Efficient molecular conformation generation with quantum-inspired algorithm

• URL: http://arxiv.org/abs/2404.14101v1
• Date: Mon, 22 Apr 2024 11:40:08 GMT
• Title: Efficient molecular conformation generation with quantum-inspired algorithm
• Authors: Yunting Li, Xiaopeng Cui, Zhaoping Xiong, Zuoheng Zou, Bowen Liu, Bi-Ying Wang, Runqiu Shu, Huangjun Zhu, Nan Qiao, Man-Hong Yung,
• Abstract summary: We propose the use of quantum-inspired algorithm to solve the molecular unfolding (MU) problem. The root-mean-square deviation between the conformation determined by our approach and density functional theory (DFT) is negligible. Results indicate that quantum-inspired algorithms can be applied to solve practical problems even before quantum hardware become mature.
• Score: 4.625636280559916
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conformation generation, also known as molecular unfolding (MU), is a crucial step in structure-based drug design, remaining a challenging combinatorial optimization problem. Quantum annealing (QA) has shown great potential for solving certain combinatorial optimization problems over traditional classical methods such as simulated annealing (SA). However, a recent study showed that a 2000-qubit QA hardware was still unable to outperform SA for the MU problem. Here, we propose the use of quantum-inspired algorithm to solve the MU problem, in order to go beyond traditional SA. We introduce a highly-compact phase encoding method which can exponentially reduce the representation space, compared with the previous one-hot encoding method. For benchmarking, we tested this new approach on the public QM9 dataset generated by density functional theory (DFT). The root-mean-square deviation between the conformation determined by our approach and DFT is negligible (less than about 0.5 Angstrom), which underpins the validity of our approach. Furthermore, the median time-to-target metric can be reduced by a factor of five compared to SA. Additionally, we demonstrate a simulation experiment by MindQuantum using quantum approximate optimization algorithm (QAOA) to reach optimal results. These results indicate that quantum-inspired algorithms can be applied to solve practical problems even before quantum hardware become mature.

Related papers

• Performance Benchmarking of Quantum Algorithms for Hard Combinatorial Optimization Problems: A Comparative Study of non-FTQC Approaches [0.0]
  This study systematically benchmarks several non-fault-tolerant quantum computing algorithms across four distinct optimization problems. Our benchmark includes noisy intermediate-scale quantum (NISQ) algorithms, such as the variational quantum eigensolver. Our findings reveal that no single non-FTQC algorithm performs optimally across all problem types, underscoring the need for tailored algorithmic strategies.
  arXiv  Detail & Related papers  (2024-10-30T08:41:29Z)
• Optimization by Decoded Quantum Interferometry [43.55132675053983]
  We introduce a quantum algorithm for reducing classical optimization problems to classical decoding problems. We show that DQI achieves a better approximation ratio than any quantum-time classical algorithm known to us.
  arXiv  Detail & Related papers  (2024-08-15T17:47:42Z)
• A quantum annealing approach to the minimum distance problem of quantum codes [0.0]
  We introduce an approach to compute the minimum distance of quantum stabilizer codes by reformulating the problem as a Quadratic Unconstrained Binary Optimization problem. We demonstrate practical viability of our method by comparing the performance of purely classical algorithms with the D-Wave Advantage 4.1 quantum annealer.
  arXiv  Detail & Related papers  (2024-04-26T21:29:42Z)
• Variational Quantum Algorithms for the Allocation of Resources in a Cloud/Edge Architecture [1.072460284847973]
  We show that Variational Quantum Algorithms can be a viable alternative to classical algorithms in the near future. In particular, we compare the performances, in terms of success probability, of two algorithms, i.e., Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) The simulation experiments, performed for a set of simple problems, %CM230124 that involve a Cloud and two Edge nodes, show that the VQE algorithm ensures better performances when it is equipped with appropriate circuit textitansatzes that are able to restrict the search space
  arXiv  Detail & Related papers  (2024-01-25T17:37:40Z)
• Multi-sequence alignment using the Quantum Approximate Optimization Algorithm [0.0]
  We present a Hamiltonian formulation and implementation of the Multiple Sequence Alignment problem with the variational Quantum Approximate Optimization Algorithm (QAOA) We consider a small instance of our QAOA-MSA algorithm in both a quantum simulator and its performance on an actual quantum computer. While the ideal solution to the instance of MSA investigated is shown to be the most probable state sampled for a shallow p5 quantum circuit, the level of noise in current devices is still a formidable challenge.
  arXiv  Detail & Related papers  (2023-08-23T12:46:24Z)
• Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
  We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem. The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
  arXiv  Detail & Related papers  (2023-04-18T11:57:15Z)
• Adiabatic Quantum Computing for Multi Object Tracking [170.8716555363907]
  Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time. As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware. We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers.
  arXiv  Detail & Related papers  (2022-02-17T18:59:20Z)
• A Hybrid Quantum-Classical Algorithm for Robust Fitting [47.42391857319388]
  We propose a hybrid quantum-classical algorithm for robust fitting. Our core contribution is a novel robust fitting formulation that solves a sequence of integer programs. We present results obtained using an actual quantum computer.
  arXiv  Detail & Related papers  (2022-01-25T05:59:24Z)
• The Variational Quantum Eigensolver: a review of methods and best practices [3.628860803653535]
  The variational quantum eigensolver (or VQE) uses the variational principle to compute the ground state energy of a Hamiltonian. This review aims to provide an overview of the progress that has been made on the different parts of the algorithm.
  arXiv  Detail & Related papers  (2021-11-09T14:40:18Z)
• Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
  We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions. Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
  arXiv  Detail & Related papers  (2021-06-24T20:16:02Z)
• Error mitigation and quantum-assisted simulation in the error corrected regime [77.34726150561087]
  A standard approach to quantum computing is based on the idea of promoting a classically simulable and fault-tolerant set of operations. We show how the addition of noisy magic resources allows one to boost classical quasiprobability simulations of a quantum circuit.
  arXiv  Detail & Related papers  (2021-03-12T20:58:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.