This paper investigates the information-theoretic limits of quantum state tomography, focusing on the achievable fidelity of reconstructing an unknown quantum state from a set of measurements. We derive a rigorous boundary for state reconstruction, considering the inherent constraints imposed by quantum noise and measurement imperfections. Utilizing Shannon's information theory, we analyze the fundamental trade-off between measurement resolution and the accumulation of noise. The core claim is that there exists an ultimate fidelity limit determined by these factors, preventing perfect state reconstruction. We quantify this limit through the concept of mutual information, demonstrating how the noise level dictates the achievable fidelity. This work provides a theoretical framework for understanding the fundamental constraints on quantum state reconstruction, moving beyond purely empirical estimations. The key result is expressed mathematically as: Fmax = I(S; O) where Fmax represents the maximum achievable fidelity, I(S; O) is the mutual information between the state being measured (S) and the observed measurement outcomes (O), and the inequality highlights the fundamental role of noise in limiting fidelity.
This paper investigates the information-theoretic limits of quantum state tomography, focusing on the achievable fidelity of reconstructing an unknown quantum state from a set of measurements. We derive a rigorous boundary for state reconstruction, considering the inherent constraints imposed by quantum noise and measurement imperfections. Utilizing Shannon's information theory, we analyze the fundamental trade-off between measurement resolution and the accumulation of noise. The core claim is that there exists an ultimate fidelity limit determined by these factors, preventing perfect state reconstruction. We quantify this limit through the concept of mutual information, demonstrating how the noise level dictates the achievable fidelity. This work provides a theoretical framework for understanding the fundamental constraints on quantum state reconstruction, moving beyond purely empirical estimations. The key result is expressed mathematically as: Fmax = I(S; O) where Fmax represents the maximum achievable fidelity, I(S; O) is the mutual information between the state being measured (S) and the observed measurement outcomes (O), and the inequality highlights the fundamental role of noise in limiting fidelity.
Quantum state tomography (QST) is a crucial technique for characterizing the state of a quantum system. However, the inherent limitations of measurement processes, including detector efficiency and photon counting statistics, introduce noise that affects the accuracy of the reconstructed quantum state. This work presents a rigorous information-theoretic framework for analyzing and quantifying the accuracy achievable in QST. We leverage concepts from information theory, specifically mutual information and channel capacity, to precisely determine the trade-offs between measurement resolution and the complexity of state reconstruction. The central argument is that the achievable accuracy is fundamentally limited by the information that can be reliably extracted from the measurement data, rather than relying solely on empirical estimates of error. This approach provides a theoretical benchmark for evaluating QST protocols and identifying optimal measurement strategies. The key contributions lie in establishing a quantifiable relationship between measurement parameters, noise levels, and the fidelity of the reconstructed quantum state. This offers a more robust and predictive method for assessing the quality of QST than traditional approaches.
Quantum state tomography (QST) is a crucial technique for characterizing the state of a quantum system, often employed in quantum metrology, quantum communication, and fundamental quantum information science. However, QST inherently suffers from significant measurement redundancy and uncertainty, demanding a large number of measurements to achieve a desired level of accuracy. This work proposes a novel framework for QST leveraging concepts from algorithmic information theory, specifically Kolmogorov complexity, to rigorously quantify this uncertainty and redundancy. We introduce a tailored complexity measure for quantum states that allows for the design of measurement strategies optimized to minimize the number of required measurements while maintaining a specified accuracy. Our approach shifts the focus from purely statistical estimation to a fundamental information-theoretic analysis, offering a deeper understanding of the reconstruction problem and potentially leading to more efficient and robust QST protocols. The core claim is that applying Kolmogorov complexity provides a quantifiable metric for the inherent uncertainty within a quantum state, directly informing the development of improved estimation techniques. The mechanism involves defining and utilizing this complexity measure to guide the selection of measurement angles, ultimately reducing redundancy and improving the precision of state reconstruction.
Assumption-free reconstruction of quantum states from measurements is essential for benchmarking and certifying quantum devices, but it remains difficult due to the extensive measurement statistics and experimental resources it demands. An approach to alleviating these demands is provided by adaptive measurement strategies, which can yield up to a quadratic improvement in reconstruction accuracy for pure states by dynamically optimizing measurement settings during data acquisition. A key open question is whether these asymptotic advantages remain in realistic experiments, where readout is inevitably noisy. In this work, we analyze the impact of readout noise on adaptive quantum state tomography with readout-error mitigation, focusing on the challenging regime of reconstructing pure states using mixed-state estimators. Using analytical arguments based on Fisher information optimization and extensive numerical simulations using Bayesian inference, we show that any nonzero readout noise eliminates the asymptotic quadratic scaling advantage of adaptive strategies. We numerically investigate the behavior for finite measurement statistics for single- and two-qubit systems with exact readout-error mitigation and find a gradual transition from ideal to sub-optimal scaling. We furthermore investigate realistic scenarios where detector tomography is performed with a limited number of state copies for calibration, showing that insufficient detector characterization leads to estimator bias and limited reconstruction accuracy. Although our result imposes an upper bound on the reconstruction accuracy that can be achieved with adaptive strategies, we nevertheless observe numerically a constant-factor gain in reconstruction accuracy, which becomes larger as the readout noise decreases. This indicates potential practical benefits in using adaptive measurement strategies in well-calibrated experiments.
