Using quantum annealing to design lattice proteins

• URL: http://arxiv.org/abs/2402.09069v1
• Date: Wed, 14 Feb 2024 10:28:43 GMT
• Title: Using quantum annealing to design lattice proteins
• Authors: Anders Irb\"ack, Lucas Knuthson, Sandipan Mohanty, Carsten Peterson
• Abstract summary: We demonstrate the fast and consistent identification of the correct HP model ground states using the D-Wave hybrid quantum-classical solver. An equally relevant biophysical challenge, called the protein design problem, is the inverse of the above. Here, we approach the design problem by a two-step procedure, implemented and executed on a D-Wave machine.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum annealing has shown promise for finding solutions to difficult optimization problems, including protein folding. Recently, we used the D-Wave Advantage quantum annealer to explore the folding problem in a coarse-grained lattice model, the HP model, in which amino acids are classified into two broad groups: hydrophobic (H) and polar (P). Using a set of 22 HP sequences with up to 64 amino acids, we demonstrated the fast and consistent identification of the correct HP model ground states using the D-Wave hybrid quantum-classical solver. An equally relevant biophysical challenge, called the protein design problem, is the inverse of the above, where the task is to predict protein sequences that fold to a given structure. Here, we approach the design problem by a two-step procedure, implemented and executed on a D-Wave machine. In the first step, we perform a pure sequence-space search by varying the type of amino acid at each sequence position, and seek sequences which minimize the HP-model energy of the target structure. After mapping this task onto an Ising spin glass representation, we employ a hybrid quantum-classical solver to deliver energy-optimal sequences for structures with 30-64 amino acids, with a 100% success rate. In the second step, we filter the optimized sequences from the first step according to their ability to fold to the intended structure. In addition, we try solving the sequence optimization problem using only the QPU, which confines us to sizes $\le$20, due to exponentially decreasing success rates. To shed light on the pure QPU results, we investigate the effects of control errors caused by an imperfect implementation of the intended Hamiltonian on the QPU, by numerically analyzing the Schr\"odinger equation. We find that the simulated success rates in the presence of control noise semi-quantitatively reproduce the modest pure QPU results for larger chains.

Related papers

• Increasing the Hardness of Posiform Planting Using Random QUBOs for Programmable Quantum Annealer Benchmarking [1.6385815610837167]
  We investigate making posiform planted QUBOs computationally harder by fusing many smaller random discrete coefficient spin-glass Ising models. We benchmark the capabilities of three D-Wave superconducting qubit quantum annealing processors. We find that the D-Wave QPU ground-state sampling success rate does not change with respect to the size of the random QUBOs we employ.
  arXiv  Detail & Related papers  (2024-11-06T02:46:33Z)
• Quantum Gate Generation in Two-Level Open Quantum Systems by Coherent and Incoherent Photons Found with Gradient Search [77.34726150561087]
  We consider an environment formed by incoherent photons as a resource for controlling open quantum systems via an incoherent control. We exploit a coherent control in the Hamiltonian and an incoherent control in the dissipator which induces the time-dependent decoherence rates.
  arXiv  Detail & Related papers  (2023-02-28T07:36:02Z)
• A self-consistent field approach for the variational quantum eigensolver: orbital optimization goes adaptive [52.77024349608834]
  We present a self consistent field approach (SCF) within the Adaptive Derivative-Assembled Problem-Assembled Ansatz Variational Eigensolver (ADAPTVQE) This framework is used for efficient quantum simulations of chemical systems on nearterm quantum computers.
  arXiv  Detail & Related papers  (2022-12-21T23:15:17Z)
• Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction [7.769624124148049]
  In this work, we apply deep reinforcement learning to the HP model for protein folding. We find that a DQN based on long short-term memory (LSTM) architecture greatly enhances the RL learning ability and significantly improves the search process. Experimentally we show that it can find multiple distinct best-known solutions per trial.
  arXiv  Detail & Related papers  (2022-11-27T21:17:48Z)
• Folding lattice proteins with quantum annealing [0.0]
  We develop a novel spin representation for lattice protein folding tailored for quantum annealing. With our encoding, the Hamiltonian by design has the quadratic structure required for calculations on an Ising-type annealer. Results are evaluated against existing exact results for HP chains with up to $N=30$ beads with 100% hit rate.
  arXiv  Detail & Related papers  (2022-05-12T13:30:57Z)
• On optimization of coherent and incoherent controls for two-level quantum systems [77.34726150561087]
  This article considers some control problems for closed and open two-level quantum systems. The closed system's dynamics is governed by the Schr"odinger equation with coherent control. The open system's dynamics is governed by the Gorini-Kossakowski-Sudarshan-Lindblad master equation.
  arXiv  Detail & Related papers  (2022-05-05T09:08:03Z)
• Quantum Deep Learning for Mutant COVID-19 Strain Prediction [22.182326473943004]
  Early prediction of possible variants (especially spike protein) of COVID-19 epidemic strains may lead to early prevention and treatment. We propose a development tool named DeepQuantum, and use this software to realize the goal of predicting spike protein variation structure. In addition, this hybrid quantum-classical model for the first time achieves quantum-inspired blur convolution.
  arXiv  Detail & Related papers  (2022-03-04T08:33:28Z)
• 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)
• Benchmarking adaptive variational quantum eigensolvers [63.277656713454284]
  We benchmark the accuracy of VQE and ADAPT-VQE to calculate the electronic ground states and potential energy curves. We find both methods provide good estimates of the energy and ground state. gradient-based optimization is more economical and delivers superior performance than analogous simulations carried out with gradient-frees.
  arXiv  Detail & Related papers  (2020-11-02T19:52:04Z)
• Quantum-optimal-control-inspired ansatz for variational quantum algorithms [105.54048699217668]
  A central component of variational quantum algorithms (VQA) is the state-preparation circuit, also known as ansatz or variational form. Here, we show that this approach is not always advantageous by introducing ans"atze that incorporate symmetry-breaking unitaries. This work constitutes a first step towards the development of a more general class of symmetry-breaking ans"atze with applications to physics and chemistry problems.
  arXiv  Detail & Related papers  (2020-08-03T18:00:05Z)
• MoG-VQE: Multiobjective genetic variational quantum eigensolver [0.0]
  Variational quantum eigensolver (VQE) emerged as a first practical algorithm for near-term quantum computers. Here, we propose the approach which can combine both low depth and improved precision. We observe nearly ten-fold reduction in the two-qubit gate counts as compared to the standard hardware-efficient ansatz.
  arXiv  Detail & Related papers  (2020-07-08T20:44:50Z)

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.