The Noisy Intermediate-Scale Quantum (NISQ) era poses a challenge for developers: hardware providers expose capabilities through heterogeneous interfaces with proprietary metrics varying widely across providers, hindering informed backend selection. Static characterization metrics - coherence times T1/T2, gate error rates - exhibit limitations: they fail to capture dynamic variability across successive executions, overlook the impact of transpilation, and lack architectural comparability across physically distinct technologies. We propose a Figure of Merit (FoM) based on Grover's algorithm as an algorithmic stress test evaluating quantum backend performance holistically. The metric combines success probability on target states with penalties for non-uniform amplification and leakage to non-marked states, yielding a unified score across hardware architectures. Implemented via the Qonscious framework - a conditional execution platform using polymorphic adapters, it executes agnostically on IBM, IonQ backends, and simulators. Main contributions: (1) proposal and validation of GroverFigureOfMerit, incorporating uniformity and leakage penalties (adapted from GRADE) with emphasis on noise, transpilation, and topological constraints; (2) systematic analysis of heterogeneity across nine quantum providers motivating agnostic metrics; and (3) experimental demonstration via ideal simulators and real-processor noise models, confirming sensitivity to noise, topology, and transpilation overhead. Results confirm the metric distinguishes backend performance under a unified score, capturing intrinsic algorithmic limits. Validation on physical QPUs is identified as a natural next step.
The Noisy Intermediate-Scale Quantum (NISQ) era poses a challenge for developers: hardware providers expose capabilities through heterogeneous interfaces with proprietary metrics varying widely across providers, hindering informed backend selection. Static characterization metrics - coherence times T1/T2, gate error rates - exhibit limitations: they fail to capture dynamic variability across successive executions, overlook the impact of transpilation, and lack architectural comparability across physically distinct technologies. We propose a Figure of Merit (FoM) based on Grover's algorithm as an algorithmic stress test evaluating quantum backend performance holistically. The metric combines success probability on target states with penalties for non-uniform amplification and leakage to non-marked states, yielding a unified score across hardware architectures. Implemented via the Qonscious framework - a conditional execution platform using polymorphic adapters, it executes agnostically on IBM, IonQ backends, and simulators. Main contributions: (1) proposal and validation of GroverFigureOfMerit, incorporating uniformity and leakage penalties (adapted from GRADE) with emphasis on noise, transpilation, and topological constraints; (2) systematic analysis of heterogeneity across nine quantum providers motivating agnostic metrics; and (3) experimental demonstration via ideal simulators and real-processor noise models, confirming sensitivity to noise, topology, and transpilation overhead. Results confirm the metric distinguishes backend performance under a unified score, capturing intrinsic algorithmic limits. Validation on physical QPUs is identified as a natural next step.
Quantum computing holds the potential to provide speedups in solving complex problems that are currently difficult for classical computers. However, the realization of this potential is hindered by the issue of current hardware reliability, primarily due to noise and architectural imperfections. As quantum computing systems rapidly advance, there exists a need to create a generalizable benchmarking tool that can assess reliability across different hardware platforms. In this paper, we introduce GRADE (Grover-based Reliability Assessment for Device Evaluation), an open-source benchmarking toolkit to evaluate the reliability of quantum hardware using a generalized form of Grover's algorithm. GRADE operates by implementing Grover's algorithm to search through a variety of primitive collections that are customizable by the user, analyzing the probability distribution of the results to assess accuracy and stability. This approach aims to evaluate the hardware performance of Grover's algorithm, which is fundamental in unordered search problems, making it an ideal candidate for benchmarking purposes, as it is one of the few generalizable quantum algorithms that provide a direct speedup. Importantly, GRADE can adapt to a wide range of quantum computing platforms, ensuring it can be applied across different hardware architectures. Additionally, our work provides a scoring function for evaluating the hardware performance for multi-target Grover's implementation. Our work validates this approach across a multitude of simulators and hardware platforms from varying providers, demonstrating its adaptability to differing backend providers.
Cloud-accessible quantum processors enable direct execution of quantum algorithms on heterogeneous hardware platforms. Unlike classical systems, however, identical quantum circuits may exhibit substantially different behavior across devices due to architectural variations in qubit connectivity, gate fidelity, and coherence times.
In this work, we systematically benchmark five representative quantum algorithms - Bell state preparation, GHZ state generation, Quantum Fourier Transform (QFT), Grover's Search, and the Quantum Approximate Optimization Algorithm (QAOA) - across trapped-ion, superconducting, and simulator backends using Amazon Braket. Performance metrics including fidelity, CHSH violation, success probability, circuit depth, and gate counts are evaluated.
