We settle the long-standing open question of whether parameterized quantum circuits can learn high-dimensional semantic relationships on NISQ hardware and whether quantum phenomena provide measurable advantages. Using IBM's 156-qubit Eagle processor ibm_fez, we show that previous limitations of quantum natural language processing were not fundamental but originated from poor encoding design. All headline results below are obtained on real hardware (no error mitigation, free-tier access, November 2025). Key hardware-validated findings: • Encoding hierarchy: 132× performance gap between faithful Direct Angle Encoding (r = 0.989) and destructive Difference Encoding (r = 0.007) • Genuine quantum learning: +1.61 training effect on hardware, transforming actively anti-correlated random unitaries (r = −0.92) into semantically meaningful manifolds (r = +0.69) with only 21 parameters • Entanglement is required: entangled circuits achieve r = +0.69 while product-state ablation collapses to constant output (r = −0.11), yielding +0.81 entanglement advantage on real hardware • Hardware outperforms simulation: +18% correlation improvement versus noiseless simulator • First empirical scaling law for quantum semantic learning: increasing training data from 12 → 40 pairs improves generalization 4.25× (r = 0.08 → 0.34), projecting parity with classical cosine baseline (r ≈ 0.86) at ~100 training examples We establish three architectural prerequisites for quantum semantic learning on NISQ devices: (1) Sparse Encoding, (2) Ancilla-Based Measurement, and (3) Non-Aliased Angle Scaling [0.1, π−0.1]. This record contains: • Complete manuscript (PDF) • All source code (Qiskit 2.2.3) • Trained parameters (best_theta) • Raw IBM Quantum hardware results (JSON) • Publication-ready figures • 83-pair test set and interactive visualizations All experiments are fully reproducible with an IBM Quantum free-tier account. Related software: https://doi.org/10.5281/zenodo.17728126
We settle the long-standing open question of whether parameterized quantum circuits can learn high-dimensional semantic relationships on NISQ hardware and whether quantum phenomena provide measurable advantages. Using IBM's 156-qubit Eagle processor ibm_fez, we show that previous limitations of quantum natural language processing were not fundamental but originated from poor encoding design. All headline results below are obtained on real hardware (no error mitigation, free-tier access, November 2025). Key hardware-validated findings: • Encoding hierarchy: 132× performance gap between faithful Direct Angle Encoding (r = 0.989) and destructive Difference Encoding (r = 0.007) • Genuine quantum learning: +1.61 training effect on hardware, transforming actively anti-correlated random unitaries (r = −0.92) into semantically meaningful manifolds (r = +0.69) with only 21 parameters • Entanglement is required: entangled circuits achieve r = +0.69 while product-state ablation collapses to constant output (r = −0.11), yielding +0.81 entanglement advantage on real hardware • Hardware outperforms simulation: +18% correlation improvement versus noiseless simulator • First empirical scaling law for quantum semantic learning: increasing training data from 12 → 40 pairs improves generalization 4.25× (r = 0.08 → 0.34), projecting parity with classical cosine baseline (r ≈ 0.86) at ~100 training examples We establish three architectural prerequisites for quantum semantic learning on NISQ devices: (1) Sparse Encoding, (2) Ancilla-Based Measurement, and (3) Non-Aliased Angle Scaling [0.1, π−0.1]. This record contains: • Complete manuscript (PDF) • All source code (Qiskit 2.2.3) • Trained parameters (best_theta) • Raw IBM Quantum hardware results (JSON) • Publication-ready figures • 83-pair test set and interactive visualizations All experiments are fully reproducible with an IBM Quantum free-tier account. Related software: https://doi.org/10.5281/zenodo.17728126
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.
Quantum machine learning on real noisy intermediate-scale quantum (NISQ) hardware has remained largely confined to binary or few-class tasks, limited by the cost of on-hardware training and the underuse of large devices at inference. We present a unified framework that classifies ten-class MNIST end-to-end on a 127-qubit IBM Eagle processor, with three central contributions. First, a two-phase protocol decouples a gradient-based classical optimization of the encoder and readout from a gradient-free optimization of the quantum parameters, removing the parameter-shift gradient cost that makes on-hardware training impractical. Second, we introduce Quantum Multi-Programming to a trained quantum classifier for the first time, packing multiple circuit copies onto one device to deliver parallel inference at no mean-accuracy cost while cutting quantum-processor job submissions proportionally. Third, a controlled comparison shows that on-hardware fine-tuning yields no measurable accuracy gain, motivating a practical NISQ workflow: train on a classical simulator and reserve the hardware for inference only. Benchmarked against a matched-capacity classical network, the quantum module shows no per-parameter accuracy advantage at this scale; we therefore frame the work as a feasibility-and-workflow demonstration for multi-class quantum image classification on current hardware.
