Quantum networking protocol advancement heavily relies on classical simulation for algorithm design, validation, and optimization before deployment on quantum hardware. However, selecting the quantum computing framework significantly influences simulation performance memory utilization, and productivity. This research paper renders a systematic performance analysis of quantum computing simulators for network protocol implementation across six important quantum frameworks: Qiskit, CirQ, Amazon Braket SDK, PyQuil, Q#, and PennyLane. We have evaluated it by implementing nine fundamental networking protocols, conducting 1,620 simulation executions with N = 30 trials per measurement under both ideal and noisy conditions. We have observed significant performance disparities: PyQuil accomplishes 405× faster total execution than Qiskit with 1,237× lower peak memory consumption, whereas Q# demonstrates comparable speed using JIT compilation. while using real depolarizing noise (p=0.001) all frameworks exhibit 0.1–1.0% teleportation fidelity degradation.
Quantum networking protocol advancement heavily relies on classical simulation for algorithm design, validation, and optimization before deployment on quantum hardware. However, selecting the quantum computing framework significantly influences simulation performance memory utilization, and productivity. This research paper renders a systematic performance analysis of quantum computing simulators for network protocol implementation across six important quantum frameworks: Qiskit, CirQ, Amazon Braket SDK, PyQuil, Q#, and PennyLane. We have evaluated it by implementing nine fundamental networking protocols, conducting 1,620 simulation executions with N = 30 trials per measurement under both ideal and noisy conditions. We have observed significant performance disparities: PyQuil accomplishes 405× faster total execution than Qiskit with 1,237× lower peak memory consumption, whereas Q# demonstrates comparable speed using JIT compilation. while using real depolarizing noise (p=0.001) all frameworks exhibit 0.1–1.0% teleportation fidelity degradation.
The increasing complexity of quantum algorithms and the limitations of current Noisy Intermediate-Scale Quantum (NISQ) hardware underscore the importance of efficient classical simulators. To support informed decision-making by users of quantum circuit simulators, we benchmark seven statevector-based quantum circuit simulators (Qiskit, Qulacs, Qibo, Qsimov, Cirq, Pennylane and the Intel Quantum Simulator (IQS)) on a multicore node of the Lusitania high-performance computing (HPC) system. We evaluate their performance in terms of execution time, memory usage and core scalability using Grover’s algorithm, the quantum Fourier transform (QFT), and quantum volume (QV) circuits, across qubit counts ranging from 3 to 30. Our results reveal that Qulacs offers the best performance for circuits below 22 qubits, while Qiskit becomes the fastest for larger and more complex circuits. Qiskit and Qulacs achieve the most efficient parallel performance across multiple cores, while others display limited scaling benefits. IQS shows the lowest memory consumption in QFT and QV benchmarks for systems under 24 qubits; however, it suffers from higher execution times, particularly for Grover’s algorithm. The experimental implementation of Qsimov consistently underperforms in both runtime and scalability; for this reason, it is employed as a baseline in our measurements, serving to highlight the importance of performance optimizations in statevector-based quantum circuit simulators. Previous findings provide a comprehensive performance landscape to guide researchers in selecting appropriate simulators for both standard and large-scale quantum workloads on HPC infrastructures.
Quantum computing has emerged as a transformative technological advancement with the potential to challenge the foundations of classical cryptography. Traditional cryptographic algorithms, such as RSA, rely heavily on the computational hardness of problems like integer factorization—a problem quantum algorithm like Shor's can theoretically solve efficiently. This research investigates the practical capabilities and performance limitations of three prominent quantum simulation frameworks: Qiskit, Cirq, and QuTiP. The study aims to analyze and compare their efficiency in simulating the decryption process for encrypted data of varying bit lengths—specifically 8 -bit, 16 -bit, 32 -bit, 64 -bit, 128 -bit, and 512 -bit keys. By simulating classical decryption schemes through quantum circuits, we assess the computational resources, execution time, and overall performance of each platform. This benchmarking helps to highlight how effectively these simulators can model cryptographic tasks and where they currently fall short in terms of scalability and execution time. The results offer valuable insights into the practicality of deploying quantum computing for real-world cryptographic operations and outline the current limitations imposed by simulation-based approaches, especially as key sizes grow in complexity. Ultimately, this research contributes to a deeper understanding of how existing quantum tools perform under cryptographic workloads and what improvements are necessary to make quantum-assisted cryptanalysis viable at scale.
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
Abstract Quantum computing represents a substantial threat to contemporary cryptographic systems, primarily due to the vulnerabilities introduced by quantum algorithms such as Shor’s algorithm, Deutsch’s algorithm, and Grover’s algorithm. In this study, advanced quantum computing frameworks, including Curl, PennyLane, and Qiskit, were selected to model these algorithms and to facilitate a comprehensive comparative analysis. A generalization scheme for quantum computing and its limitations are discussed. The pseudo-codes of the principal algorithms and their implications for cryptography are presented in detail. An empirical evaluation of time and memory consumption demonstrates that the Qiskit library exhibits superior performance in the implementation of the three aforementioned algorithms, requiring fewer lines of code, reduced execution time, and lower memory usage. This work offers insights into the efficiency of quantum computing algorithms and frameworks and highlights the broader impact of advances in quantum computing on traditional cryptographic paradigms.
