Protein folding poses a significant challenge in biology due to its combinatorial nature and NP-completeness, making it a prime candidate for quantum computing solutions. Quantum computers offer the potential to expedite protein folding predictions by leveraging superposition and entanglement properties for efficient exploration of protein structure space. This study focuses on enhancing peptide folding predictions through an innovative Ansatz circuit design incorporating T-gates alongside Clifford gates to reduce noise in quantum algorithms. The study benchmarks existing tools like MD simulation and stochastic techniques against the Variational Quantum Eigensolver (VQE) using quantum simulators and QPUs. Detailed qubit configurations and peptide folding processes are outlined, highlighting the use of efficient quantum structures and circuit designs. The experimental setup includes running the circuit for multiple replicas to analyze error rates and energy minimization for different peptide sequences. The results demonstrate a significant difference in gate performance between Clifford gates alone and Clifford + T gates, with T gates leading to reduced noise and improved performance. Statistical analyses reveal a negative mean difference and a high effect size, emphasizing the impact of T gates on noise reduction in quantum algorithms for peptide folding predictions. The study showcases consistent results across multiple replicas, indicating the reliability and efficiency of the proposed Ansatz circuit design. The study highlights the importance of gate sequencing and optimization in quantum computing applications for protein structure prediction. Future directions include expanding the model to larger proteins, refining ansatz circuits, and collaborating with experts to advance quantum algorithms for complex biological systems.
Protein folding poses a significant challenge in biology due to its combinatorial nature and NP-completeness, making it a prime candidate for quantum computing solutions. Quantum computers offer the potential to expedite protein folding predictions by leveraging superposition and entanglement properties for efficient exploration of protein structure space. This study focuses on enhancing peptide folding predictions through an innovative Ansatz circuit design incorporating T-gates alongside Clifford gates to reduce noise in quantum algorithms. The study benchmarks existing tools like MD simulation and stochastic techniques against the Variational Quantum Eigensolver (VQE) using quantum simulators and QPUs. Detailed qubit configurations and peptide folding processes are outlined, highlighting the use of efficient quantum structures and circuit designs. The experimental setup includes running the circuit for multiple replicas to analyze error rates and energy minimization for different peptide sequences. The results demonstrate a significant difference in gate performance between Clifford gates alone and Clifford + T gates, with T gates leading to reduced noise and improved performance. Statistical analyses reveal a negative mean difference and a high effect size, emphasizing the impact of T gates on noise reduction in quantum algorithms for peptide folding predictions. The study showcases consistent results across multiple replicas, indicating the reliability and efficiency of the proposed Ansatz circuit design. The study highlights the importance of gate sequencing and optimization in quantum computing applications for protein structure prediction. Future directions include expanding the model to larger proteins, refining ansatz circuits, and collaborating with experts to advance quantum algorithms for complex biological systems.
Protein structure prediction remains central to computational biology and drug discovery. While deep learning models such as AlphaFold have achieved remarkable accuracy for known fold families, they struggle with novel folds, intrinsically disordered regions, and conformational dynamics, and they do not explicitly optimize physics-based energy functions. We present a quantum-assisted approach to protein conformation optimization that uses parameterized quantum circuits within a variational framework to explore the energy landscape of small proteins and fragments from first-principles or coarse-grained physics. Our method encodes backbone torsion angles (ϕ, ψ) and selected side-chain degrees of freedom into qubit registers and employs a variational quantum eigensolver (VQE)-inspired optimization to minimize an approximate Hamiltonian incorporating bonded interactions, sterics, hydrogen bonding, and hydrophobic contacts. We introduce problem-inspired ansätze that respect chain connectivity and secondary-structure motifs (helices, beta-sheets) and propose hierarchical circuit growth strategies for scaling from peptides to larger domains. Through classical simulations of small peptides and secondary-structure motifs, we demonstrate that quantum variational optimization can identify low-energy conformations consistent with experimentally determined structures. We provide explicit resource estimates for NISQ and early fault-tolerant quantum computers and discuss hybrid classical-quantum strategies for refining quantum solutions with classical molecular dynamics and machinelearning priors. While full protein-folding quantum advantage remains distant, we identify concrete subproblems where nearterm quantum devices offer complementary physics-based insight beyond data-driven approaches, opening pathways toward practical quantum-classical protein structure and drug discovery pipelines.
