Abstract Quantum machine learning for genomic sequence classification faces three compounding constraints on current hardware: barren plateaus in variational training, exponential concentration of quantum kernel expressivity in highdimensional feature spaces, and noise accumulation that limits practical circuit depth to fewer than 50 layers on current NISQ devices. Existing quantum genomics studies address these constraints using fixed-architecture circuits without noise-calibrated validation on standardized benchmarks, leaving the potential of adaptive circuit topology largely unexplored. Three QAOA variants are developed and systematically compared to address this gap. Classical QAOA establishes a baseline using fixed p-layer circuits with standard phase and mixing operators. Alternating QAOA introduces structured CNOT entanglement between alternating qubit pairs to evaluate whether non-local correlations improve genomic pattern recognition. The primary contribution, Adaptive QAOA, employs gradient-driven operator selection from feature-weighted Pauli operator pools, generating variable-depth problemspecific circuits that adapt their topology to dataset structure rather than applying universal ans¨atze. All three architectures are evaluated through quantum simulation on two standardized genomic benchmarks: Human Nontata Promoters (36,131 sequences) and Demo Coding vs. Intergenic Sequences (100,000 sequences). Experiments are conducted under both noiseless conditions and noise models calibrated to IBM superconducting hardware specifications, across feature space dimensionalities ranging from 2 to 10 dimensions using 10-fold cross-validation. Adaptive QAOA consistently achieves the highest precision (approximately 75% under noiseless simulation) and maintains perfect specificity across both datasets and noise conditions, results not observed for fixed-depth variants. These performance gains are accompanied by reduced hardware resource consumption, with Adaptive QAOA requiring the fewest qubits and lowest quantum usage time (∼0.52–0.60 s) across most configurations. Superior coherence characteristics — average T1 of 82.12 μs and T2 of 55.45 μs, exceeding Classical QAOA by 13.6% and 113.3% respectively — provide a physical basis for reduced error accumulation under realistic noise constraints. All QAOA variants maintain stable classification performance across all tested dimensionalities, contrasting with compounding errors documented for fixed-architecture quantum kernel methods on high-dimensional genomic data. These results establish that gradient-driven adaptive circuit topology with domain-specific feature weighting represents a more effective design strategy than uniform depth scaling or entanglement-based ansatz modification for near-term quantum genomic classification.
Abstract Quantum machine learning for genomic sequence classification faces three compounding constraints on current hardware: barren plateaus in variational training, exponential concentration of quantum kernel expressivity in highdimensional feature spaces, and noise accumulation that limits practical circuit depth to fewer than 50 layers on current NISQ devices. Existing quantum genomics studies address these constraints using fixed-architecture circuits without noise-calibrated validation on standardized benchmarks, leaving the potential of adaptive circuit topology largely unexplored. Three QAOA variants are developed and systematically compared to address this gap. Classical QAOA establishes a baseline using fixed p-layer circuits with standard phase and mixing operators. Alternating QAOA introduces structured CNOT entanglement between alternating qubit pairs to evaluate whether non-local correlations improve genomic pattern recognition. The primary contribution, Adaptive QAOA, employs gradient-driven operator selection from feature-weighted Pauli operator pools, generating variable-depth problemspecific circuits that adapt their topology to dataset structure rather than applying universal ans¨atze. All three architectures are evaluated through quantum simulation on two standardized genomic benchmarks: Human Nontata Promoters (36,131 sequences) and Demo Coding vs. Intergenic Sequences (100,000 sequences). Experiments are conducted under both noiseless conditions and noise models calibrated to IBM superconducting hardware specifications, across feature space dimensionalities ranging from 2 to 10 dimensions using 10-fold cross-validation. Adaptive QAOA consistently achieves the highest precision (approximately 75% under noiseless simulation) and maintains perfect specificity across both datasets and noise conditions, results not observed for fixed-depth variants. These performance gains are accompanied by reduced hardware resource consumption, with Adaptive QAOA requiring the fewest qubits and lowest quantum usage time (∼0.52–0.60 s) across most configurations. Superior coherence characteristics — average T1 of 82.12 μs and T2 of 55.45 μs, exceeding Classical QAOA by 13.6% and 113.3% respectively — provide a physical basis for reduced error accumulation under realistic noise constraints. All QAOA variants maintain stable classification performance across all tested dimensionalities, contrasting with compounding errors documented for fixed-architecture quantum kernel methods on high-dimensional genomic data. These results establish that gradient-driven adaptive circuit topology with domain-specific feature weighting represents a more effective design strategy than uniform depth scaling or entanglement-based ansatz modification for near-term quantum genomic classification.
