Toward end-to-end quantum simulation for protein dynamics

• URL: http://arxiv.org/abs/2411.03972v2
• Date: Wed, 16 Apr 2025 06:20:01 GMT
• Title: Toward end-to-end quantum simulation for protein dynamics
• Authors: Zhenning Liu, Xiantao Li, Chunhao Wang, Jin-Peng Liu,
• Abstract summary: We systematically investigate end-to-end quantum algorithms for simulating various protein dynamics with effects.<n>For the read-out stage, our algorithms estimate a range of classical observables, including energy, low-frequency vibrational modes, density of states, displacement correlations, and optimal control parameters.<n>Overall, our study demonstrates that quantum simulation of protein dynamics represents a robust, end-to-end application for both early-stage and fully fault-tolerant quantum computing.
• Score: 5.65693337062667
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modeling and simulating the protein folding process overall remains a grand challenge in computational biology. We systematically investigate end-to-end quantum algorithms for simulating various protein dynamics with effects, such as mechanical forces or stochastic noises. A major focus is the read-in of system settings for simulation, for which we discuss (i) efficient quantum algorithms to prepare initial states--whether for ensemble or single-state simulations, in particular, the first efficient procedure for preparing Gaussian pseudo-random amplitude states, and (ii) the first efficient loading of the connectivity matrices of the protein structure. For the read-out stage, our algorithms estimate a range of classical observables, including energy, low-frequency vibrational modes, density of states, displacement correlations, and optimal control parameters. Between these stages, we simulate the dynamic evolution of the protein system, by using normal mode models--such as Gaussian network models (GNM) and all-atom normal mode models. In addition, we conduct classical numerical experiments focused on accurately estimating the density of states and applying optimal control to facilitate conformational changes. These experiments serve to validate our claims regarding potential quantum speedups. Overall, our study demonstrates that quantum simulation of protein dynamics represents a robust, end-to-end application for both early-stage and fully fault-tolerant quantum computing.

