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The prospects of quantum computing in computational molecular biology
Carlos Outeiral, Martin Strahm, Jiye Shi, Garrett M. Morris, Simon C. Benjamin, Charlotte M. Deane
TL;DR
Computational biology contains problems that exceed current computational resources, motivating the search for quantum approaches. This review surveys quantum information processing and algorithmic developments in statistical methods, electronic structure calculations, and optimization, while emphasizing hardware and state-preparation limitations. It concludes that quantum computing may transform computational biology, but useful processors and practical quantum advantage remain future prospects.
Problem
Protein folding, ligand binding, electronic-structure calculations, and other biological problems remain computationally difficult or infeasible with current methods and resources.
Method
The review introduces quantum information processing and synthesizes quantum algorithmic developments in statistical methods, electronic structure calculations, and optimization.
Results
The review identifies promising quantum algorithms across computational biology, including machine learning, quantum simulation, and optimization applications.
Takeaways & Limitations
Quantum computing may make currently impossible computational-biology problems difficult and difficult problems routine, potentially transforming the field.
Takeaways & Limitations
Practical quantum computing is constrained by hardware errors, error-correction overhead, difficult quantum-state preparation, and challenges extracting complete wavefunctions.
Abstract
from arXiv · showhide
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.
1 | INTRODUCTION
Computational biology relies heavily on algorithms, yet key problems such as protein folding, ligand binding, and large-scale genomic alignment remain beyond current computational resources. This review introduces quantum computing as a possible paradigm shift and surveys promising developments across statistical methods, electronic structure calculations, and optimization.
- 1 | INTRODUCTION: Computational methods now routinely support biological experiments and predictions, with many highly cited scientific papers focused on biological algorithms.Examples include quantum simulation, sequence alignment, computational genetics, and X-ray diffraction data processing.
- 1 | INTRODUCTION: The best algorithms for protein folding, ligand–macromolecule binding affinity, and large-scale genomic alignment require resources beyond today’s most powerful supercomputers.These challenges remain computationally infeasible despite substantial progress in computational biology.
- 1 | INTRODUCTION: Quantum computing introduces operations unavailable to classical machines and is presented as a potential solution to computationally infeasible biological problems.The proposed paradigm shift is based on exploiting quantum-mechanical effects in computation.
- 1 | INTRODUCTION: Quantum algorithms can be investigated mathematically and with high-performance simulators even before fully capable quantum hardware exists.Early prototypes and simulators have enabled investigation of algorithms showing promise for biological applications.
- 1 | INTRODUCTION: The review surveys quantum computing applications in computational biology through statistical methods, electronic structure calculations, and optimization.It frames these areas as having already shown promising algorithmic developments.
2 | QUANTUM INFORMATION PROCESSING
Quantum information processing represents information with qubits, manipulates it with quantum gates, and derives computational power from superposition and entanglement. Its practical realization remains constrained by hardware errors, error-correction overhead, and the difficulty of preparing some required quantum states.
- 2.1.1 | Quantum information: Introducing the qubit: Qubits can occupy superpositions of 0 and 1, unlike classical bits, and measurement collapses a superposition to an observed state.The amplitudes of the component states determine the quantum state and its measurement behavior.
- 2.1.1 | Quantum information: Introducing the qubit: Entanglement correlates multiple qubits so that operations on one can affect the collective state, making it a fundamental resource for useful quantum computation.Without entanglement, quantum algorithms can be enacted classically without significant speed differences.
- 2.1.2 | Quantum gates: Quantum gates are unitary operations that manipulate qubit registers, potentially acting on 2^N-dimensional state vectors while controlling only N qubits.Unitarity preserves normalization, and arbitrary unitary transformations represent valid quantum gates.
- 2.1.2 | Quantum gates: A universal set of one- and two-qubit gates can implement arbitrary quantum gates despite the infinite number of possible one-qubit unitary operations.This provides a finite operational basis for quantum computation.
- 2.2 | Quantum hardware: Quantum processors face computation errors from decoherence, imperfect gates, fluctuations, and control imperfections that can corrupt calculations.Fault-tolerant error correction is possible below code-dependent thresholds but requires many physical qubits per logical qubit.
- 2.2 | Quantum hardware: Variational algorithms and error mitigation target noisy intermediate-scale quantum processors by combining short quantum computations with classical parameter optimization.These approaches seek useful computation before full fault tolerance is available.
3 | STATISTICAL METHODS AND MACHINE LEARNING
Quantum computing is presented as a potential accelerator for statistical learning in computational biology, while data access, measurement, and data scarcity constrain practical benefits. The review surveys quantum approaches spanning unsupervised, supervised, probabilistic, and generative models.
- Quantum machine learning could accelerate biological data analysis, including genomics, drug discovery, and structural biology applications.The section targets developments likely to be directly applicable in biology.
