Protein folding with an all-to-all trapped-ion quantum computer

• URL: http://arxiv.org/abs/2506.07866v2
• Date: Tue, 10 Jun 2025 22:46:38 GMT
• Title: Protein folding with an all-to-all trapped-ion quantum computer
• Authors: Sebastián V. Romero, Alejandro Gomez Cadavid, Pavle Nikačević, Enrique Solano, Narendra N. Hegade, Miguel Angel Lopez-Ruiz, Claudio Girotto, Masako Yamada, Panagiotis Kl. Barkoutsos, Ananth Kaushik, Martin Roetteler,
• Abstract summary: bias-field digitized counterdiabatic quantum optimization (BF-DCQO) algorithm is implemented on IonQ's fully connected trapped-ion quantum processors.<n>We tackle protein folding on a tetrahedral lattice for up to 12 amino acids, representing the largest quantum hardware implementations of protein folding problems reported to date.
• Score: 32.72482046729195
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We experimentally demonstrate that the bias-field digitized counterdiabatic quantum optimization (BF-DCQO) algorithm, implemented on IonQ's fully connected trapped-ion quantum processors, offers an efficient approach to solving dense higher-order unconstrained binary optimization (HUBO) problems. Specifically, we tackle protein folding on a tetrahedral lattice for up to 12 amino acids, representing the largest quantum hardware implementations of protein folding problems reported to date. Additionally, we address MAX 4-SAT instances at the computational phase transition and fully connected spin-glass problems using all 36 available qubits. Across all considered cases, our method consistently achieves optimal solutions, highlighting the powerful synergy between non-variational quantum optimization approaches and the intrinsic all-to-all connectivity of trapped-ion architectures. Given the expected scalability of trapped-ion quantum systems, BF-DCQO represents a promising pathway toward practical quantum advantage for dense HUBO problems with significant industrial and scientific relevance.

Related papers

• Distributed Variational Quantum Algorithm with Many-qubit for Optimization Challenges [0.25782420501870296]
  Existing quantum algorithms struggle with scalability and accuracy due to excessive reliance on entanglement.<n>We propose variational quantum optimization algorithm (VQOA), which leverages many-qubit (MQ) operations in an ansatz solely employing quantum superposition.<n>We also introduce distributed VQOA, which integrates high-performance computing with quantum computing to achieve superior performance across MQ systems and classical nodes.
  arXiv  Detail & Related papers  (2025-02-28T22:13:23Z)
• Solving the Traveling Salesman Problem via Different Quantum Computing Architectures [0.0]
  We study the application of emerging photonic and quantum computing architectures to solving the Traveling Salesman Problem (TSP)<n> Gate-based quantum computers demonstrated accurate results for small TSP instances in simulation.<n>Ising-based architectures show improved scalability for larger problem sizes.
  arXiv  Detail & Related papers  (2025-02-24T23:37:19Z)
• Solving Constrained Optimization Problems Using Hybrid Qubit-Qumode Quantum Devices [7.954263125127824]
  We show how to solve Quadratic Unconstrained Binary Optimization problems using hybrid qubit$qux2014$ devices.<n>The potential of hybrid quantum computers to tackle complex optimization problems in both academia and industry is highlighted.
  arXiv  Detail & Related papers  (2025-01-20T20:40:58Z)
• Bias-Field Digitized Counterdiabatic Quantum Algorithm for Higher-Order Binary Optimization [39.58317527488534]
  We present an enhanced bias-field digitized counterdiabatic quantum optimization (BF-DCQO) algorithm to address higher-order unconstrained binary optimization (HUBO) problems. Our protocol is experimentally validated using 156 qubits on an IBM quantum processor with a heavy-hex architecture.
  arXiv  Detail & Related papers  (2024-09-05T17:38:59Z)
• Bias-field digitized counterdiabatic quantum optimization [39.58317527488534]
  We call this protocol bias-field digitizeddiabatic quantum optimization (BF-DCQO) Our purely quantum approach eliminates the dependency on classical variational quantum algorithms. It achieves scaling improvements in ground state success probabilities, increasing by up to two orders of magnitude.
  arXiv  Detail & Related papers  (2024-05-22T18:11:42Z)
• 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)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• Squeezing and quantum approximate optimization [0.6562256987706128]
  Variational quantum algorithms offer fascinating prospects for the solution of optimization problems using digital quantum computers. However, the achievable performance in such algorithms and the role of quantum correlations therein remain unclear. We show numerically as well as on an IBM quantum chip how highly squeezed states are generated in a systematic procedure.
  arXiv  Detail & Related papers  (2022-05-20T18:00:06Z)
• 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)
• Realization of arbitrary doubly-controlled quantum phase gates [62.997667081978825]
  We introduce a high-fidelity gate set inspired by a proposal for near-term quantum advantage in optimization problems. By orchestrating coherent, multi-level control over three transmon qutrits, we synthesize a family of deterministic, continuous-angle quantum phase gates acting in the natural three-qubit computational basis.
  arXiv  Detail & Related papers  (2021-08-03T17:49:09Z)
• Polynomial unconstrained binary optimisation inspired by optical simulation [52.11703556419582]
  We propose an algorithm inspired by optical coherent Ising machines to solve the problem of unconstrained binary optimization. We benchmark the proposed algorithm against existing PUBO algorithms, and observe its superior performance. The application of our algorithm to protein folding and quantum chemistry problems sheds light on the shortcomings of approxing the electronic structure problem by a PUBO problem.
  arXiv  Detail & Related papers  (2021-06-24T16:39:31Z)
• 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.