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.
Quantum software ecosystems provide increasingly capable tools for simulation and hardware execution, but differences in programming models, supported features, and backend capabilities can make it difficult to move and systematically compare the same quantum algorithm across execution environments. We introduce eigenkit, a provider-neutral quantum computing library that separates algorithm definition from backend execution through a common circuit representation and provider interface. The library currently supports 12 execution providers spanning simulators and quantum hardware, provides backend-independent gradient computation, and integrates with standard scientific-computing and machine-learning workflows through NumPy, PyTorch, and scikit-learn interfaces. We evaluate the practical relevance of this abstraction across both simulation and quantum hardware. Simulator performance varies substantially with circuit size, structure, batching, and application workload, showing that no single backend is consistently preferable. On quantum hardware, experiments on IBM Heron and IQM Emerald show that which processor is better suited to a given algorithm depends not only on hardware constraints, but also on how well the topology of the quantum algorithm matches the physical connectivity of the processor. These results demonstrate that backend portability is not only a software-design convenience, but can be important for both computational efficiency and quantum execution quality.
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, across
14quantum 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.