Benchmarking noisy quantum computers for variational workloads requires more than a single best energy number: two backends may reach similar energies while differing sharply in convergence speed, stability, and parameter sensitivity. We present a compact uncertainty quantification driven benchmarking study for the Variational Quantum Eigensolver (VQE), combining Bayesian optimization with Metropolis-Hastings refinement to search a backend conditioned VQE landscape and characterizing each backend by its best observed energy, evaluations to target, objective dispersion, and Sobol sensitivity fingerprint. On a 92 parameters LiH VQE across seven IBM fake backends, Brisbane wins overall (best energy 1.0207, top normalized quality), Kyoto matches its energy but converges five times more slowly, and Osaka trades a modest energy penalty for near optimal speed and low dispersion. The Sobol fingerprints further show that the dominant VQE parameters differ across backends, indicating backend specific robust regions rather than a universal calibration. Uncertainty quantification therefore provides a practical application level view of backend quality for near term variational workloads.
Benchmarking noisy quantum computers for variational workloads requires more than a single best energy number: two backends may reach similar energies while differing sharply in convergence speed, stability, and parameter sensitivity. We present a compact uncertainty quantification driven benchmarking study for the Variational Quantum Eigensolver (VQE), combining Bayesian optimization with Metropolis-Hastings refinement to search a backend conditioned VQE landscape and characterizing each backend by its best observed energy, evaluations to target, objective dispersion, and Sobol sensitivity fingerprint. On a 92 parameters LiH VQE across seven IBM fake backends, Brisbane wins overall (best energy 1.0207, top normalized quality), Kyoto matches its energy but converges five times more slowly, and Osaka trades a modest energy penalty for near optimal speed and low dispersion. The Sobol fingerprints further show that the dominant VQE parameters differ across backends, indicating backend specific robust regions rather than a universal calibration. Uncertainty quantification therefore provides a practical application level view of backend quality for near term variational workloads.
We present an uncertainty quantification (UQ) framework for application level benchmarking and characterization of noisy quantum backends. The framework compares two workload classes under one statistical pipeline: noisy intermediate scale quantum (NISQ) variational quantum algorithms (VQAs) and Quantum Singular Value Transformation (QSVT) based Green's function reconstruction. For the VQA branch, we evaluate ten benchmark families spanning chemistry, optimization, simulation, compiling, linear solving, partial differential equations, metrology, error correction, tomography, and channel fidelity estimation. For the QSVT branch, we reconstruct orbital resolved Green's functions and spectral peaks from a block encoded real time propagator. The workflow combines Bayesian optimization, posterior distribution refinement, sensitivity analysis, robust parameter density estimation, backend ranking, noise correlation, and resource estimation analysis. Instead of reporting only one best parameter vector, the framework identifies robust parameter regions, residual gaps to ideal behavior, backend specific failure modes, and calibration sensitive uncertainty. The result is a common benchmark for variational and non-variational workloads that measures how reliably each backend reaches useful task level behavior.
Quantum computing offers speedups, but NISQ processors face hardware-induced errors that degrade fidelity and reproducibility. We introduce an uncertainty-aware benchmarking framework that combines uncertainty quantification with global sensitivity analysis to evaluate not only peak fidelity but also its reliability over time. Using Bayesian Optimization with SGLD refinement under fixed budgets, seeds, bounds, and trust-region rules, and repeating runs across days, we capture calibration drift and quantify efficiency, stability, landscape complexity, and maintenance cost. A noise-aware Gaussian process surrogate provides scalable sensitivity estimates without error mitigation. Applied to VQAMET and VQC on three IBMQ backends, the framework delivers actionable guidance for co-tuning and backend selection, complementing and often outperforming quantum volume style metrics.
