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.
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.
Academic quantum computing platforms often face unique challenges in executing quantum workloads due to fragmented software environments and limited engineering support. Unlike commercial ecosystems, academic devices typically evolve without full-stack integration in mind, making it difficult to run complex applications—such as variational quantum algorithms (VQA)—reliably and efficiently. Issues such as incompatible software layers and lack of automated job management significantly increase the overhead of theory-experiment collaboration. To address these challenges, we develop a modular, end-to-end workflow that decouples application-layer code from low-level hardware control, automates circuit submission and result collection, and supports fine-grained circuit-level job scheduling and recovery. The architecture employs a dual-end application programming interface (API) design, enabling robust operation across unstable or resource-constrained hardware backends. For practical use, the framework is lightweight and user-friendly, allowing rapid prototyping of full-stack workflows using basic Python tools. We validate this workflow on a high-fidelity trapped-ion quantum computer by demonstrating a variational quantum eigensolver (VQE) experiment with a classically bootstrapped ansatz initialization technique. The system successfully executed over 60,000 circuits across multiple molecular test cases with minimal human intervention, highlighting the framework’s effectiveness in enabling reproducible, resilient quantum experimentation in academic settings.
Hybrid quantum-high performance computing (Q-HPC) workflows are emerging as a key strategy for running quantum applications at scale in current noisy intermediate-scale quantum (NISQ) devices. These workflows must operate seamlessly across diverse simulators and hardware backends since no single simulator offers the best performance for every circuit type. Simulation efficiency depends strongly on circuit structure, entanglement, and depth, making a flexible and backend-agnostic execution model essential for fair benchmarking, informed platform selection, and ultimately the identification of quantum advantage opportunities. In this work, we extend the Quantum Framework (QFw), a modular and HPC-aware orchestration layer, to integrate multiple local backends (Qiskit Aer, NWQ-Sim, QTensor, and TN-QVM) and a cloud-based quantum backend (IonQ) under a unified interface. Using this integration, we execute a number of non-variational as well as variational workloads. The results highlight workload-specific backend advantages: while Qiskit Aer’s matrix product state excels for large Ising models, NWQ-Sim not only leads on large-scale entanglement and Hamiltonian but also shows the benefits of concurrent subproblem execution in a distributed manner for optimization problems. These findings demonstrate that simulator-agnostic, HPC-aware orchestration is a practical path toward scalable, reproducible, and portable Q-HPC ecosystems, thereby accelerating progress toward demonstrating quantum advantage.
Quantum machine learning (QML) stands at the intersection of quantum computing and artificial intelligence, offering the potential to solve problems that remain intractable for classical methods. However, the current landscape of QML software frameworks suffers from severe fragmentation: models developed in TensorFlow Quantum cannot execute on PennyLane backends, circuits authored in Qiskit Machine Learning cannot be deployed to Amazon Braket hardware, and researchers who invest in one ecosystem face prohibitive switching costs when migrating to another. This vendor lock-in impedes reproducibility, limits hardware access, and slows the pace of scientific discovery. In this paper, we present a framework-agnostic quantum neural network (QNN) architecture that abstracts away vendor-specific interfaces through a unified computational graph, a hardware abstraction layer (HAL), and a multi-framework export pipeline. The core architecture supports simultaneous integration with TensorFlow, PyTorch, and JAX as classical co-processors, while the HAL provides transparent access to IBM Quantum, Amazon Braket, Azure Quantum, IonQ, and Rigetti backends through a single application programming interface (API). We introduce three pluggable data encoding strategies (amplitude, angle, and instantaneous quantum polynomial encoding) that are compatible with all supported backends. An export module leveraging Open Neural Network Exchange (ONNX) metadata enables lossless circuit translation across Qiskit, Cirq, PennyLane, and Braket representations. We benchmark our framework on the Iris, Wine, and MNIST-4 classification tasks, demonstrating training time parity (within 8\% overhead) compared to native framework implementations, while achieving identical classification accuracy.
Quantum circuit simulation remains essential for developing and validating quantum algorithms, especially as current quantum hardware is limited in scale and quality. However, the growing diversity of simulation methods and software tools creates a high barrier to selecting the most suitable backend for a given circuit. We introduce Maestro, a unified interface for quantum circuit simulation that integrates multiple simulation paradigms - state vector, MPS, tensor network, stabilizer, GPU-accelerated, and p-block methods - under a single API. Maestro includes a predictive runtime model that automatically selects the optimal simulator based on circuit structure and available hardware, and applies backend-specific optimizations such as multiprocessing, GPU execution, and improved sampling. Benchmarks across heterogeneous workloads demonstrate that Maestro outperforms individual simulators in both single-circuit and large batched settings, particularly in high-performance computing environments. Maestro provides a scalable, extensible platform for quantum algorithm research, hybrid quantum-classical workflows, and emerging distributed quantum computing architectures.
Early quantum computers are being offered by Cloud providers, with the ability to run Hybrid Quantum-Classical (HQC) applications that span Quantum and Classical (binary) computing resources. These HQC algorithms help benchmark and validate the performance of such near-term quantum hardware. Equally important is the software platform that allows a remote user to program the quantum computer on the cloud. The recent Qiskit Runtime platform from IBM allows a seamless and prioritized access to quantum backends and co-located classical hardware within their hybrid cloud for optimized execution of HQC workloads. In contrast, IBM’s Circuit API offers finer application definition but the classical resources are not co-located but placed farther away. Here, we study and contrast the execution flows of a Variational Quantum Eigensolver (VQE) application defined using Qiskit Runtime, and an equivalent implementation using the Circuit API. We quantify the latencies of the various components in these execution. This offers insights on how to better orchestrate the execution of quantum applications across such hybrid resources.
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.