We consider realistic measurement systems, where measurements are accompanied by decoherence processes. The aim of this work is the construction of methods and algorithms for precise quantum measurements with fidelity close to the fundamental limit. In the present work the notions of ideal and non-ideal quantum measurements are strictly formalized. It is shown that non-ideal quantum measurements could be represented as a mixture of ideal measurements. Based on root approach the quantum state reconstruction method is developed. Informational accuracy theory of non-ideal quantum measurements is proposed. The monitoring of the amount of information about the quantum state parameters is examined, including the analysis of the information degradation under the noise influence. The study of achievable fidelity in non-ideal quantum measurements is performed. The results of simulation of fidelity characteristics of a wide class of quantum protocols based on polyhedrons geometry with high level of symmetry are presented. The impact of different decoherence mechanisms, including qubit amplitude and phase relaxation, bit-flip and phase-flip, is considered.
Quantum states and measurements are two central physical objects in the theory of quantum mechanics. The preparation, control and measurement of quantum states lies at the heart of modern quantum information processing protocols ranging from secure communication to quantum computation and precision sensing. And yet, the reliable operation of these advanced protocols ultimately rests on a far more essential prerequisite - our ability to calibrate, certify and benchmark the quantum states we prepare and the quantum measurements we perform. Quantum information theory provides a natural framework for these tasks, meaningfully connecting the resources we expend for characterisation with the accuracy and precision we can expect in return. While decades of advances in quantum information have refined our understanding of quantum characterisation and sharpened our practical ability to calibrate with enhanced precision, important questions about the fundamental limits of quantum characterisation remain unanswered. Although incorporating entanglement at the measurement stage is known to enhance the estimation of unknown quantum states, the extent of this improvement remains poorly understood. This leaves open a fundamental question on the utility of complex entangling measurement strategies, impeding progress towards bridging the significant gap between practically achievable precision and the ultimate theoretical quantum limit. Complementary to quantum state estimation, which is relatively well-established in theory and practice, the characterisation of quantum measurements lacks a rigorous information-theoretic foundation, leading to a dearth of principled benchmarks and provably optimal strategies despite growing experimental interest. This thesis develops and applies information-theoretic tools to answer questions in quantum characterisation, with an eye to enabling practical improvements beyond current techniques in the field. First, we introduce a computable measure of entanglement enhancement for quantum state estimation and study its variation across general multi-parameter estimation problems for higher-dimensional quantum states. We show that the entanglement enhancement is maximised for full qudit state tomography but grows only linearly with system dimension even when allowing an asymptotically large number of entangling operations. Our proposed tight upper bound to entanglement enhancement therefore constitutes a strong limitation on the utility of such entangling measurements. Next, we formulate the dual problem of quantum detector estimation, developing a comprehensive framework for maximally precise calibration centred around a newly introduced quantity - the detector quantum Fisher information. This information-theoretic measure stipulates the ultimate precision attainable for measurement calibration and identifies the optimal input states that achieve this precision. Further, our framework enables the first provably optimal detector estimation experiment and reveals the scope for entanglement-based enhancements, thereby placing measurement characterisation on equal footing with state characterisation. Considered together, these results suggest two contrasting conclusions about quantum characterisation. On the one hand, they show that for quantum state estimation, increasingly powerful entangling measurements offer only diminishing returns at the ultimate precision limit. On the other hand, they highlight practical quantum advantages available for detector estimation by providing a framework that can improve calibration performance and guide future experimental design. More broadly, these results complete and advance the triad of state, process, and detector characterisation, positioning quantum information measures as not merely abstract bounds, but practical guides for deciding when complex quantum resources are genuinely worth deploying and where near-term quantum advantages are most likely to be realised.
This paper investigates the optimization of quantum state measurement using the principles of information entropy. The core idea is to treat quantum state measurement as an information entropy problem, aiming to maximize the information gained from the measurement process. Traditional quantum measurement techniques often rely on probabilistic outcomes, lacking control and inherent optimization. This work proposes a novel approach that leverages information entropy to design and optimize measurement schemes. The theoretical framework centers around quantifying uncertainty associated with quantum states and formulating measurement strategies to minimize this uncertainty, thereby maximizing the information obtained. Specifically, we explore the relationship between measurement outcomes, entropy, and the achievable precision. The proposed methodology offers a pathway to more efficient and accurate quantum state determination, with potential applications in quantum sensing, quantum communication, and fundamental quantum information theory. The key contributions of this research lie in the systematic application of information entropy to guide measurement selection and the development of a quantifiable metric for evaluating the effectiveness of measurement strategies. The final result is a framework that allows for controlled optimization of quantum measurements, moving beyond purely probabilistic approaches.