A perspective on protein structure prediction using quantum computers
arXiv:2312.00875 · doi:10.1021/acs.jctc.4c00067
Abstract
Despite the recent advancements by deep learning methods such as AlphaFold2, \textit{in silico} protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whether quantum computers can offer some meaningful benefits for approaching this problem. Yet, identifying specific problem instances amenable to quantum advantage, and estimating quantum resources required are equally challenging tasks. Here, we share our perspective on how to create a framework for systematically selecting protein structure prediction problems that are amenable for quantum advantage, and estimate quantum resources for such problems on a utility-scale quantum computer. As a proof-of-concept, we validate our problem selection framework by accurately predicting the structure of a catalytic loop of the Zika Virus NS3 Helicase, on quantum hardware.
References in corpus (36)
- A variational eigenvalue solver on a quantum processor
- Quantum algorithm for solving linear systems of equations
- Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
- A Quantum Approximate Optimization Algorithm
- Strengths and Weaknesses of Quantum Computing
- Identification of direct residue contacts in protein-protein interaction by message passing
- A Survey on the Explainability of Supervised Machine Learning
- Parallel implementation of high-fidelity multi-qubit gates with neutral atoms
- Quantum Computing for Finance: State of the Art and Future Prospects
- The Future of Quantum Computing with Superconducting Qubits
- Obstacles to State Preparation and Variational Optimization from Symmetry Protection
- Blueprint for a Scalable Photonic Fault-Tolerant Quantum Computer
- Quantum Computer Systems for Scientific Discovery
- -mixers: analytical and numerical results for QAOA
- Demonstration of quantum advantage in machine learning
- Resource-Efficient Quantum Algorithm for Protein Folding
- Quantum Computing at the Frontiers of Biological Sciences
- Machine Learning Coarse-Grained Potentials of Protein Thermodynamics
- Parameter Transfer for Quantum Approximate Optimization of Weighted MaxCut
- Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC
- Quantum Algorithm for the Vlasov Equation
- Influence of Multiple Sequence Alignment Depth on Potts Statistical Models of Protein Covariation
- Quantum kernels for real-world predictions based on electronic health records
- Low depth mechanisms for quantum optimization
- Classical and Quantum Bounded Depth Approximation Algorithms
- Quantum algorithm for energy matching in hard optimization problems
- Provable Quantum Advantage in Randomness Processing
- Towards Quantum Advantage in Financial Market Risk using Quantum Gradient Algorithms
- On Grover's Search Algorithm from a Quantum Information Geometry Viewpoint
- The role of controllability in optimizing quantum dynamics
- Error generation and propagation in Majorana-based topological qubits
- Quantum Dropout: On and Over the Hardness of Quantum Approximate Optimization Algorithm
- A Comment on Fisher Information and Quantum Algorithms
- Towards quantum-enabled cell-centric therapeutics
- Quantum Information Science and Technology for Nuclear Physics. Input into U.S. Long-Range Planning, 2023
- Bang-bang control as a design principle for classical and quantum optimization algorithms
Cited by in corpus (9)
- Integrating Quantum Computing Resources into Scientific HPC Ecosystems
- Towards quantum computing for clinical trial design and optimization: A perspective on new opportunities and challenges
- Quantum-machine-assisted Drug Discovery
- Simulating spin biology using a digital quantum computer: Prospects on a near-term quantum hardware emulator
- Energy Landscape Structure of Small Graph Isomorphism Under Variational Optimization
- A Quantum Framework for Protein Binding-Site Structure Prediction on Utility-Level Quantum Processors
- RinQ: Towards predicting central sites in proteins on current quantum computers
- Quantum Annealing Algorithms for Estimating Ising Partition Functions
- Exploring Quantum Annealing for Coarse-Grained Protein Folding