arXiv subjects

Hanna Linn

Publications and source records attributed to Hanna Linn.

6 recordsLinked to original sources

arXiv2026

Protein folding on a 64 qubit trapped-ion hardware via counterdiabatic quantum optimization

We report the largest trapped-ion hardware demonstration of lattice protein-folding optimization to date, using bias-field digitized counterdiabatic quantum optimization (BF-DCQO) on a fully connected 64-qubit Barium development system similar to the forthcoming IonQ Tempo line. Six peptide sequences with 14-16 amino-acid residues are encoded using a coarse-grained tetrahedral lattice model, yielding higher-order spin-glass Hamiltonians with long-range interactions involving up to five-body terms and mapped to 46-61 qubits. The resulting instances are demanding for near-term quantum hardware because low-energy configurations must satisfy backbone-geometry constraints while optimizing dense residue-contact interactions. BF-DCQO uses a non-variational bias-feedback mechanism, where low-energy samples from each round define longitudinal fields that guide subsequent quantum evolutions. Across the studied instances, BF-DCQO shifts raw sampled energy distributions toward lower energies than uniform random sampling, with the strongest improvements appearing in residue-contact variables. To preserve this signal, we introduce a consensus-based post-processing pipeline that combines quantum-learned contact information with feasible backbone geometries. The resulting hybrid workflow reaches the classical reference energy in multiple instances and improves over the corresponding random-seeded pipeline. These results show that BF-DCQO can generate structured samples for dense protein-folding Hamiltonians at previously unexplored trapped-ion scales.

quant-ph↗

arXiv2025

Efficient Quantum Protein Structure Prediction with Problem-Agnostic Ansatzes

Accurately predicting protein structures from amino acid sequences remains a fundamental challenge in computational biology, with profound implications for understanding biological functions and enabling structure-based drug discovery. Quantum computing approaches based on coarse-grained lattice models combined with variational algorithms have been proposed as an initial step towards predicting protein structures using quantum computers. In this work, we introduce a more efficient quantum protein structure prediction workflow that bypasses the need for explicit Hamiltonian construction by employing a problem-agnostic ansatz. The ansatz is trained to minimize an energy-based cost function that can be efficiently computed on classical computers, eliminating the need for ancillary qubits and reducing circuit depth compared to previous Hamiltonian-based methods. This enables a more scalable approach for larger proteins and facilitates the inclusion of higher-order interactions, previously hard to achieve in quantum approaches. We validate our method by benchmarking a hardware-efficient ansatz on a large set of proteins with up to 26 amino acids, modeled on the tetrahedral, body-centered cubic, and face-centered cubic lattices, incorporating up to second-nearest-neighbor interactions. We assess the performance on both a noise-free simulator and the ibm_kingston quantum computer using a set of distinct metrics to probe different aspects of the prediction quality. These experiments push the boundaries of quantum methods for protein structure prediction, targeting sequences that are longer than those typically addressed in prior studies. Overall, the results highlight the scalability and versatility of our approach, while also identifying key areas for improvement to inform future algorithm development and hardware advancements.

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arXiv2025

Designing lattice proteins with variational quantum algorithms

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 approach, with a careful choice of mixer, yields good results in noiseless simulations (success probability $\geq$0.95 in all instances), its performance drops drastically under noise. With the problem-agnostic circuits, which are more compatible with hardware constraints, improved performance is observed in noisy simulations, compared to their problem-informed counterparts. When running the problem-agnostic circuits on a real quantum device, with parameters taken from simulations, we obtain a significant success probability ($\geq$0.2) in most instances.

quant-ph↗

arXiv2025

Quantum Algorithm for Protein Structure Prediction Using the Face-Centered Cubic Lattice

In this work, we present the first implementation of the face-centered cubic (FCC) lattice model for protein structure prediction with a quantum algorithm. Our motivation to encode the FCC lattice stems from our observation that the FCC lattice is more capable in terms of modeling realistic secondary structures in proteins compared to other lattices, as demonstrated using root mean square deviation (RMSD). We utilize two quantum methods to solve this problem: a polynomial fitting approach (PolyFit) and the Variational Quantum Eigensolver with constraints (VQEC) based on the Lagrangian duality principle. Both methods are successfully deployed on Eagle R3 (ibm_cleveland) and Heron R2 (ibm_kingston) quantum computers, where we are able to recover ground state configurations for the 6-amino acid sequence KLVFFA under noise. A comparative analysis of the outcomes generated by the two QPUs reveals a significant enhancement (reaching nearly a two-fold improvement for PolyFit and a three-fold improvement for VQEC) in the prediction and sampling of the optimal solution (ground state conformations) on the newer Heron R2 architecture, highlighting the impact of quantum hardware advancements for this application.

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arXiv2023

Resource analysis of quantum algorithms for coarse-grained protein folding models

Protein folding processes are a vital aspect of molecular biology that is hard to simulate with conventional computers. Quantum algorithms have been proven superior for certain problems and may help tackle this complex life science challenge. We analyze the resource requirements for simulating protein folding on a quantum computer, assessing this problem's feasibility in the current and near-future technological landscape. We calculate the minimum number of qubits, interactions, and two-qubit gates necessary to build a heuristic quantum algorithm with the specific information of a folding problem. Particularly, we focus on the resources needed to build quantum operations based on the Hamiltonian linked to the protein folding models for a given amino acid count. Such operations are a fundamental component of these quantum algorithms, guiding the evolution of the quantum state for efficient computations. Specifically, we study course-grained folding models on the lattice and the fixed backbone side-chain conformation model and assess their compatibility with the constraints of existing quantum hardware given different bit-encodings. We conclude that the number of qubits required falls within current technological capabilities. However, the limiting factor is the high number of interactions in the Hamiltonian, resulting in a quantum gate count unavailable today.

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arXiv2023

Detecting quantum speedup of random walks with machine learning

We explore the use of machine-learning techniques to detect quantum speedup in random walks on graphs. Specifically, we investigate the performance of three different neural-network architectures (variations on fully connected and convolutional neural networks) for identifying linear, cyclic, and random graphs that yield quantum speedups in terms of the hitting time for reaching a target node after starting in another node of the graph. Our results indicate that carefully building the data set for training can improve the performance of the neural networks, but all architectures we test struggle to classify large random graphs and generalize from training on one graph size to testing on another. If classification accuracy can be improved further, valuable insights about quantum advantage may be gleaned from these neural networks, not only for random walks, but more generally for quantum computing and quantum transport.

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