Designing lattice proteins with variational quantum algorithms

• URL: http://arxiv.org/abs/2508.02369v1
• Date: Mon, 04 Aug 2025 12:55:01 GMT
• Title: Designing lattice proteins with variational quantum algorithms
• Authors: Hanna Linn, Lucas Knuthson, Anders Irbäck, Sandipan Mohanty, Laura García-Álvarez, Göran Johansson,
• Abstract summary: In inverse problem, protein design, one seeks sequences that fold to a given target structure.<n>Here, we investigate the utility of variational quantum algorithms for the first of these two steps on today's noisy quantum devices.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum heuristics have shown promise in solving various optimization problems, including lattice protein folding. Equally relevant is the inverse problem, protein design, where one seeks sequences that fold to a given target structure. The latter problem is often split into two steps: (i) searching for sequences that minimize the energy in the target structure, and (ii) testing whether the generated sequences fold to the desired structure. Here, we investigate the utility of variational quantum algorithms for the first of these two steps on today's noisy intermediate-scale quantum devices. We focus on the sequence optimization task, which is less resource-demanding than folding computations. We test the quantum approximate optimization algorithm and variants of it, with problem-informed quantum circuits, as well as the hardware-efficient ansatz, with problem-agnostic quantum circuits. While the former algorithms yield acceptable results in noiseless simulations, their performance drops under noise. With the problem-agnostic circuits, which are more compatible with hardware constraints, an improved performance is observed in both noisy and noiseless simulations. However, the results deteriorate when running on a real quantum device. We attribute this discrepancy to features not captured by the simulated noise model, such as the temporal aspect of the hardware noise.

Related papers

• Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise [49.97673761305336]
  Noise remains a major obstacle to achieving reliable quantum algorithms.<n>We present a provably noise-resilient training theory and algorithm to enhance the robustness of parameterized quantum circuit classifiers.
  arXiv  Detail & Related papers  (2025-05-24T02:51:34Z)
• Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
  Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems. In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems. We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
  arXiv  Detail & Related papers  (2024-04-17T11:00:12Z)
• Noise-induced transition in optimal solutions of variational quantum algorithms [0.0]
  Variational quantum algorithms are promising candidates for delivering practical quantum advantage on noisy quantum hardware. We study the effect of noise on optimization by studying a variational quantum eigensolver (VQE) algorithm calculating the ground state of a spin chain model.
  arXiv  Detail & Related papers  (2024-03-05T08:31:49Z)
• Optimized Noise Suppression for Quantum Circuits [0.40964539027092917]
  Crosstalk noise is a severe error source in, e.g., cross-resonance based superconducting quantum processors. Intrepid programming algorithm extends previous work on optimized qubit routing by swap insertion. We evaluate the proposed method by characterizing crosstalk noise for two chips with up to 127 qubits.
  arXiv  Detail & Related papers  (2024-01-12T07:34:59Z)
• Simulating photonic devices with noisy optical elements [0.615738282053772]
  In the near-term, the performance of any quantum algorithm should be tested and simulated in the presence of noise. We apply the recently proposed noisy gates approach to efficiently simulate noisy optical circuits. We also evaluate the performance of a photonic variational quantum algorithm to solve the MAX-2-CUT problem.
  arXiv  Detail & Related papers  (2023-11-17T16:06:20Z)
• Scalable noisy quantum circuits for biased-noise qubits [37.69303106863453]
  We consider biased-noise qubits affected only by bit-flip errors, which is motivated by existing systems of stabilized cat qubits. For realistic noise models, phase-flip will not be negligible, but in the Pauli-Twirling approximation, we show that our benchmark could check the correctness of circuits containing up to $106$ gates.
  arXiv  Detail & Related papers  (2023-05-03T11:27:50Z)
• Limitations of variational quantum algorithms: a quantum optimal transport approach [11.202435939275675]
  We obtain extremely tight bounds for standard NISQ proposals in both the noisy and noiseless regimes. The bounds limit the performance of both circuit model algorithms, such as QAOA, and also continuous-time algorithms, such as quantum annealing.
  arXiv  Detail & Related papers  (2022-04-07T13:58:44Z)
• Optimization and Noise Analysis of the Quantum Algorithm for Solving One-Dimensional Poisson Equation [17.65730040410185]
  We propose an efficient quantum algorithm for solving one-dimensional Poisson equation. We further develop this algorithm to make it closer to the real application on the noisy intermediate-scale quantum (NISQ) devices. We analyze the effect of common noise existing in the real quantum devices on our algorithm using the IBM Qiskit toolkit.
  arXiv  Detail & Related papers  (2021-08-27T09:44:41Z)
• 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)
• 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)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)
• Space-efficient binary optimization for variational computing [68.8204255655161]
  We show that it is possible to greatly reduce the number of qubits needed for the Traveling Salesman Problem. We also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models.
  arXiv  Detail & Related papers  (2020-09-15T18:17:27Z)

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.