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 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.
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.
Abstract: This paper explores hybrid quantum-classical optimization algorithms implemented across multiple quantum software development kits (SDKs): Qiskit and Cirq,. We evaluate the Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA) in terms of convergence rate, final cost value, and fidelity, using both simulators and real quantum hardware backends. Our results offer comparative insights into SDK performance and propose a standardized benchmarking approach for future quantum algorithm research. Keywords: Hybrid Quantum Algorithms, Qiskit, Cirq, VQE, QAOA, Quantum SDKs, Quantum Optimization, NISQ
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.
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.
Autonomous coding agents perform well on many conventional programming tasks, but scientific computing demands a rigorous validation paradigm that extends beyond simple functional test completion: generated code must preserve physical fidelity, differentiable workflows, framework-native semantics, and scalable representations. We introduce Open Research Benchmark for Integrated Tasks in Quantum Computing (ORBIT-Q) to address this gap. At its core, ORBIT-Q contributes a carefully curated suite of complex, research-level quantum workflows that serves as a challenging testbed for modern scientific programming. ORBIT-Q combines a rigorous multi-tier verification pipeline to support two orthogonal comparisons: different agent harness and model configurations at a fixed quantum software framework, and different quantum software frameworks at a fixed agent. In our systematic evaluations, TensorCircuit-NG (TC) exhibits the highest capability and performance efficiency among the evaluated quantum software frameworks under agent-driven programming, and Codex with GPT-5.5 is the strongest tested agent configuration on TC. However, a significant performance and design gap remains between frontier autonomous agents and human expert reference implementations. We further evaluate two efficiency dimensions: agent-side resource use and artifact-side runtime. Together, these results establish ORBIT-Q as a rigorous benchmark for autonomous scientific programming, framework-agent synergy, and quantum software performance.
Fault-tolerant quantum computing requires understanding how error-correcting codes perform on diverse physical hardware. This is typically assessed via noisy stabilizer simulation of logical circuits at HPC scale, combined with a noise model that yields a logical error rate for the relevant code distances and depths. The uniform depolarizing model is the standard baseline, but its homogeneous assumptions fail to capture the heterogeneity, asymmetries, and correlations of real devices, where Pauli, measurement, and spatio-temporal errors are not weakly coupled. Yet these same structured features create opportunities for joint code-hardware co-design, motivating noise models that more faithfully reflect target hardware while remaining tractable to simulate.
We introduce FTPrimitiveBench, a systematic benchmarking approach for studying how logical primitives interact with hardware-motivated noise. It supports both custom specifications and representative structured noise families: Pauli bias, measurement bias, and spatial or spatio-temporal non-uniformity -- together with generators for core surface-code Clifford primitives: logical memory, lattice surgery, transversal logical Hadamard, and the logical phase gate via lattice surgery. We find that structured noise affects these primitives in qualitatively distinct ways, with outcomes shaped by the interplay between noise model, primitive, and decoder choice. These results extend memory benchmarks to active logical computation, where the interaction between noise structure and primitive implementation matters. By standardizing the link between noise-model specification and primitive construction, FTPrimitiveBench enables reproducible comparative studies of QEC protocols and decoders, supporting hardware-aware co-design of fault-tolerant architectures. Code: https://github.com/ShuwenKan/FTPrimitiveBench.