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.
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.
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.
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.
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.
Variational Quantum Algorithms (VQAs) are promising methods for solving combinatorial optimization problems on noisy intermediate-scale quantum (NISQ) devices. However, benchmarking VQAs is difficult due to their stochastic behavior and the lack of standardized performance criteria. This work introduces a general framework for evaluating VQAs applied to Quadratic Unconstrained Binary Optimization (QUBO) problems. The framework uses three complementary metrics: feasibility, quality, and reproducibility. It also introduces a quality diagram that visualizes trade-offs between success probability and computational resources. Reproducibility is formalized using Shannon entropy, and a decision rule is defined for selecting algorithms under resource constraints. As a demonstration, the framework is applied to several VQAs using Conditional Value at Risk (CVaR) cost functions and different shot counts on a 16-qubit QUBO instance. The results show how the framework supports systematic benchmarking and provides a foundation for adaptive algorithm selection in hybrid quantum-classical workflows.
Test suite of quantum algorithms for Noisy Intermediate Scale Quantum (NISQ) computers. The test suite includes benchmark-style code for quantum volume circuits (QV), fairness sampling circuits, quantum telecloning circuits, and other NISQ benchmark style algorithms on small problems (i.e., up to 100 qubits), such as Variational Quantum Eigensolver (VQE), Hamiltonian Simulation, and Grover unstructured search example circuits. These benchmark-style applications are implemented in quantum software packages, mostly IBM's QISKIT, but may include vendor-specific frameworks, such as PyQuil (for Rigetti) or Q\# for Microsoft, or CirQ (for Google) as the test suite grows with the vendor sample. The test suite also includes numerical simulation code for Quantum Alternating Operator Ansatz (QAOA) algorithms, VQE, Hamiltonian Simulation and search examples. Numerical simulation code simulates quantum computers on classical computers, which is only possible for small problem instances; the implementation framework of choice is typically within Python, using the numpy/scipy libraries as well as extensions to the Julia language.
Abstract Quantum optimization has become a leading application for near-term quantumcomputing, and yet many publications compare algorithms against idealizedassumptions and small toy benchmarks. This limits the interpretability, reproducibility,and practical relevance of reported performance gains, particularlyin the noisy intermediate-scale quantum (NISQ) era. In this work, we presenta systematic literature review that investigate quantum optimization beyondtoy benchmarks. Following established SLR protocols, we analyze the literaturealong multiple methodological dimensions, including algorithmic approach,benchmark realism, encoding strategies, hybrid quantum-classical workflows,hardware and noise modeling, evaluation metrics, and reporting practices. Weintroduce a unified taxonomy that captures the interaction between problemformulation, encoding overhead, noise-aware execution, and hybrid optimizationloops. In addition, we propose a reproducibility checklist and scoring rubric toassess reporting completeness and experimental rigor across studies. Instead ofdeveloping new quantum optimization algorithms or making theoretical quantumadvantage claims, the main contribution of this work is that it providesa methodological analysis on benchmarking realism and encoding or evaluationpractices as well as reproducibility rigor in NISQ-era quantum optimization studies.Our review uncovers a continued chasm between algorithmic innovation andevaluation maturity, with the number of publications and diversity of methodsgrowing without corresponding growth in standardized benchmarks, strongclassical baselines, noise-consistent evaluation, or reproducibility.