We evaluate calibration-based digital twins for IBM Quantum hardware, aiming to reproduce hardware measurement outcomes on classical simulators. We present a workflow that builds twins from downloadable calibration CSV files by mapping coherence times, gate and readout error rates, and operation durations to thermal-relaxation, depolarizing, and readout error channels, while reconstructing a directed coupling map to restore connectivity constraints during transpilation. We compare four twin variants (CSV-built, backend-derived simulator, backend-derived noise model, and fake-backend snapshots) under a common execution and validation protocol. Experiments on two IBM QPUs, ibm_brisbane and ibm_sherbrooke, use randomized five-qubit circuits of depths 10, 20, and 30 across four optimization levels. Weighted Jaccard similarity indicates that twins constructed from downloadable calibration CSV data often achieved the closest agreement with hardware, while backend-derived twins provided competitive and practical baselines. The results further show that agreement depends on both the target device and the transpilation settings, underscoring the need to validate digital twins for the specific execution setup rather than assuming transferability across devices.
This work evaluates the applicability of GANs in generating synthetic time series data. Hypoid gear vibration data was used as experimental data to test models. GANs for time series can be divided into two different types: discrete type (discrete time series) and continuous type (continuous time series). In time series, there are complex time-dependent features and their attributes, for example, when using biometric data, ECG characteristics will depend on the age and health of the individual. TimeGAN is a generative time series model that combines unsupervised learning using GAN with supervised autoregressive learning. TimeGAN model performace was evaluated using predictive score and visualization using PCA and tSNE analysis for original and synthetic data. The best predictive score was 0.062, however visual evaluation using tSNE and PCA analysis revealed that generated data was not sufficiently similar to the real. It can be concluded that the application of GANs to time series presents difficulties that are not present in image generation models. Continuing this work could lead to testing other time series GANs in the future, e.g. DoppelGANger whose synthetic data structure does not require complex reconstruction.