Quantum machine learning (QML) has emerged as a promising field, combining the power of quantum computing with classical machine learning techniques to solve complex computational tasks. As the demand for efficient quantum simulations grows, multiple QML frameworks, including PennyLane, Qiskit, and TensorFlow Quantum (TFQ), have been developed to facilitate hybrid quantum-classical computations. This study aims to evaluate and compare the performance of three leading QML frameworks PennyLane, Qiskit, and TensorFlow Quantum in simulating quantum machine learning models, focusing on accuracy, execution time, and noise tolerance. The study examined three leading quantum machine learning (QML) frameworks PennyLane (v0.24), Qiskit (v0.43.2), and TensorFlow Quantum (TFQ, v0.7) each tested on a 64-bit Ubuntu Linux system with an Intel Core i7 processor, 32 GB RAM, and NVIDIA GeForce RTX 3080 GPU. Quantum algorithms like variational quantum circuits (VQCs) and quantum support vector machines (QSVMs) were simulated using these frameworks. Various quantum gates, including Pauli-X, Y, Z, Hadamard, CNOT, and Rotation gates (Rx, Ry, Rz), were used for parameter optimization and quantum superposition. The Iris and MNIST datasets were adapted for quantum encoding using Amplitude and Angle Encoding for binary classification tasks. Simulations were run on classical computers, while select circuits were executed on IBM Quantum’s Falcon r5.11 processor to compare hardware performance. PennyLane achieved the highest accuracy (92%) in quantum simulations, excelling in hybrid quantum-classical model integration. TensorFlow Quantum provided the fastest execution time, especially in classical simulations, making it suitable for rapid prototyping. Qiskit produced deeper quantum circuits but had higher error rates (6%) on real quantum hardware, highlighting noise and decoherence effects. The VQC models optimized by stochastic gradient descent (SGD) showed significant improvements in model performance, and QSVMs effectively classified both linearly separable and non-separable datasets, demonstrating the potential of quantum algorithms over classical approaches. Statistical analysis confirmed the significance of the results (p < 0.05). PennyLane is the most robust framework for hybrid quantum-classical models, while TensorFlow Quantum is suited for rapid prototyping. Qiskit, despite longer execution times, excels in hardware implementation. The choice of framework should align with the specific needs of quantum machine learning tasks.
Abstract The rapid evolution of computational paradigms has necessitated a critical reevaluation of network security protocols, particularly as they migrate from classical cloud infrastructures to emerging quantum environments. Evaluating the overhead and efficiency of these protocols requires sophisticated Performance Analysis(PA) methodologies that can bridge traditional microservices and highly parallel quantum simulations. This paper proposes a comprehensive, Architecture-Agnostic Framework(AAF) designed to measure and compare the performance of cryptographic protocols across these divergent domains. By leveraging advanced profiling techniques, deterministic modeling, and distributed tracing, the proposed methodology aims to provide researchers with actionable insights into computational bottlenecks and resource utilization. Keywords: Performance Analysis(PA), Architecture-agnostic framework(AAF) 1.Introduction The transition from monolithic architectures to distributed cloud-native environments has fundamentally altered how network security protocols are deployed and maintained. In contemporary cloud computing, cryptographic protocols must operate seamlessly across containerized microservices without introducing prohibitive latency. Simultaneously, the advent of quantum computing introduces a paradigm shift, promising unprecedented computational power while threatening classical encryption methods. Consequently, understanding the performance profiles of network security protocols in both classical cloud and quantum-simulated environments has become an urgent academic and industrial priority. Rigorous performance analysis is required to ensure that next-generation cryptographic models do not cripple system throughput or escalate energy consumption. Despite the critical nature of this domain, formulating a unified methodology for comparing security protocols across cloud and quantum systems presents a complex, multi-dimensional problem. The core scope of this research involves defining how computational overhead, latency, and resource utilization can be accurately measured when the underlying hardware architectures are fundamentally different. Classical cloud systems rely on heterogeneous processing elements like CPUs and GPUs, whereas quantum environments—currently modeled through massively parallel simulations—operate on entirely different probabilistic principles. Bridging this measurement gap requires robust frameworks capable of abstracting architectural complexities while delivering precise, comparable performance metrics. Existing approaches to performance analysis are largely insufficient for addressing the nuances of cross-paradigm security protocol evaluation. First, while current distributed tracing visualizations are effective for individual request monitoring, they often fall short in providing a comprehensive understanding of aggregate performance across diverse distributed nodes (Leone & Traini, 2023). Second, existing statistical methods utilized for evaluating optimization algorithms can lead to logical paradoxes and biased rankings if they lack strict isomorphism criteria, rendering them inadequate for benchmarking complex, black-box quantum security protocols (Jing et al., 2024). These limitations highlight the urgent need for a specialized evaluation strategy that transcends the boundaries of standard software profiling. To address these critical gaps in the literature, this paper introduces a unified evaluation framework for network security protocols. The primary contributions of this paper are as follows: We propose a novel, architecture-agnostic performance analysis pipeline that integrates microservice tracing with