- Advantages and shortcomings of quantum machine learning: Exponential speedups could let moderately sized quantum computers tackle training problems requiring the largest classical supercomputers.
- Advantages and shortcomings of quantum machine learning: Quantum models may provide secondary benefits including compact information representations and enhanced biomedical data privacy.For sufficiently large datasets, training may take O(log N) while reconstructing substantial information requires O(N) calls.
- Advantages and shortcomings of quantum machine learning: Practical advantages depend on preparing quantum inputs and extracting useful outputs, while quantum machine learning also faces scarce appropriate data.HHL-style procedures generally expose global properties rather than the full solution, and QRAM assumptions lack a working device and may introduce costly bottlenecks.
- Unsupervised learning: Quantum PCA extracts principal components from high-dimensional data, using repeated measurements to obtain components associated with large eigenvalues.The procedure is described as reducing the dimensionality of information stored in a quantum computer.
- Quantum algorithms have been proposed for persistent homology, SVMs, Gaussian-process regression, HMMs, and generative models including Boltzmann machines.Reported approaches include polynomial-kernel SVM training in O(log N), quantum HMMs using fewer hidden states, and quadratically faster Boltzmann sampling.
4 | EFFICIENT SIMULATION OF QUANTUM SYSTEMS
Quantum simulation could address chemically and biologically important calculations that exceed classical capabilities, while supporting both fault-tolerant and near-term approaches. The main promise is more accurate electronic-structure modeling, although information extraction and ansatz selection remain important limitations.
- Motivation: Quantum simulation targets electronic environments underlying ligand binding and enzyme catalysis, which are extremely difficult to model classically.Understanding these processes reduces to calculating electronic structure and associated energies.
- Advantages: Quantum computers could efficiently solve fully correlated electronic structure, enabling accurate binding-energy estimates and chemical-dynamics simulations.Direct diagonalization of the FCI matrix could yield exact results within a chosen basis set.
- Advantages: Quantum simulation can avoid some classical approximations, including the Born–Oppenheimer approximation, enabling nonadiabatic and relativistic studies relevant to enzymes, DNA mutation, and transition metals.These capabilities are proposed for systems where nuclear motion or relativistic effects matter.
- Near-term devices: Near-term variational algorithms combine short quantum runs with classical optimization, and quantum simulation has been demonstrated on noisy devices.The quantum processor prepares parameterized states, estimates Hamiltonian expectation values, and supplies results to the optimizer.
- Limitations: Quantum simulation has practical drawbacks: retrieving an entire wavefunction is harder than solving classically, and random ansätze can develop vanishing gradients as qubit counts grow.The review notes that no general theory currently identifies a good ansatz, while physically motivated ansätze are expected to avoid this issue.
- Fault-tolerant quantum computation: Fault-tolerant simulation uses Hamiltonian evolution and phase estimation to obtain eigenvalues, with ground-state success depending on the initial guess state's overlap.A physically motivated initial state, such as Hartree–Fock, is used to improve this probability.
5 | OPTIMIZATION PROBLEMS
Quantum optimization offers possible approaches to difficult biological problems such as protein folding and network analysis, but its speedup guarantees remain uncertain and important limitations persist.
- Many computational-biology tasks seek global extrema of high-dimensional functions, including protein free-energy minimization and biological network community detection.
- Adiabatic quantum optimization: Adiabatic quantum computing encodes a score function in a Hamiltonian and slowly evolves from an easily prepared ground state toward the problem Hamiltonian.The system is expected to remain in its instantaneous ground state when the Hamiltonian changes slowly enough.
- Limitations: Exponentially vanishing gaps can occur as problem size increases, and no adiabatic or general quantum solution to NP-complete problems has withstood scrutiny.
- Quantum optimization models: Adiabatic quantum computing is universal only with nonstoquastic evolution, whereas D-Wave’s commercially available implementation uses stoquastic Hamiltonians and is therefore nonuniversal.QAOA provides a separate variational optimization approach for near-term processors.
- Protein structure prediction: Protein lattice folding is NP-hard; studies report possible limited quantum speedup, while D-Wave-based rotamer sampling showed scaling that seemed almost constant versus classical simulated annealing.The reported limited speedup may require error-corrected adiabatic machines or fault-tolerant universal quantum simulation.
6 | CONCLUSIONS
The review presents quantum computing as a potentially transformative technology for computational biology while emphasizing that practical quantum computation is only beginning. It surveys the field’s promise and acknowledges that its coverage is not exhaustive.
- Even small quantum computers may outperform the best supercomputers on certain tasks, potentially making impossible problems difficult and difficult problems routine.
- The review introduces quantum information-processing principles and discusses three important areas relevant to computational biology.
- The review does not cover every topic in the field.
- An emerging quantum computational biology may develop over the next decades as practical quantum computation is only just beginning.
CONFLICT OF INTEREST
The paper declares no conflicts of interest.
- The authors declared no conflicts of interest for this article.