arXiv subjects

Daniel Blankenberg

Publications and source records attributed to Daniel Blankenberg.

12 recordsLinked to original sources

arXiv2026

Logarithmic-scale variational quantum eigensolver for off-lattice protein structure prediction in continuous torsional angle space

Classical and current quantum approaches to protein structure prediction (QPSP) face limitations, notably massive qubit requirements restricting near-term models to simplistic on-lattice simulations. We propose a logarithmic-scale variational quantum eigensolver (VQE) that reduces qubit requirements for N torsional degrees of freedom to O(log2N), enabling off-lattice, all-atom simulations. Our architecture extracts molecular torsions from relative phases in statevector simulations. On quantum hardware, a decoder maps the empirical cumulative distribution function (CDF) from basis-state probabilities to bounded torsional variables. These feed a classical algorithm to build heavy-atom coordinates. We use an EfficientSU2 ansatz and multi-stage relaxation to mitigate barren plateaus. Structures are evaluated via a custom hybrid quantum-classical Hamiltonian, alongside Rosetta and OpenMM benchmarks. Evaluation on chignolin and Trp-cage yielded native-like conformations. Chignolin reached a 0.623 {\AA} C{\alpha} RMSD in retained snapshots and 1.199 {\AA} in final models; Trp-cage achieved a 2.501 {\AA} RMSD among snapshots (3.512 {\AA} in final models). Execution on IBM processors (ibm_cleveland, ibm_miami) successfully recovered native-like structures with a best RMSD of 1.758 {\AA}. The custom energy function performed best overall, though energy-ranking imbalances persisted across sampled landscapes for all functions. This introduces the first all-atom, continuous-space quantum algorithm for QPSP. By converting physical qubit constraints into circuit depth constraints, it proves high-resolution prediction is feasible with exponentially fewer qubits. Despite current limits like computational overhead and energy function sensitivity, it establishes a scalable foundation for hybrid quantum biophysics.

quant-ph↗

arXiv2026

InterQ: Communication-Aware Scheduling Across Modular QPUs with Classical and Quantum Links

As quantum computing scales toward practical workloads, future systems are expected to move beyond single monolithic processors toward modular architectures that connect multiple QPUs. Different platforms realize this modularity through different communication models: superconducting systems rely on real-time classical links and dynamic-circuit coordination, trapped-ion systems use photonic interconnects for remote entanglement, and neutral-atom systems provide strong intra-core connectivity with proposed optical links for inter-core communication. This heterogeneity makes communication-aware scheduling essential for shared modular quantum cloud environments. We present InterQ, a communication-aware scheduler for modular QPU architectures with heterogeneous communication models. InterQ jointly considers qubit capacity, placement, parallel execution, and communication-driven dependencies across distributed subcircuits, while enabling adaptive circuit cutting to reduce makespan while balancing fidelity and communication overhead. The framework distinguishes classical-link execution, where measurement and feedforward impose synchronization constraints, from quantum-link execution, where entanglement distribution and state transfer determine coordination cost. Using a unified simulation framework to compare superconducting, trapped-ion, and neutral-atom modular systems, InterQ shows how communication models and scheduler-driven cutting decisions affect throughput, latency, and fidelity. Across evaluated workloads, InterQ exposes an architecture-dependent tradeoff: neutral-atom modular QPUs achieve the highest fidelity, superconducting systems minimize runtime, and trapped-ion systems provide a balanced intermediate profile across fidelity and makespan.