Adaptive quantum machine learning (QML) represents an exciting frontier at the intersection of quantum computing and classical bio-inspired optimization. The constraints of noisy intermediate-scale quantum (NISQ) devices—limited qubit counts, significant decoherence, and measurement uncertainties—pose formidable challenges to training parameterized quantum circuits (PQCs) using traditional gradient-based methods. To overcome these hurdles, we propose a novel Adaptive PSO–QML framework that integrates a particle swarm optimization (PSO)–inspired algorithm with dynamic hyperparameter adjustment, enabling real-time tuning of PQC parameters based on performance feedback. Unlike static optimizers, our approach continuously adapts its inertia weight and cognitive/social coefficients to balance exploration and exploitation, responding to stagnation or rapid improvement in measured fitness. We evaluate this hybrid algorithm on two benchmark classification tasks: the classical Iris dataset encoded into a 2-qubit circuit and a quantum-encoded subset of the MNIST dataset on 4 qubits. Our experiments, conducted on the Qiskit Aer simulator under realistic noise models (depolarizing, amplitude damping, and mixed), demonstrate that Adaptive PSO–QML achieves an average accuracy improvement of 12% over fixed-parameter PSO and 15% over Adam-based gradient descent, with p-values below 0.01 across paired t-tests. Statistical analysis summarized in Table 1 confirms the significance of these gains. Detailed simulation research further reveals that the adaptive mechanism not only accelerates convergence by approximately 30%—reducing total circuit evaluations—but also exhibits robust performance across varying circuit depths (1–3 entangling layers) and noise intensities. Our findings suggest that bio-inspired adaptation is a powerful tool for mitigating barren plateaus and noise effects in NISQ-era QML, paving the way for scalable quantum advantage in near-term hardware.
This study investigates classification performance and trainability of variational quantum classifiers on near-term noisy quantum hardware across circuit depth and qubit count regimes within the context of quantum computing and machine learning, an area of growing scientific importance given its implications for hybrid quantum-classical algorithm design and practical NISQ hardware benchmarking for near-term quantum advantage assessment. Using gradient-based parameter optimization of parameterized quantum circuits with hardware noise modeling via density matrix simulation and physical execution on IBM Eagle processors, we examine expressibility of parameterized circuits enabling nonlinear decision boundaries in feature Hilbert space, with trainability constrained by barren plateau gradients at large qubit counts in 96 circuit architectures (4 qubit counts x 4 depths x 6 ansatz families) evaluated across 5 classification datasets with 20 random initialization seeds each drawn from IBM Quantum Eagle (127-qubit) and Falcon (27-qubit) processors with noise mitigation via zero-noise extrapolation. Results indicate that 10-qubit, 4-layer VQC achieves 84.2% classification accuracy on the best benchmark, within 3.8 percentage points of classical SVM, but gradient variance decays exponentially with qubit count (variance halving per 2 added qubits), confirming barren plateau scaling (p < 0.001), with 84.2% accuracy, 3.8 pp gap to classical SVM baseline as the primary quantitative benchmark. Concordance between primary and confirmatory measurement approaches exceeded 93%, validating the analytical framework. These findings contribute empirically to quantum computing and machine learning and carry actionable implications for the design of programs and policies targeting hybrid quantum-classical algorithm design and practical NISQ hardware benchmarking for near-term quantum advantage assessment.
Abstract—Quantum Machine Learning (QML) operating on Noisy Intermediate-Scale Quantum (NISQ) hardware represents an emerging computational paradigm to address high-dimensional optimization problems that challenge classical Von Neumann supercomputing architectures. By mapping complex feature vectors into exponentially large 2^N Hilbert state spaces via Parameterized Quantum Circuits (PQCs) and Variational Quantum Algorithms (VQAs), QML targets computational advantages in combinatorial optimization, high-dimensional classification, and quantum-enhanced molecular modeling. However, the theoretical promise of NISQ-era variational computing is severely constrained by three fundamental bottlenecks: (1) Barren Plateaus, characterized by the exponential concentration of measure and vanishing variance of cost function gradients; (2) Hardware Decoherece Noise and readout error rates that corrupt quantum state fidelity; and (3) Classical-to-Quantum State Preparation bottlenecks that can erase theoretical computational speedups. This paper delivers an exhaustive, mathematically formal technical investigation and empirical benchmark of QML architectures for high-dimensional optimization. We formalize the Haar-distributed geometric mechanics of barren plateaus under 2-design unitary assumptions, evaluate barren plateau mitigation through shallow local cost operators and layer-by-layer parameter initialization, and contrast Variational Quantum Eigensolvers (VQE) with Quantum Kernel Support Vector Machines (Q-SVM). Furthermore, we construct an empirical benchmarking testbed executing across both state-vector simulators and superconducting quantum backends (IBM Quantum Heron / Eagle architectures), evaluating 127-qubit Hamiltonian optimization and high-dimensional financial portfolio allocation. Our findings demonstrate that while global cost functions suffer exponential gradient decay Var[grad] ~ O(2^-N) across circuit depth d >= 4, local Hamiltonian decomposition maintains trainable variance, achieving a 98.2% ground-state fidelity under zero-noise extrapolation (ZNE) error mitigation. Finally, we formulate open challenges in quantum error mitigation scaling, non-perturbative barren plateau escapes, and quantum data loading efficiency. Index Terms—Quantum Machine Learning (QML), Parameterized Quantum Circuits (PQCs), Variational Quantum Eigensolver (VQE), Barren Plateaus, Quantum Kernel Methods, NISQ Computing, Error Mitigation, Hilbert Space.
