This public benchmark report presents sanitized results from the SoftQuantus QCOS Autopilot validation on IBM Quantum hardware. The report documents the preparation of a 2-qubit Bell state on IBM’s ibm_fez Heron r2 processor, achieving approximately 0.85 raw fidelity with 17 hardware evaluations and no error mitigation. QCOS Autopilot is positioned as a hardware-agnostic quantum control and optimization layer for NISQ-era systems. This public version focuses on benchmark outcomes, reproducibility context, and enterprise positioning, while excluding proprietary implementation details, internal heuristics, optimization logic, and confidential execution artifacts. The report is intended for researchers, quantum software developers, infrastructure partners, investors, and institutions evaluating hardware-efficient quantum control, reproducible quantum benchmarking, and vendor-agnostic quantum computing workflows.
This public benchmark report presents sanitized results from the SoftQuantus QCOS Autopilot validation on IBM Quantum hardware. The report documents the preparation of a 2-qubit Bell state on IBM’s ibm_fez Heron r2 processor, achieving approximately 0.85 raw fidelity with 17 hardware evaluations and no error mitigation. QCOS Autopilot is positioned as a hardware-agnostic quantum control and optimization layer for NISQ-era systems. This public version focuses on benchmark outcomes, reproducibility context, and enterprise positioning, while excluding proprietary implementation details, internal heuristics, optimization logic, and confidential execution artifacts. The report is intended for researchers, quantum software developers, infrastructure partners, investors, and institutions evaluating hardware-efficient quantum control, reproducible quantum benchmarking, and vendor-agnostic quantum computing workflows.
This record presents the scientific validation of QCOS (Quantum Circuit Optimization System), a reproducible and test-driven orchestration framework for variational quantum algorithms in the NISQ era. The document provides rigorous empirical evidence of correctness, statistical robustness, and optimization behavior using the H₂ VQE benchmark. Validation includes full Hamiltonian spectral equivalence (Qiskit Nature vs OpenFermion), shot-noise scaling analysis, multi-start optimization studies, and a comprehensive unit-test suite. This white paper is intended as a technical reference for researchers, industry partners, and decision-makers evaluating QCOS as a viable quantum software system rather than a performance-claim-driven.
Despite rapid progress in quantum optimization, broad real-hardware benchmarks comparing multiple algorithmic families across diverse combinatorial problems under a common protocol remain limited. We benchmark gate-based quantum optimization on four NP-hard problems: multi-dimensional knapsack (MDKP), maximum independent set (MIS), quadratic assignment (QAP), and market-share (MSP). We study VQE, CVaR-VQE, standard, multi-angle, and warm-start QAOA, together with qubit-efficient PCE and QRAO, on IBM Heron r1/r2 processors using resilience-level-2 mitigation. To our knowledge, this includes the first real-hardware QRAO results and the first multi-problem PCE hardware benchmark.
Across 247 method-instance combinations, we report transpiled circuit size, hardware outcomes, and an independent-error gate-count fidelity proxy, Fest. For MDKP and MIS, an empirical operating point near Fest≈0.1, corresponding to about 770 two-qubit gates at the median Heron-r2 CZ error rate, marks the onset of noise-dominated execution. QAP exposes a separate bottleneck: dense one-hot encodings and an exponentially sparse feasible manifold, with feasible fraction 10!/2100 at n=10; no tested hardware method produces a feasible assignment. Compiled QAOA-family circuits are generally noise dominated, and a matched uniform-random control shows that most feasible low-fidelity outcomes fall within the random range, apart from one finite-sample MIS warm-start exception. A SWAP-aware, fractional-gate, Nighthawk-topology compilation counterfactual reduces two-qubit counts but leaves all circuits below Fest=10−3. These conclusions apply to the tested implementations rather than QAOA in general. Qubit-efficient methods extend runnable instance sizes, but only within the empirical fidelity budget.
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.
This record contains the Madmartigan 96-Active-Qubit Structured-Output Benchmark package developed under the Quantum State Command Encoding (QSCE) research program. The benchmark documents the progression from an original 16-qubit Madmartigan structured-output circuit into a controlled 96-active-qubit, six-tile NISQ benchmark executed on IBM superconducting quantum hardware. The objective was not merely to increase qubit count, but to test whether a designed quantum circuit could preserve a reference-specific structured-output band across multiple physical tile regions under real hardware constraints. The final benchmark package includes execution on IBM Marrakesh using 96 active qubits, 156 measured qubits, six simultaneous 16-qubit tiles, 4096 shots per run, no quantum error correction, and no post-selection. The primary GLOBALPACK T6 rank-2 layout is accompanied by calibration-aware 64-active-qubit replication results, multi-seed robustness testing, an 831-depth 96-active-qubit stress layout, same-layout generic RCS controls, phase-scrambled architecture-adjacent controls, and partial-entanglement ablation analysis. The central finding is that the Madmartigan circuit preserved repeatable, reference-specific structured-output behavior across tiled hardware execution, while same-layout generic RCS controls and phase-scrambled controls failed to reproduce the Madmartigan reference band. The package includes statistical raw-versus-control separation, physical tile-map visualizations, and reproducibility artifacts to support independent technical review. Included materials may include some or all of the following: benchmark report, QASM3 circuit exports, QPY circuit artifacts, raw hardware counts, metadata, analysis CSVs, physical tile maps, scanner outputs, statistical comparison files, figures, and supporting run scripts. These artifacts are provided to support auditability, reproducibility review, and continued evaluation of QSCE/Madmartigan structured-output preservation as a potential near-term NISQ utility pathway. This work is positioned as a structured-output preservation benchmark rather than a universal quantum advantage claim. Its relevance lies in testing whether quantum hardware output can be engineered into stable, classifiable, reference-specific signal bands that may support future quantum-to-classical handoff, signaling, authentication, command validation, and cyber-hardening primitives. Reproducibility package available upon request at frank@quantummidiposse.com
Quantum processors have crossed the one-hundred-qubit mark, but noise continues to limit circuit performance, while full quantum error correction remains too costly for routine use. Error suppression and mitigation therefore play an important role in extracting value from current hardware, yet independent comparisons of commercial solutions on identical workloads and devices remain scarce. We benchmark IBM Qiskit Runtime, Q-CTRL Performance Management, and Qedma QESEM on IBM Pittsburgh, a 156-qubit IBM Quantum Heron r3 processor. For Sampler workloads, we run Bernstein-Vazirani, quantum phase estimation, GHZ-state preparation, and randomized mirror circuits with up to 100 measured qubits, comparing raw execution, IBM measurement twirling, and Q-CTRL. For Estimator workloads, we measure chain-averaged magnetization and correlation observables of an eight-layer transverse-field Ising circuit at 25, 50, and 75 qubits against an exact matrix-product-state reference, comparing IBM raw execution, IBM TREX plus twirling, Q-CTRL, and QESEM. Q-CTRL produced the best results on the three structured Sampler workloads while keeping reported QPU times within the same order as the IBM configurations. Across six Ising observable and system-size cases, aggregate mean absolute error was 0.0883 for IBM raw execution, 0.0807 for IBM TREX plus twirling, 0.0285 for Q-CTRL, and 0.0188 for QESEM. Relative to raw execution, Q-CTRL and QESEM reduced aggregate error by factors of 3.10 and 4.70, respectively, while QESEM used 7.5 to 11.1 times the reported QPU time of Q-CTRL. These results show that managed error suppression and mitigation can substantially improve current hardware performance, but with distinct accuracy and execution-time tradeoffs.