parallel simulation profiling to evaluate security protocols. We outline a methodology for contrasting classical cloud encryption overhead with hypothetical quantum key distribution (QKD) simulations, providing a scalable evaluation plan. 2.Distributed Tracing and Process Mining in the Cloud The monitoring of cloud-based microservices heavily relies on distributed tracing and event log analysis. Practitioners frequently utilize distributed tracing tools to collect and visualize remote procedure calls (RPC) and associated events across independent services (Leone & Traini, 2023). The core idea of these approaches is to construct a holistic view of an end-to-end request, allowing developers to pinpoint latency spikes and functional errors within a distributed security protocol. However, a major weakness is that while these tools excel at isolating individual requests, they struggle to provide meaningful aggregate performance visualizations necessary for evaluating sustained protocol throughput (Leone & Traini, 2023). Process mining offers a complementary log-based approach to performance analysis by measuring business and system processes. Systematic literature reviews in this domain have led to the creation of frameworks for categorizing performance analysis based on context and data availability (Milani & Maggi, 2018). The strength of process mining lies in its ability to reconstruct the actual execution flow of security protocols from event logs, highlighting deviations from expected behavior. In comparison to this work, our proposed framework builds upon log-based process evaluation but extends it to capture the unique hardware-level metrics required for quantum-simulated protocol analysis. 2.1.Parallel System Modeling and Heterogeneous Profiling Analyzing massive scale environments requires moving beyond empirical logging toward sophisticated mathematical modeling and hardware-aware profiling. Previous techniques for analyzing massively parallel systems utilized stochastic simulations, but recent tools apply systems of ordinary differential equations (ODEs) to improve scalability and predict higher-moment performance measures (Stefanek et al., 2010). The primary advantage of ODE-based tools is their ability to handle massive state spaces efficiently, which is particularly relevant when simulating the probabilistic states of quantum security protocols. A notable limitation, however, is the presence of "switch points" that can introduce errors in differential equation approximations (Stefanek et al., 2010). Furthermore, the integration of diverse computational units—such as CPUs, GPUs, and FPGAs—necessitates architecture-agnostic profiling tools. Efforts such as Adaptyst aim to enable heterogeneous performance analysis for scientific workloads utilizing advanced kernel mechanisms like eBPF (Graczyk et al., 2025). This heterogeneous profiling is crucial for optimizing task dispatching across different platforms. Our framework adopts the principles of heterogeneous hardware profiling to measure how classical security protocols utilize modern cloud accelerators compared to simulated quantum components. 2.2.Automated Diagnostics and Ontology-Guided Analysis The increasing complexity of parallel and service-oriented architectures has driven the development of automated, programmatic analysis tools. Python-based APIs have been proposed to facilitate high-level, flexible performance analysis, automating the detection of load imbalances and scalability bottlenecks in parallel applications (Cankur et al., 2024). The strength of programmatic APIs is their ability to reduce developer effort by providing configurable methods for correlating distinct performance metrics (Cankur et al., 2024). However, these tools are generally constrained by the lack of inherent semantic understanding of the underlying system architecture. To mitigate this semantic gap, researchers have proposed ontology-guided performance analysis frameworks. By adding semantics to multi-dimensional measurement data, it is possible to systematically detect and diagnose performance bottlenecks in complex software stacks, such as blockchain environments (Klenik & Pataricza, 2021). This approach standardizes the evaluation workflow across data acquisition, representation, and analysis phases. Compared to existing methods, our work directly integrates ontological principles to map classical cloud security metrics to their equivalent quantum-simulated counterparts, ensuring rigorous comparative evaluation. 3.Multi-layered Architecture To systematically compare network security protocols across cloud and quantum environments, we propose a multi-layered, architecture-agnostic performance analysis framework. This framework is specifically designed to reconcile the disparity between classical microservice execution and massively parallel quantum simulations. By synthesizing distributed tracing with ODE-based scalability modeling, the framework isolates cryptographic processing time from underlying network latency. The architecture assumes a hybrid deployment where classical nodes communicate with simulated quantum co-processors, necessitating synchronized profiling at both the software and hardware levels. The methodology follows a structured, numbered pipeline to ensure reproducibility and rigor during the evaluation phase: Semantic Workload Definition: Define the security protocol tasks using an ontology-guided approach, mapping specific cryptographic functions to execution targets (e.g., cloud container vs. quantum simulator) (Klenik & Pataricza, 2021). Data Acquisition and Tracing: Deploy low-overhead operating system tracing (such as eBPF or the Linux Trace Toolkit) to capture time domain samples and hardware performance counters (Dagenais et al., 2005). Aggregate Visualization & Modeling: Utilize programmatic APIs to aggregate individual remote procedure calls, computing higher-moment performance analysis via differential equation approximations to predict scalability (Stefanek et al., 2010)(Cankur et al., 2024). Isomorphic Comparison: Apply strict isomorphism criteria to ensure that the performance evaluation remains unaffected by the specific modeling approach used in either environment (Jin