Related papers

• Demonstration of a variational quantum eigensolver with a solid-state spin system under ambient conditions [15.044543674753308]
  Quantum simulators offer the potential to utilize the quantum nature of a physical system to study another physical system. The variational-quantum-eigensolver algorithm is a particularly promising application for investigating molecular electronic structures. Spin-based solid-state qubits have the advantage of long decoherence time and high-fidelity quantum gates.
  arXiv  Detail & Related papers  (2024-07-23T09:17:06Z)
• Thermalization and Criticality on an Analog-Digital Quantum Simulator [133.58336306417294]
  We present a quantum simulator comprising 69 superconducting qubits which supports both universal quantum gates and high-fidelity analog evolution. We observe signatures of the classical Kosterlitz-Thouless phase transition, as well as strong deviations from Kibble-Zurek scaling predictions. We digitally prepare the system in pairwise-entangled dimer states and image the transport of energy and vorticity during thermalization.
  arXiv  Detail & Related papers  (2024-05-27T17:40:39Z)
• Low-Rank Variational Quantum Algorithm for the Dynamics of Open Quantum Systems [0.5755004576310334]
  A variational quantum algorithm is developed to simulate the real-time evolution of the density matrix governed by the Lindblad master equation. The algorithm encodes each pure state of the statistical mixture as a parametrized quantum circuit. Two variational Ans"atze are proposed, and their effectiveness is assessed in the simulation of the dynamics of a 2D dissipative transverse field Ising model.
  arXiv  Detail & Related papers  (2024-03-09T13:23:14Z)
• A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics [73.35846234413611]
  In drug discovery, molecular dynamics (MD) simulation provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. We propose NeuralMD, the first machine learning (ML) surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding dynamics. We demonstrate the efficiency and effectiveness of NeuralMD, achieving over 1K$times$ speedup compared to standard numerical MD simulations.
  arXiv  Detail & Related papers  (2024-01-26T09:35:17Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• Universality of critical dynamics with finite entanglement [68.8204255655161]
  We study how low-energy dynamics of quantum systems near criticality are modified by finite entanglement. Our result establishes the precise role played by entanglement in time-dependent critical phenomena.
  arXiv  Detail & Related papers  (2023-01-23T19:23:54Z)
• Probing finite-temperature observables in quantum simulators of spin systems with short-time dynamics [62.997667081978825]
  We show how finite-temperature observables can be obtained with an algorithm motivated from the Jarzynski equality. We show that a finite temperature phase transition in the long-range transverse field Ising model can be characterized in trapped ion quantum simulators.
  arXiv  Detail & Related papers  (2022-06-03T18:00:02Z)
• Benchmarking Quantum Simulators using Ergodic Quantum Dynamics [4.2392660892009255]
  We analyze a sample-efficient protocol to estimate the fidelity between an experimentally prepared state and an ideal state. We numerically demonstrate our protocol for a variety of quantum simulator platforms.
  arXiv  Detail & Related papers  (2022-05-24T17:18:18Z)
• Efficient Fully-Coherent Quantum Signal Processing Algorithms for Real-Time Dynamics Simulation [3.3917542048743865]
  We develop fully-coherent simulation algorithms based on quantum signal processing (QSP) We numerically analyze these algorithms by applying them to the simulation of spin dynamics of the Heisenberg model.
  arXiv  Detail & Related papers  (2021-10-21T17:56:33Z)
• Quantum algorithms for quantum dynamics: A performance study on the spin-boson model [68.8204255655161]
  Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator. variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware. We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
  arXiv  Detail & Related papers  (2021-08-09T18:00:05Z)
• Quantum-Classical Hybrid Algorithm for the Simulation of All-Electron Correlation [58.720142291102135]
  We present a novel hybrid-classical algorithm that computes a molecule's all-electron energy and properties on the classical computer. We demonstrate the ability of the quantum-classical hybrid algorithms to achieve chemically relevant results and accuracy on currently available quantum computers.
  arXiv  Detail & Related papers  (2021-06-22T18:00:00Z)
• Fixed Depth Hamiltonian Simulation via Cartan Decomposition [59.20417091220753]
  We present a constructive algorithm for generating quantum circuits with time-independent depth. We highlight our algorithm for special classes of models, including Anderson localization in one dimensional transverse field XY model. In addition to providing exact circuits for a broad set of spin and fermionic models, our algorithm provides broad analytic and numerical insight into optimal Hamiltonian simulations.
  arXiv  Detail & Related papers  (2021-04-01T19:06:00Z)
• Quantum Markov Chain Monte Carlo with Digital Dissipative Dynamics on Quantum Computers [52.77024349608834]
  We develop a digital quantum algorithm that simulates interaction with an environment using a small number of ancilla qubits. We evaluate the algorithm by simulating thermal states of the transverse Ising model.
  arXiv  Detail & Related papers  (2021-03-04T18:21:00Z)
• Randomizing multi-product formulas for Hamiltonian simulation [2.2049183478692584]
  We introduce a scheme for quantum simulation that unites the advantages of randomized compiling on the one hand and higher-order multi-product formulas on the other. Our framework reduces the circuit depth by circumventing the need for oblivious amplitude amplification. Our algorithms achieve a simulation error that shrinks exponentially with the circuit depth.
  arXiv  Detail & Related papers  (2021-01-19T19:00:23Z)
• Adaptive Variational Quantum Dynamics Simulations [3.629716738568079]
  We propose a general-purpose, self-adaptive approach to construct variational wavefunction ans"atze for highly accurate quantum dynamics simulations. We apply this approach to the integrable Lieb-Schultz-Mattis spin chain and the nonintegrable mixed-field Ising model. We envision that a wide range of dynamical simulations of quantum many-body systems on near-term quantum computing devices will be made possible through the AVQDS framework.
  arXiv  Detail & Related papers  (2020-11-01T20:21:57Z)
• Microcanonical and finite temperature ab initio molecular dynamics simulations on quantum computers [0.0]
  Ab initio molecular dynamics (AIMD) is a powerful tool to predict properties of molecular and condensed matter systems. We provide solutions for the alleviation of the statistical noise associated to the measurements of the expectation values of energies and forces. We also propose a Langevin dynamics algorithm for the simulation of canonical, i.e., constant temperature, dynamics.
  arXiv  Detail & Related papers  (2020-08-18T20:24:27Z)
• Realistic simulation of quantum computation using unitary and measurement channels [1.406995367117218]
  We introduce a new simulation approach that relies on approximating the density matrix evolution by a sum of unitary and measurement channels. This model shows an improvement of at least one order of magnitude in terms of accuracy compared to the best known approaches.
  arXiv  Detail & Related papers  (2020-05-13T14:29:18Z)
• Digital quantum simulation of molecular dynamics and control [0.0]
  We study how quantum computers could be employed to design optimally-shaped fields to control molecular systems. We introduce a hybrid algorithm that utilizes a quantum computer for simulating the field-induced quantum dynamics of a molecular system in time. Numerical illustrations are then presented that explicitly treat paradigmatic vibrational and rotational control problems.
  arXiv  Detail & Related papers  (2020-02-28T00:45:56Z)

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.