance of preparing a guess state |ψ〉 with nonzero overlap with the true wavefunction. (b) Variational algorithm for quantum simulation in a near‐term quantum computer. This algorithm combines the quantum processor with a classical optimization routine to perform multiple short runs that are quick enough to avoid errors. The quantum computer prepares a guess state with an ansatz quantum circuit dependent on several parameters {θk}. The individual terms of the Hamiltonian are measured one by one (or in commuting groups, employing more advanced strategies), obtaining an estimation of the expected energy for a particular vector of parameters. The parameters are then optimized by the classical optimization routine until convergence. The variational approach has been extended to many algorithmic tasks beyond quantum simulation
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Electronic Structure Theory > Ab Initio Electronic Structure Methods
Computer and Information Science > Computer Algorithms and Programming
Structure and Mechanism > Computational Biochemistry and Biophysics

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