Hybrid quantum-classical models represent a crucial step toward leveraging near-term quantum devices for sequential data processing. We present Quantum Recurrent Neural Networks (QRNNs) and Quantum Convolutional Neural Networks (QCNNs) as hybrid quantum language models, reporting the first empirical demonstration of generative language modeling trained and evaluated end-to-end on real quantum hardware. Our architecture combines hardware-optimized parametric quantum circuits with a lightweight classical projection layer, utilizing a multi-sample SPSA strategy to efficiently train quantum parameters despite hardware noise. To characterize the capabilities of these models, we introduce a synthetic dataset designed to isolate syntactic dependencies in a controlled, low-resource environment. Experiments on IBM Quantum processors reveal the critical trade-offs between circuit depth and trainability, demonstrating that while noise remains a significant factor, observable-based readout enables the successful learning of sequential patterns on NISQ devices. These results establish a rigorous engineering baseline for generative quantum natural language processing, validating the feasibility of training complex sequence models on current quantum hardware.
Eight verified quantum computing jobs executed on IBM Quantum hardware (ibm_fez Heron R2, 156 qubits; ibm_torino Heron R1, 133 qubits) between January 27 and March 15, 2026. User ID: IBMid-692001DJRD. Experiments include: 1. Bell State Entanglement — 96.3% fidelity on ibm_fez Heron R2 (Concurrence C = 0.926, Tangle T = 0.858, 1,000 shots). Cross-platform benchmark: Heron R2 vs Heron R1 (+8.3 pp fidelity advantage, noise ratio 2.43×). 2. Reproducibility Test — Two independent runs of identical circuit (χ² = 2.70, p = 0.44, df = 3). H₀ not rejected: statistically indistinguishable results at 95% confidence. 3. BB84 Quantum Key Distribution — Full Alice/Eve/Bob protocol implementation. Alice baseline error 0.60% → Bob QBER 49.0% under eavesdropping (82× degradation). Experimental demonstration of the quantum no-cloning theorem. Control group design isolates eavesdropper signal from hardware noise baseline. 4. Variational Quantum Circuit (VQC) — 2-qubit circuit, 10 trained parameters with tied weights (θ₃ = θ₄), 8 CZ gates in 4 entangling layers. 4-qubit ansatz: Shannon entropy 3.885/4.000 bits (97.1%), ground-state bias confirmed (χ² = 300.94, p ≈ 0). 5. Quantum Random Number Generation (QRNG) — 8-bit outputs, 10/10 unique values per shot (range 16–254). Total: 39,010 quantum measurements across 8 jobs and 10 circuits. QPU time: 4.8 seconds. Job completion rate: 100%. All results decoded from raw IBM SamplerV2 BitArray binary archives (zlib + base64 + NumPy .npy pipeline). No simulations. Statistical validation applied throughout (Wilson 95% CIs, chi-squared tests). Stack: Qiskit Runtime SamplerV2 · QPY · OpenQASM 2.0 · Python · NumPy · SciPy Repository: https://github.com/dinohatibovicWhitepaper: https://dinohatibovic.github.io (Doc QAI-WP-2026-01-28-V4)
This paper presents an empirical evaluation of the Qube Engine, a hybrid quantum-classical Variational Quantum Regressor (VQR) implemented on Noisy Intermediate-Scale Quantum (NISQ) systems. The framework integrates classical data encoding via single-qubit rotations, parameterized RY+RZ variational layers, and linear entangling circuits evaluated on 4-qubit quantum registers. Optimization is conducted using classical algorithms (COBYLA) targeting Pauli-Z expectation values and Mean Squared Error (MSE) loss on continuous feature spaces. We evaluate model performance across ideal statevector simulation (AerSimulator) and physical IBM Quantum superconducting processors (ibm_marrakesh, ibm_fez, ibm_kingston). Experimental results demonstrate consistent optimization dynamics and low simulation-to-hardware expectation drift (Δ=0.0188), corresponding to a 98.23% cross-backend output stability score under shallow circuit depths. This work provides a realistic analysis of variational regression dynamics, hardware execution variance, and structural trade-offs for near-term quantum machine learning models on NISQ hardware.
Designing parameterized quantum circuits (PQCs) that are expressive, trainable, and robust to hardware noise is a central challenge for quantum machine learning (QML) on noisy intermediate-scale quantum (NISQ) devices. We present a Domain-Aware Quantum Circuit (DAQC) that leverages image priors to guide locality-preserving encoding and entanglement via non-overlapping DCT-style zigzag windows. The design employs interleaved encode-entangle-train cycles, where entanglement is applied among qubits hosting neighboring pixels, aligned to device connectivity. This staged, locality-preserving information flow expands the effective receptive field without deep global mixing, enabling efficient use of limited depth and qubits. The design concentrates representational capacity on short-range correlations, reduces long-range two-qubit operations, and encourages stable optimization, thereby mitigating depth-induced and globally entangled barren-plateau effects. We evaluate DAQC on MNIST, FashionMNIST, and PneumoniaMNIST datasets. On quantum hardware, DAQC achieves performance competitive with strong classical baselines (e.g., ResNet-18/50, DenseNet-121, EfficientNet-B0) and substantially outperforming Quantum Circuit Search (QCS) baselines. To the best of our knowledge, DAQC, which uses a quantum feature extractor with only a linear classical readout (no deep classical backbone), currently achieves the best reported performance on real quantum hardware for QML-based image classification tasks. Code and pretrained models are available at: https://github.com/gurinder-hub/DAQC.