This technical note presents the complete benchmark suite of the QOPS Hypercube Engine,a deterministic computational framework integrating hypercube-driven state evolution,topological invariant extraction, operator-space exploration, and stable renormalization-likedimensional reduction. Unlike classical quantum simulators or machine-learning systems, QOPS is based on ahypercube-directed acyclic graph (HDAG) model, enabling deterministic program extraction,sequence contraction, and operator mining within high-dimensional state spaces.All benchmarks were generated using a Rust backend with fixed-time execution semantics andexported as machine-readable JSON artifacts. The benchmark suite covers: Advanced algorithm stability VQE, VQC and QAOA convergence Deterministic quantum-walk propagation Integration consistency across subsystems Topological invariants (Chern number, Berry phase, Jacobian stability) Hypercube cascade convergence (Hⁿ → H¹) Deterministic operator-mining performance GUI latency measurements Cross-system comparisons with Qiskit, Cirq, PennyLane and QSO The results show that QOPS exhibits exceptional deterministic behaviour,including microsecond-level execution stability, monotonic convergence across all measuredoptimization tasks, and robust invariant extraction without numerical drift.These properties position QOPS as a unique experimental framework for exploringtopological computation, hyperdimensional state dynamics, and operator-space navigation.
This technical note presents the complete benchmark suite of the QOPS Hypercube Engine,a deterministic computational framework integrating hypercube-driven state evolution,topological invariant extraction, operator-space exploration, and stable renormalization-likedimensional reduction. Unlike classical quantum simulators or machine-learning systems, QOPS is based on ahypercube-directed acyclic graph (HDAG) model, enabling deterministic program extraction,sequence contraction, and operator mining within high-dimensional state spaces.All benchmarks were generated using a Rust backend with fixed-time execution semantics andexported as machine-readable JSON artifacts. The benchmark suite covers: Advanced algorithm stability VQE, VQC and QAOA convergence Deterministic quantum-walk propagation Integration consistency across subsystems Topological invariants (Chern number, Berry phase, Jacobian stability) Hypercube cascade convergence (Hⁿ → H¹) Deterministic operator-mining performance GUI latency measurements Cross-system comparisons with Qiskit, Cirq, PennyLane and QSO The results show that QOPS exhibits exceptional deterministic behaviour,including microsecond-level execution stability, monotonic convergence across all measuredoptimization tasks, and robust invariant extraction without numerical drift.These properties position QOPS as a unique experimental framework for exploringtopological computation, hyperdimensional state dynamics, and operator-space navigation.
This Technical Note presents a fully reproducible benchmark suite for Metatron QSO, a deterministic and noiseless quantum-algorithm simulation core. All results were generated in continuous integration (CI) using open-source code, ensuring full transparency and repeatability. The benchmark suite covers a wide range of quantum-algorithm families, including: Grover & multi-target search Quantum walks on structured graphs QAOA (MaxCut) with convergence validation VQE (multiple ansatz families) VQC classification experiments Boson sampling interference statistics Cross-framework comparisons (Qiskit, Cirq, PennyLane, ProjectQ) The goal of this report is to provide an ideal, noise-free reference baseline for algorithmic behaviour, interference stability, and hybrid optimisation — independent of any hardware backend.Such deterministic baselines can support research in quantum computing, algorithm design, hardware benchmarking, and educational environments. All code, configurations, benchmark JSON artifacts, and CI workflows are openly available at: GitHub Repository:https://github.com/LashSesh/qso This work is released as an open scientific contribution.
This paper presents the S-QPU v1.0 (Semantic Quantum Processing Unit), the first deterministic semantic processor designed to execute the Quranic Assembly Language (QAL) and implement its formal semantic state-transition model directly in hardware. The S-QPU extends conventional vector-processing architectures by introducing a novel semantic state register R_\Psi, immutable semantic memory banks (VMEM and WMEM), and microcoded semantic operators—including ALIGN, MAPVW, and ENTANGLE—that enable reproducible semantic evolution with fixed, mathematically grounded execution paths. Unlike statistical machine-learning accelerators, the S-QPU provides deterministic, timing-invariant execution with strict hazard rules, static microcode schedules, and complete invariance of semantic transformations across hardware generations. The architecture defines a full ISA, semantic ALU cluster, entanglement engine (KRED), pipeline organization, error-handling model, numerical stability constraints, and an analytical performance framework. A rigorous verification strategy—including ISA-level correctness via QVM, RTL simulation, formal proofs, and microarchitectural validation—ensures exact correspondence between software and hardware semantics. We further present deployment models across FPGA, ASIC, cloud clusters, and embedded systems, along with a scalable roadmap for future versions featuring multi-engine parallelism, multi-state processing, and distributed semantic meshes. The S-QPU v1.0 establishes the foundational hardware substrate for deterministic semantic computation derived from the structural properties of Quranic semantic embeddings, enabling reproducible, mathematically exact semantic processing at scale.