Protein folding is a computationally intensive problem in bioinformatics that involves predicting the three-dimensional structure of a protein from its amino acid sequence. Classical approaches face challenges due to the exponential complexity of the search space. In this paper, we propose Q-ProteinFold, a quantum-classical hybrid system that leverages the Variational Quantum Eigensolver (VQE) for protein folding approximation. The system encodes protein sequences into parameterized quantum circuits using Qiskit and explores multiple conformations through quantum superposition and entanglement. A classical optimizer iteratively minimizes the energy function to identify stable protein structures. The implementation is developed using Python, Streamlit, and Qiskit Aer simulator. Experimental results demonstrate energy convergence, probabilistic state distribution, and effective fold prediction. This work highlights the potential of quantum computing in solving complex biological optimization problems.
Protein folding is a fundamental process crucial for the functionality of biological molecules. Despite its significance, predicting protein structures accurately remains a challenging task due to the complex nature of folding pathways and interactions. In this study, we explore the application of quantum computing, specifically error mitigated VQE, in investigating the folding of disordered regions in Ubiquitin C. By integrating advanced simulation techniques and quantum algorithms, we aim to unravel the intricate dynamics of protein folding at a molecular level. We employ a combination of molecular dynamics simulations and quantum VQE algorithms to analyze the folding kinetics and stability of C-terminal region of Ubiquitin C. Utilizing state-of-the-art quantum simulators and computational tools, we track the evolution of protein conformations and assess ground state energy values to elucidate the folding process. Our results demonstrate the effectiveness of error mitigated VQE in providing accurate ground state energy values compared to traditional methods like MD simulations with difference less than -0.91 kcal/mol. The analysis reveals insights into the structural transitions and stability of Ubiquitin C during the folding process, shedding light on key interactions and conformational changes. This study underscores the potential of quantum computing in advancing our understanding of protein folding dynamics.
Abstract Predicting the three-dimensional structure of a protein from its primary sequence of amino acids is known as the protein folding problem. Due to the central role of proteins’ structures in chemistry, biology and medicine applications, this subject has been intensively studied for over half a century. Although classical algorithms provide practical solutions for the sampling of the conformation space of small proteins, they cannot tackle the intrinsic NP-hard complexity of the problem, even when reduced to the simplest Hydrophobic-Polar model. On the other hand, while fault-tolerant quantum computers are beyond reach for state-of-the-art quantum technologies, there is evidence that quantum algorithms can be successfully used in noisy state-of-the-art quantum computers to accelerate energy optimization in frustrated systems. In this work, we present a model Hamiltonian with O(N4) O ( N 4 ) scaling and a corresponding quantum variational algorithm for the folding of a polymer chain with N monomers on a lattice. The model reflects many physico-chemical properties of the protein, reducing the gap between coarse-grained representations and mere lattice models. In addition, we use a robust and versatile optimization scheme, bringing together variational quantum algorithms specifically adapted to classical cost functions and evolutionary strategies to simulate the folding of the 10 amino acid Angiotensin on 22 qubits. The same method is also successfully applied to the study of the folding of a 7 amino acid neuropeptide using 9 qubits on an IBM 20-qubit quantum computer. Bringing together recent advances in building gate-based quantum computers with noise-tolerant hybrid quantum-classical algorithms, this work paves the way towards accessible and relevant scientific experiments on real quantum processors.
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 ≥ 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 ( ≥ 0.2 ) in most instances.
Abstract Quantum computing in biology is one of the most rapidly evolving field of technology. Protein folding is one of the key challenges which requires accurate and efficient algorithms with a quick computational time. Structural conformations of proteins with disordered regions need colossal amount of computational resource to map its least energy conformation state. In this regard, quantum algorithms like Variational quantum eigensolver (VQE) are applied in the current research work to predict the lowest energy value of 50 peptides of 7 amino acids each. VQE is initially used to calculate the energy values over which Variational Quantum Optimization is applied via Conditional Value at Risk (CVaR) over 100 iterations of 500000 shots each to obtain least ground state energy value. This is compared to the molecular dynamics-based simulations of 50 nanoseconds each to calculate the energy values along with the folding pattern. The results suggest efficient folding outcomes from CvaR-VQE compared to MD based simulations. With the ever-expanding quantum hardware and improving algorithms the problem of protein folding can be resolved to obtain in depth insights on the biological process and drug design.