quant-ph↗

arXiv2026

QuMod: Parallel Quantum Job Scheduling on Modular QPUs using Circuit Cutting

The quantum computing community is increasingly positioning quantum processors as accelerators within classical HPC workflows, analogous to GPUs and TPUs. However, many real-world applications require scaling to hundreds or thousands of physical qubits to realize logical qubits via error correction. To reach these scales, hardware vendors employing diverse technologies -- such as trapped ions, photonics, neutral atoms, and superconducting circuits -- are moving beyond single, monolithic QPUs toward modular architectures connected via interconnects. For example, IonQ has proposed photonic links for scaling, while IBM has demonstrated a modular QPU architecture by classically linking two 127-qubit devices. Using dynamic circuits, Bell-pair-based teleportation, and circuit cutting, they have shown how to execute a large quantum circuit that cannot fit on a single QPU. As interest in quantum computing grows, cloud providers must ensure fair and efficient resource allocation for multiple users sharing such modular systems. Classical interconnection of QPUs introduces new scheduling challenges, particularly when multiple jobs execute in parallel. In this work, we develop a multi-programmable scheduler for modular quantum systems that jointly considers qubit mapping, parallel circuit execution, measurement synchronization across subcircuits, and teleportation operations between QPUs using dynamic circuits.

quant-ph↗

arXiv2026

An Automatic Pipeline for the Integration of Python-Based Tools into the Galaxy Platform: Application to the anvi'o Framework

The integration of command-line tools into the Galaxy platform is crucial for making complex computational methods accessible to a broader audience and ensuring reproducible research. However, the manual development of tool wrappers is a time-consuming, error-prone, and knowledge-intensive process. This bottleneck significantly affects the rapid deployment of new and updated tools, creating a gap between tool development and its availability to the scientific community. We have developed a novel, automated approach that directly translates Python tool interfaces into Galaxy-compliant tool wrappers. Our method leverages the argparse library, a standard for command-line argument parsing in Python. By embedding structured metadata within the metavar attribute of input and output arguments, our system programmatically parses the tool's interface to extract all necessary information. This includes parameter types, data formats, help text, and input/output definitions. The system then uses this information to automatically generate a complete and valid Galaxy tool XML wrapper, requiring no manual intervention. To validate the scalability and effectiveness of our approach, we applied it to the anvi'o framework, a comprehensive and complex bioinformatics platform comprising hundreds of individual programs. Our method successfully parsed the argparse definitions for the entire anvi'o suite and generated functional Galaxy tool wrappers. The resulting integration allows for the seamless execution of anvi'o workflows within the Galaxy environment. This work presents a significant advancement in the automation of tool integration for scientific workflow systems. By establishing a convention-based approach using Python's argparse library, we have created a scalable and generalizable solution that dramatically reduces the effort required to make command-line tools available in Galaxy.

cs.SC↗

arXiv2026

hdlib 2.0: Extending Machine Learning Capabilities of Vector-Symbolic Architectures

Following the initial publication of hdlib, a Python library for designing Vector-Symbolic Architectures (VSA), we introduce a major extension that significantly enhances its machine learning capabilities. VSA, also known as Hyperdimensional Computing, is a computing paradigm that represents and processes information using high-dimensional vectors. While the first version of hdlib established a robust foundation for creating and manipulating these vectors, this update addresses the growing need for more advanced, data-driven modeling within the VSA framework. Here, we present four extensions: significant enhancements to the existing supervised classification model also enabling feature selection, and a new regression model for predicting continuous variables, a clustering model for unsupervised learning, and a graph-based learning model. Furthermore, we propose the first implementation ever of Quantum Hyperdimensional Computing with quantum-powered arithmetic operations and a new Quantum Machine Learning model for supervised learning. hdlib remains open-source and available on GitHub at https://github.com/cumbof/hdlib under the MIT license, and distributed through the Python Package Index (pip install hdlib) and Conda (conda install -c conda-forge hdlib). Documentation and examples of these new features are available on the official Wiki at https://github.com/cumbof/hdlib/wiki.

cs.LG↗

arXiv2025

Quantum Hyperdimensional Computing: a foundational paradigm for quantum neuromorphic architectures