The QED-C suite of Application-Oriented Benchmarks provides the ability to gauge performance characteristics of quantum computers as applied to real-world applications. Its benchmark programs sweep over a range of problem sizes and inputs, capturing key performance metrics related to the quality of results, total time of execution, and quantum gate resources consumed. In this manuscript, we investigate challenges in broadening the relevance of this benchmarking methodology to applications of greater complexity. First, we introduce a method for improving landscape coverage by varying algorithm parameters systematically, exemplifying this functionality in a new scalable HHL linear equation solver benchmark. Second, we add a VQE implementation of a Hydrogen Lattice simulation to the QED-C suite, and introduce a methodology for analyzing the result quality and run-time cost trade-off. We observe a decrease in accuracy with increased number of qubits, but only a mild increase in the execution time. Third, unique characteristics of a supervised machine-learning classification application are explored as a benchmark to gauge the extensibility of the framework to new classes of application. Applying this to a binary classification problem revealed the increase in training time required for larger anzatz circuits, and the significant classical overhead. Fourth, we add methods to include optimization and error mitigation in the benchmarking workflow which allows us to: identify a favourable trade off between approximate gate synthesis and gate noise; observe the benefits of measurement error mitigation and a form of deterministic error mitigation algorithm; and to contrast the improvement with the resulting time overhead. Looking ahead, we discuss how the benchmark framework can be instrumental in facilitating the exploration of algorithmic options and their impact on performance.
Meta-Analysis — Applied Quantum Computing Track This record presents a unifying meta-analysis of applied benchmarks within the Radial Coherential Dynamics (RCD) research program, synthesizing results across multiple quantum algorithms, system dimensionalities, and problem classes under a single coherence-regulation principle. Building upon the foundational decoherence suppression model (Topological Decoherence Suppression via Coherential Geometry, Zenodo, December 2025, DOI: 10.5281/zenodo.18000641), and its applied benchmarks in variational and optimization algorithms, this work consolidates evidence that a fixed decoherence scaling law T2→T2/αT_2 \rightarrow T_2/\sqrt{\alpha}T2→T2/α (with α≈1.3×10−4\alpha \approx 1.3 \times 10^{-4}α≈1.3×10−4) produces consistent, non-ad-hoc improvements across NISQ-relevant tasks. The meta-analysis integrates four independent benchmarks: (i) VQE for molecular H₄ (0D), (ii) VQE for a graphene-inspired hexagon (2D, PBC), (iii) VQE for a 1D Hubbard chain (OBC), and (iv) QAOA for MaxCut optimization on graphs. All benchmarks employ a controlled A/B methodology in which identical circuits are executed with standard noise (control) and coherence-regulated noise (treatment), with no parameter tuning introduced at the benchmark level. Across these diverse settings, coherence regulation yields stable reductions in energy error (≈ 40–46% for VQE tasks) and optimization gap (≈ 25% for QAOA), with magnitudes consistent with the underlying decoherence scaling and robust to changes in topology, dimensionality, and algorithmic structure. This work does not introduce new physical assumptions or experimental claims. Rather, it provides a consolidated, cross-domain view intended to clarify scope, consistency, and limitations, and to facilitate independent assessment and downstream reuse by the scientific community. All figures, summary data, and benchmark references are provided for transparency and reproducibility.
Reproducibility and comparability remain fundamental challenges in the empirical evaluation of Quantum Software Engineering (QSE) tools. These challenges arise from the intrinsic probabilistic behavior of quantum programs and the variability introduced by quantum hardware and simulator backends. While existing quantum benchmarks typically offer only static circuit definitions, they often leave researchers responsible for data generation and execution, hindering the creation of standardized and reproducible experiments. In this paper, we introduce QSimBench, a novel, open-source benchmark suite and accompanying Python library designed to address these limitations. QSimBench provides over 20 million precomputed shot-level execution traces:20,000measurements each, across14quantum algorithms, multiple circuit sizes (4−15qubits), and six Qiskit-based backends including both ideal and noise-injected simulations. Each configuration includes not only detailed measurement histories but also the original circuit in OpenQASM, the backend's noise model, and configuration metadata, ensuring full transparency and enabling in-depth analysis. QSimBench empowers researchers to conduct scalable, reproducible, and statistically rigorous evaluations of QSE tools, such as those for monitoring, orchestration, and statistical post-processing, without the need for expensive or difficult-to-reproduce quantum executions. The benchmark is distributed with a lightweight Python API that facilitates fast data retrieval, flexible sampling strategies, and seamless integration into experimental pipelines. QSimBench is publicly available, aiming to foster collaborative and reproducible research within the quantum software community.
The UHF Quantum-Classical Optimization Engine demonstrates robust performance across multiple optimization domains. In benchmarks including Traveling Salesperson Problem instances, lattice optimization, and extreme stress tests, UHF consistently outperforms classical, hybrid, and Google quantum-classical tools in computation speed, solution quality, and system stability. Leveraging meta-conscious heuristics and qutrit-inspired permutation strategies, the engine achieves high reproducibility and scalability across problem sizes. Detailed benchmark tables and CSV logs are provided to support reproducibility and comparison.