Our results demonstrate a strong dependence of algorithmic performance on hardware topology and noise characteristics. For example, 10-qubit GHZ states achieved fidelities above 0.8 on trapped-ion hardware, while superconducting platforms dropped below 0.15 due to routing overhead and accumulated two-qubit gate errors. These findings highlight the importance of hardware-aware algorithm selection and provide practical guidance for benchmarking in the NISQ era.
The performance of quantum algorithms on near-term devices is heavily constrained by hardware noise, yet systematic comparisons of algorithmic vulnerability across diverse noise types remain limited. In this study, we benchmark three representative algorithms: quantum teleportation, Grover’s search, and the Quantum Approximate Optimization Algorithm (QAOA), under depolarizing, amplitude damping, phase damping, and thermal relaxation channels. Using exact density-matrix simulations, we characterize algorithmic performance by evaluating state fidelity decay as a function of noise strength. To enable fair, structure-aware comparisons across circuits of varying depths, we introduce a novel metric, the per-gate decay rate. Our analysis reveals that teleportation exhibits the lowest per-gate vulnerability, whereas Grover’s algorithm demonstrates substantially larger, super-linear decay driven by its repeated oracle–diffuser architecture. QAOA displays intermediate and stable noise resilience across system sizes. Finally, we validate our simulation framework through hardware experiments on an IBM Heron processor, demonstrating strong agreement with theoretical predictions (Pearson’s correlation coefficient r = 0.981). This work establishes a quantitative, structure-aware framework for assessing circuit-level noise sensitivity and offers actionable insights for algorithm selection in the noisy intermediate-scale quantum era.
Grover's quantum search algorithm provides a proven quadratic speedup over classical linear search for unstructured databases. While its theoretical advantage is well established, experimental validation on real noisy intermediate-scale quantum (NISQ) hardware remains limited by noise, decoherence, and hardware constraints. In this work, we present an experimental comparison between classical linear search and Grover's quantum search algorithm executed on IBM Quantum real superconducting hardware using Qiskit Runtime. A four-qubit implementation corresponding to a 16-element search space is evaluated across multiple experimental runs. The results demonstrate consistent amplitude amplification, achieving an average success probability of 54.25% and an amplification factor of 8.68× over random guessing. Although classical execution remains faster in wall-clock time due to quantum hardware overhead, the experiment empirically validates the algorithmic advantage of Grover's search and quantifies the performance gap between ideal and real quantum processors in the NISQ era.
Quantum computing frameworks---Qiskit, Cirq, PennyLane, QPanda, and others---each provide their own circuit representations, compilation pipelines, and execution APIs. While individually capable, they are peers: code written for one cannot run on another without manual translation, and optimizations developed for one platform cannot benefit the rest. What is missing is not another framework, but a middleware layer that unifies them. We present QuoNic, a backend-agnostic quantum middleware that treats existing frameworks as compilation targets rather than competitors. QuoNic introduces three key innovations: (1) a vendor-neutral Intermediate Representation (IR) supporting high-level gate semantics that compile down to any backend's native instructions; (2) an intelligent scheduler that automatically selects the optimal backend and simulation method based on circuit features, achieving 1.30x median overhead relative to the best backend across 694 benchmark circuits; and (3) groverize(), a dynamic-to-static compilation pass that transforms repeat-until-success loops into coherent Grover circuits executable on any backend. Evaluated across 12 experiments covering cross-platform consistency, compilation optimization, and real-hardware validation on three quantum platforms, QuoNic achieves sub-millisecond compilation, consistent results across 8 backend configurations, and scales to 40 qubits for Clifford circuits.
We present a method for characterizing the performance of noisy quantum processors using discrete time crystals. Deviations from ideal persistent oscillatory behavior give rise to numerical scores by which relative quantum processor capabilities can be measured. We construct small sets of qubit layouts that cover the full topology of a target system, and execute our metric over these sets on a wide range of IBM Quantum processors. We show that there is a large variability in scores, not only across multiple processors, but between different circuit layouts over individual devices. The stability of results is also examined. Our results suggest that capturing the true performance characteristics of a quantum system requires interrogation over the full device, rather than isolated subgraphs. Moreover, the disagreement between our results and other metrics indicates that benchmarks computed infrequently are not indicative of the real-world performance of a quantum processor. This method is platform agnostic, simple to implement, and scalable to any number of qubits forming a linear-chain, while simultaneously allowing for identifying ill-performing regions of a device at the individual qubit level.