A significant challenge in quantum computing (QC) is developing learning models that truly align with quantum principles, as many current approaches are complex adaptations of classical frameworks. In this work, we introduce Quantum Hyperdimensional Computing (QHDC), a fundamentally new paradigm. We demonstrate that the core operations of its classical counterpart, Hyperdimensional Computing (HDC), a brain-inspired model, map with remarkable elegance and direct correspondence onto the native operations of a QC. This suggests HDC is exceptionally well-suited for a quantum-native implementation. We establish a direct, resource-efficient mapping: (i) hypervectors are mapped to quantum states, (ii) the bundling operation is implemented as a quantum-native averaging process using a Linear Combination of Unitaries (LCU) and Oblivious Amplitude Amplification (OAA), (iii) the binding operation is realized via quantum phase oracles, (iv) the permutation operation is implemented using the Quantum Fourier Transform (QFT), and (v) vector similarity is calculated using quantum state fidelity measurements based on the Hadamard Test. We present the first-ever implementation of this framework, validated through symbolic analogical reasoning and supervised classification tasks. The viability of QHDC is rigorously assessed via a comparative analysis of results from classical computation, ideal quantum simulation, and execution of a 156-qubit IBM Heron r3 quantum processor. Our results validate the proposed mappings and demonstrate the versatility of the framework, establishing QHDC as a physically realizable technology. This work lays the foundation for a new class of quantum neuromorphic algorithms and opens a promising avenue for tackling complex cognitive and biomedical problems intractable for classical systems.

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.

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.

quant-ph↗

arXiv2025

Quantum machine learning framework for longitudinal biomedical studies

Longitudinal biomedical studies play a vital role in tracking disease progression, treatment response, and the emergence of resistance mechanisms, particularly in complex disorders such as cancer and neurodegenerative diseases. However, the high dimensionality of biological data, combined with the limited size of longitudinal cohorts, presents significant challenges for traditional machine learning approaches. In this work, we explore the potential of quantum machine learning (QML) for longitudinal biomarker discovery. We propose a novel modification to the instantaneous quantum polynomial time (IQP) feature map, designed to encode temporal dependencies across multiple time points in biomedical datasets. Through numerical simulations on both synthetic and real-world datasets - including studies on follicular lymphoma and Alzheimer's disease - we demonstrate that our longitudinal IQP feature map improves the ability of quantum kernels to capture intra-subject temporal patterns, offering a promising direction for QML in clinical research.

q-bio.QM↗

arXiv2024

How quantum computing can enhance biomarker discovery

Biomarkers play a central role in medicine's gradual progress towards proactive, personalized precision diagnostics and interventions. However, finding biomarkers that provide very early indicators of a change in health status, for example for multi-factorial diseases, has been challenging. Discovery of such biomarkers stands to benefit significantly from advanced information processing and means to detect complex correlations, which quantum computing offers. In this perspective paper, quantum algorithms, particularly in machine learning, are mapped to key applications in biomarker discovery. The opportunities and challenges associated with the algorithms and applications are discussed. The analysis is structured according to different data types - multi-dimensional, time series, and erroneous data - and covers key data modalities in healthcare - electronic health records (EHRs), omics, and medical images. An outlook is provided concerning open research challenges.

q-bio.OT↗

arXiv2024

Towards quantum computing for clinical trial design and optimization: A perspective on new opportunities and challenges

Clinical trials are pivotal in the drug discovery process to determine the safety and efficacy of a drug candidate. The high failure rates of these trials are attributed to deficiencies in clinical model development and protocol design. Improvements in the clinical drug design process could therefore yield significant benefits for all stakeholders involved. This paper examines the current challenges faced in clinical trial design and optimization, reviews established classical computational approaches, and introduces quantum algorithms aimed at enhancing these processes. Specifically, the focus is on three critical aspects: clinical trial simulations, site selection, and cohort identification. This study aims to provide a comprehensive framework that leverages quantum computing to innovate and refine the efficiency and effectiveness of clinical trials.

quant-ph↗

arXiv2023

A perspective on protein structure prediction using quantum computers

Despite the recent advancements by deep learning methods such as AlphaFold2, \textit{in silico} protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whether quantum computers can offer some meaningful benefits for approaching this problem. Yet, identifying specific problem instances amenable to quantum advantage, and estimating quantum resources required are equally challenging tasks. Here, we share our perspective on how to create a framework for systematically selecting protein structure prediction problems that are amenable for quantum advantage, and estimate quantum resources for such problems on a utility-scale quantum computer. As a proof-of-concept, we validate our problem selection framework by accurately predicting the structure of a catalytic loop of the Zika Virus NS3 Helicase, on quantum hardware.

quant-ph↗