Quantum Computing Algorithms and Architecture

Variational quantum thermalization of environment-coupled DNA sequence biases

The paper uses variational quantum simulation to model DNA sequence biases under thermal environments, showing both classical and quantum methods enhance target sequence probabilities.

An inspectable Grover algorithm-based amplitude-amplification workflow for k-mer discovery and motif scanning with genomic coordinate traceability

MotifQu uses Grover's algorithm for motif discovery and scanning, showing feasibility on real genomes but limited amplitude concentration on current quantum hardware.

Scaling Quantum Optimisation Beyond Hardware Limits for Real-World Scientific Workloads: Genome Assembly on Current Quantum Hardware

This paper uses quantum computing to assemble a large genome on current hardware, achieving high accuracy despite technical limitations.

Quantum Encoding Strategies for Drug Response Prediction: An Exhaustive Benchmark on a 20-Qubit Superconducting QPU

The paper benchmarks twelve quantum encoding strategies for drug response prediction on a 20-qubit QPU, finding that a QAOA-inspired encoding achieves the best RMSE.

Quantum implementation of multi-pattern string matching for k-mer detection

This paper presents two quantum algorithms for efficient k-mer detection in genomic data using Grover's amplitude amplification, showing potential for accelerating pattern matching in large datasets.

Entropy Quantum Computing for Fixed-Backbone Protein Design

This paper applies entropy quantum computing to protein design, showing photonic solvers outperform classical methods for large instances.

Repeatable quantum-hardware execution of a fast local-topology surrogate for hyperthermal sarcomeric oscillations

This paper maps hyperthermal sarcomeric oscillations to a quantum hardware model and demonstrates repeatable execution of biologically meaningful observables, showing constrained local reconfiguration rather than maximal mixing.

A Pipeline for Solving Edge-Matching Puzzles and Their Implications for Protein Folding

The paper develops a computational pipeline to solve complex edge-matching puzzles, demonstrating its effectiveness on a puzzle comparable to Eternity II and drawing parallels to protein folding challenges.

Quantum Algorithm for Metabolic Network Analysis

This paper introduces a quantum algorithm for analyzing metabolic networks, demonstrating its effectiveness on glycolysis and TCA cycle networks.

Quantum kernel support vector machines for trabecular bone classification: comparing feature reduction strategies on synthetic micro-CT data

This paper compares dimensionality reduction strategies for quantum kernel SVMs on synthetic micro-CT trabecular bone data, finding UMAP maintains competitiveness while linear methods show quantum deficits.

Teleport-Stabilized Quantum-Walk Ranking in Near-Tie Neoantigen Regimes

The paper introduces a quantum-walk-based ranking method to stabilize neoantigen selection in near-tie scenarios by leveraging graph structure and coherence dynamics.

Spikes meet Spins: Quantum-Native Neural Decoding for Ultra_Low-Latency Brain-Computer Interfaces

This paper demonstrates a quantum computing approach for neural decoding with high accuracy and ultra-low latency using a photonic Ising machine.

Systematic Data-Driven Penalty Calibration for Constrained Quantum Optimization with Application to Molecular Docking

This paper introduces MMP, a framework for quantum optimization of molecular docking by systematically calibrating penalties using data-driven methods, achieving high solution validity and quality improvements.

Quantum Computing Reveals Energetics of Tau Peptide Fragments

This study applies VQE to a tau peptide fragment to compare quantum and classical methods in calculating electronic energies and highlights their complementary roles in peptide biophysics.

Quantome: A Quantum Surrogate Model for Biophysical Landscapes

The paper introduces a quantum surrogate model framework for biophysical landscapes, demonstrating its ability to simulate transitions between localized and delocalized states using variational quantum eigensolver simulations.

QOBRA: A Quantum Operator-Based Autoencoder for De Novo Molecular Design

QOBRA is a quantum autoencoder for de novo molecular design that generates valid, novel molecules from limited datasets, demonstrating the potential of quantum computing in molecular synthesis.

Genetic Engineering with Quantum Circuits: creating codes and studying BioBloQu genetic elements

The paper develops quantum codes for genetic engineering to identify and insert BioBloQu structures into genomic sequences using quantum computational frameworks.

Quantum algorithm for identifying RNA 3D motifs by processing RNA secondary structure graphs

This paper introduces a quantum algorithm for identifying RNA 3D motifs by processing secondary structure graphs, demonstrating its ability to detect conserved motifs in RNA sequences.

Application of Quantum Tensor Networks for Protein Classification

This paper applies quantum tensor networks to classify proteins by sub-cellular location, achieving 94% accuracy with a compact quantum model compared to a large classical model.

A Quantum Computing Pipeline for Real World Drug Discovery: From Algorithm to Quantum Hardware

This paper develops a quantum computing pipeline for drug discovery, focusing on Gibbs free energy profiles and covalent bond simulations, demonstrating its potential for real-world applications.

Divide-and-conquer quantum algorithm for hybrid de novo genome assembly of short and long reads

This paper proposes a hybrid quantum algorithm for genome assembly using short and long reads to address repetitive sequences and sequencing errors, showing improved convergence and optimization.

On quantum computing and geometry optimization

This paper explores encoding classical biomolecular data into quantum circuits for geometry optimization and uses quantum machine learning for protein side-chain classification.

Quantum algorithm for position weight matrix matching

This paper introduces two quantum algorithms for position weight matrix matching, achieving speedups over classical methods in certain scenarios.

mRNA codon optimization on quantum computers

This paper compares quantum computing and genetic algorithms for codon optimization, finding quantum computers competitive and potentially superior in future generations.

Quantum Approximated Graph Cutting: A Rapid Replacement for T-REMD?

This study evaluates whether quantum-hybrid algorithms can approximate T-REMD results for determining protein binding sites, finding speedups but differing outcomes due to lack of configurational changes.