We present a tomographic reconstruction procedure that exploits the symmetry of the SU(1,1) group existing in some physical systems. The tomographic algorithm is derived analytically and the convergence of the procedure is tested on Monte Carlo simulated data.
By systematically introducing small symmetry-violating terms into quantum gates, we examine whether such perturbations can reproduce the tomographic noise patterns observed on actual quantum hardware. This study fits into the broader context of quantum computing and quantum information processing, where realistic noise modeling remains essential for understanding device behavior.Our analytical framework mirrors Qiskit’s tomography procedures and shot model, using transparent noise control. Our results show that weak generator perturbations can reproduce average deviations in Pauli Transfer Matrices with high fidelity (given the recorded hardware noise characteristics). These findings suggest that symmetry-violating generators provide a phenomenological model for relevant classes of hardware noise. This approach offers a reproducible analytic baseline for testing noise hypotheses and calibrating simplified models against experimental data.Beyond this descriptive role, the appearance of stable symmetry-violating perturbations in the fitted models supports their physical relevance: they indicate that real devices operate slightly outside the symmetry-preserving regime assumed in standard noise models, and that these deviations leave identifiable signatures in tomographic reconstructions. Because the analytical framework isolates these violations at the generator level, it enables principled diagnosis of non-unitality and related effects. As a result, the model not only reproduces experimental process tomography data but also identifies which symmetry assumptions fail and by what magnitude, providing actionable information for calibration, simulation, and the development of symmetry-sensitive mitigation and error-correction strategies.
Given an experimental setup and a fixed number of measurements, how should one take data to optimally reconstruct the state of a quantum system? The problem of optimal experiment design (OED) for quantum state tomography was first broached by Kosut et al. [R. Kosut, I. Walmsley, and H. Rabitz, e-print arXiv:quant-ph/0411093 (2004)]. Here we provide efficient numerical algorithms for finding the optimal design, and analytic results for the case of `minimal tomography'. We also introduce the average OED, which is independent of the state to be reconstructed, and the optimal design for tomography (ODT), which minimizes tomographic bias. Monte Carlo simulations confirm the utility of our results for qubits. Finally, we adapt our approach to deal with constrained techniques such as maximum-likelihood estimation. We find that these are less amenable to optimization than cruder reconstruction methods, such as linear inversion.
A prime goal of quantum tomography is to provide quantitatively rigorous characterization of quantum systems, be they states, processes or measurements, particularly for the purposes of trouble-shooting and benchmarking experiments in quantum information science. A range of techniques exist to enable the calculation of errors, such as Monte-Carlo simulations, but their quantitative value is arguably fundamentally flawed without an equally rigorous way of authenticating the quality of a reconstruction to ensure it provides a reasonable representation of the data, given the known noise sources. A key motivation for developing such a tool is to enable experimentalists to rigorously diagnose the presence of technical noise in their tomographic data. In this work, I explore the performance of the chi-squared goodness-of-fit test statistic as a measure of reconstruction quality. I show that its behaviour deviates noticeably from expectations for states lying near the boundaries of physical state space, severely undermining its usefulness as a quantitative tool precisely in the region which is of most interest in quantum information processing tasks. I suggest a simple, heuristic approach to compensate for these effects and present numerical simulations showing that this approach provides substantially improved performance.