The Variational Quantum Eigensolver (VQE) is a leading algorithm for estimating molecular ground-state energies on near-term quantum hardware, with applications spanning quantum chemistry, materials science, and drug discovery. As VQE workloads are increasingly deployed through cloud-based ``VQE-as-a-service'' pipelines, they become exposed to adversaries such as compromised service components, malicious co-tenants, or insiders in the transpilation stack, any of which can corrupt results before they reach the user. A range of attacks on variational quantum circuits has been proposed, but each has been studied in isolation: some on quantum classifiers with accuracy-based metrics, others on variational quantum algorithms with energy-error metrics. This lack of a common evaluation setup makes their relative severity difficult to compare and leaves the security of VQE poorly characterized. In this work, we present \textbf{VQE-AdvBench}, the first unified red-teaming benchmark for the Variational Quantum Eigensolver, systematizing these attacks under a single evaluation protocol to rigorously assess VQE's adversarial robustness. We organize attacks along a black-, gray-, and white-box access taxonomy, and evaluate seven representative attack scenarios -- the QTrojan circuit backdoor, the QDoor parameter backdoor, parameter-space adaptations of FGSM and PGD, and three QNBAD noise-induced variants -- over a fixed molecule-ansatz-backend-metric configuration, on H2 and H3+ across five noise-calibrated IBM backends. Our results reveal a clear severity ordering: noise-induced attacks that manipulate the Zero-Noise Extrapolation (ZNE) pipeline are the most damaging (up to 8.84× error amplification), followed by the QTrojan circuit-level backdoor (7.52×), while the QDoor parameter-level backdoor is the least effective, yielding only marginal amplification (up to 1.37×).
Quantum chemistry is one of the most promising applications of quantum computers in the near future. For noisy intermediate-scale quantum devices, the quantum-classical hybrid framework based on the variational quantum eigensolver (VQE) has become the method of choice. In the literature, there are many different variants of VQE, but it is not known which one is optimal for a given molecule. For this purpose, we perform a thorough benchmarking on more than ten different kinds of VQE ansatzes (in systems up to 30 qubits), based on their performance on the energy accuracy, runtime until convergence, and number of parameters. Our results show that the ADAPT ansatz can be used to obtain more accurate energy for small systems (below 14 qubits), but it costs much more computational resources. For larger molecules, UCCSD0 has better performance. However, all the tested ansatzes can hardly reach chemical accuracy at stretched bond lengths. Our results were obtained using MindSpore Quantum, where the codes and the benchmarking toolkit are publicly available at Gitee.
ABSTRACT The Variational Quantum Eigensolver (VQE) is a widely studied hybrid classical‐quantum algorithm for approximating ground‐state energies in molecular and materials systems. This study benchmarks the performance of the VQE for calculating ground‐state energies of small aluminum clusters (, , and ) within a quantum‐density functional theory (DFT) embedding framework, systematically varying key parameters: (I) classical optimizers, (II) circuit types, (III) number of repetitions, (IV) simulator types, (V) basis sets, and (VI) noise models. All calculations were performed using quantum simulators to evaluate VQE performance under both idealized and noise‐augmented conditions. Our findings demonstrate that certain optimizers converge efficiently, while circuit choice and basis set selection have a marked impact on energy estimates, with higher‐level basis sets closely matching classical computation data from Numerical Python Solver (NumPy) and Computational Chemistry Comparison and Benchmark DataBase (CCCBDB). To approximate realistic conditions, we employed IBM noise models to simulate the effects of hardware noise. The results showed close agreement with CCCBDB benchmarks, with percent errors consistently below 0.2%. The results demonstrate that VQE can approximate energy estimates under simulated conditions for small aluminum clusters and highlight the importance of optimizing quantum‐DFT parameters to balance computational cost and precision. This work contributes to ongoing efforts to benchmark VQE in practical settings and lays the groundwork for future benchmarking tools for quantum chemistry and materials applications.
Abstract The variational quantum eigensolver (VQE) is a hybrid quantum–classical algorithm for preparing ground states in the current era of noisy devices. The classical component of the algorithm requires a large number of measurements on intermediate parameter values that are typically discarded. However, intermediate steps across many calculations can contain valuable information about the relationship between the quantum circuit parameters, resultant measurements, and noise specific to the device. This information would be useful when analysing families of Hamiltonians such as for potential energy landscapes for molecular interaction with catalytic surfaces or confinement in metal–organic-frameworks. In this work, we use supervised machine learning on the intermediate parameter and measurement data to amortise prediction of optimal final parameters across families of Hamiltonians. Our technique optimises parameters leading to chemically accurate ground state energies much faster than conventional techniques. It requires significantly fewer iterations and simultaneously shows the ability to find noise-aware angles if trained on noisy devices even with noise drift. We demonstrate this technique on IBM quantum devices by predicting ground state energies of H 2 for one and two qubits; H 3 for three qubits; and HeH + for four qubits where it finds optimal angles using only modelled data for training. Our technique amortises classical optimisation across multiple VQE runs, resulting in significantly faster overall classical optimisation without additional quantum overhead compared to running VQE.