Abstract Quantum kernel methods have emerged as a powerful paradigm for extending classical machine learning into high-dimensional quantum Hilbert spaces, offering potential advantages in complex, nonlinear pattern recognition. However, their performance on near-term quantum devices remains constrained by circuit depth, noise sensitivity, and suboptimal parameterization of quantum feature maps. This paper introduces an Adaptive Quantum Kernel Optimization (AQKO) framework that integrates circuit-level adaptability, hybrid optimization, and regularization to enhance both expressivity and robustness in quantum kernel learning. The proposed method employs Adaptive Quantum Feature Maps (AQFM) that dynamically adjust rotation and phase parameters through a hybrid gradient-based optimization routine, reducing overfitting and improving generalization. Furthermore, a regularized quantum kernel objective is formulated to stabilize eigenvalue spectra under Noisy Intermediate-Scale Quantum (NISQ) conditions. Extensive experiments on nonlinear benchmark datasets — including Moons , Circles , and Iris — demonstrate that AQKO achieves up to 8.5% higher classification accuracy and 32% lower circuit depth compared to static quantum kernels. Hardware validation using IBM’s ibmq_quito device confirms AQKO’s resilience under depolarizing noise (p = 0.02) with a 41% error mitigation efficiency gain. These findings highlight AQKO’s potential as a scalable and noise-tolerant foundation for real-world quantum-enhanced learning, bridging the gap between theoretical quantum kernel models and practical NISQ implementations.
Although quantum computers will not necessarily transform ML in general, they hold immense potential for specific uses, such as dealing with high-dimensional data and complex correlations that cannot be effectively modeled using classical systems. It is, however, difficult to design efficient quantum circuits, especially with the existing Noisy Intermediate-Scale Quantum (NISQ) hardware that has constraints including decoherence, a limited number of qubits, and gate fidelity limitations. The authors propose an Adaptive Quantum Circuit Architecture Search (AQCAS) framework whose structure and parameters are optimized dynamically to improve the performance of the task-specific ML. The framework integrates quantum circuit encoding, a hybrid search strategy that is based on reinforcement learning and evolutionary algorithms, and a task-specific adaptivity mechanism. Benchmark datasets were used for experimental evaluations of both simulation using Qiskit Aer and classical optimization using the Python frameworks TensorFlow and PennyLane. Fixed and standard variational quantum circuits were compared with performance measurements like the classification accuracy, circuit depth, convergence speed, and computational efficiency. The results show that the accuracy of AQCAS can be raised to 94.1%, and the circuit depth is lowered at the same time. It converged rapidly and is computationally efficient. Ablation studies show that both hybrid search and adaptivity mechanisms help to enhance generalization and stability under NISQ constraints. The results demonstrate the potential of AQCAS as a practical approach to using adaptive quantum circuits in specialized ML applications, and lay the groundwork for future development of scaling to larger devices, implementing noise-aware optimization, and integrating with hybrid quantum-classical ML pipelines.
The classification of high-dimensional genomic data presents fundamental difficulties for classical machine learning techniques because of the intrinsic non-linearity and the enormous amount of biological feature space represented. In this report, we propose a novel Quantum Kernel Mapping Algorithm (QKMA), which utilizes quantum superposition and entanglement to place genomic data into exponentially large Hilbert spaces and discover non-linear separating hyperplanes that are not computable with classical kernel methods. Our approach to QKMA is to create a parameterized quantum circuit architecture for genomic feature encoding by utilizing principles of variational quantum eigensolvers and support vector machine optimization. We have applied QKMA to three canonical classification problems in genomics: RNA sequencing data for the classification of cancer subtypes (n = 2,847 samples), SNP-based prediction of phenotypes (n = 5,102 subjects), and classification of epigenetic methylation patterns (n = 1,634 samples). QKMA achieved classification accuracies of 91.4%, 88.7%, and 93.2%, respectively, exceeding the baseline classical RBF-SVM accuracies by 7.2%, 6.3%, and 8.1%, respectively. Furthermore, our hybrid quantum-classical optimization framework reduces the kernel computation overhead by 34% compared to naive quantum simulation methods. These results suggest that quantum kernel methods provide a principled, scalable pathway to precision genomics and have important implications for early disease detection, pharmacogenomics, and personalized medicine.