The rapid advancement of quantum information science has spurred research into the ultimate performance limits of quantum systems in communication, sensing, and computation. This thesis adopts a decision-theoretic and information-theoretic viewpoint to systematically study fundamental problems in quantum statistical inference, with a focus on continuous-variable and bosonic platforms. The central theme is to uncover the interplay among physical structure (e.g., Gaussianity and phase-space geometry), operational restrictions (e.g., postselection and covariant measurements), and inference objectives (e.g., risk, error probability, and detection delay), and their joint impact on fundamental limits and attainability. The thesis first advances the understanding of phase-space entropy and the Wigner entropy conjecture. For Wigner-nonnegative quantum states, we develop verifiable sufficient criteria based on moment functionals and purity constraints, thereby extending the range of states for which the conjecture holds. We also establish explicit purity thresholds that guarantee the conjecture in the low-purity regime. These results reveal how Wigner nonnegativity constrains non-Gaussian structure and suggest pathways for incorporating further quantum-realizability conditions. Second, the thesis investigates the role of postselection in quantum state discrimination. We prove that, on average, environment-assisted partial postselection cannot outperform the Helstrom bound, yet conditional improvements are possible in selected branches. This clarifies the decision-theoretic trade-off between conditional performance and success probability, providing a rigorous basis for evaluating postselected protocols. In parameter estimation, we study phase-shift sensing within the covariant measurement framework. By linking the Bayesian risk to the Fourier coefficients of the input state, we derive an explicit characterization of optimal probe states via the Perron–Frobenius theorem and show that Heisenberg scaling is attainable in the high-energy limit. This offers constructive guidance for designing optimal sensing states under symmetry constraints. Finally, the thesis extends classical quickest change detection to sequential quantum inference. We establish the asymptotic relationship between quantum relative entropy and detection delay, and develop protocol architectures tailored to bosonic sensing and joint communication–detection tasks. In particular, for phase-change detection, we provide an analytic framework for computing quantum relative entropy with Gaussian probes, supplying a theoretical toolkit for practical system design. By integrating phase-space methods, information measures, and sequential decision theory, this thesis provides a cohesive analytical framework and delivers several substantive advances in quantum statistical inference for continuous-variable systems, with implications for quantum sensing, quantum communication, and quantum signal processing.
The rapid advancement of quantum information science has spurred research into the ultimate performance limits of quantum systems in communication, sensing, and computation. This thesis adopts a decision-theoretic and information-theoretic viewpoint to systematically study fundamental problems in quantum statistical inference, with a focus on continuous-variable and bosonic platforms. The central theme is to uncover the interplay among physical structure (e.g., Gaussianity and phase-space geometry), operational restrictions (e.g., postselection and covariant measurements), and inference objectives (e.g., risk, error probability, and detection delay), and their joint impact on fundamental limits and attainability. The thesis first advances the understanding of phase-space entropy and the Wigner entropy conjecture. For Wigner-nonnegative quantum states, we develop verifiable sufficient criteria based on moment functionals and purity constraints, thereby extending the range of states for which the conjecture holds. We also establish explicit purity thresholds that guarantee the conjecture in the low-purity regime. These results reveal how Wigner nonnegativity constrains non-Gaussian structure and suggest pathways for incorporating further quantum-realizability conditions. Second, the thesis investigates the role of postselection in quantum state discrimination. We prove that, on average, environment-assisted partial postselection cannot outperform the Helstrom bound, yet conditional improvements are possible in selected branches. This clarifies the decision-theoretic trade-off between conditional performance and success probability, providing a rigorous basis for evaluating postselected protocols. In parameter estimation, we study phase-shift sensing within the covariant measurement framework. By linking the Bayesian risk to the Fourier coefficients of the input state, we derive an explicit characterization of optimal probe states via the Perron–Frobenius theorem and show that Heisenberg scaling is attainable in the high-energy limit. This offers constructive guidance for designing optimal sensing states under symmetry constraints. Finally, the thesis extends classical quickest change detection to sequential quantum inference. We establish the asymptotic relationship between quantum relative entropy and detection delay, and develop protocol architectures tailored to bosonic sensing and joint communication–detection tasks. In particular, for phase-change detection, we provide an analytic framework for computing quantum relative entropy with Gaussian probes, supplying a theoretical toolkit for practical system design. By integrating phase-space methods, information measures, and sequential decision theory, this thesis provides a cohesive analytical framework and delivers several substantive advances in quantum statistical inference for continuous-variable systems, with implications for quantum sensing, quantum communication, and quantum signal processing.
This thesis studies quantum-enhanced phase change detection using continuous-variable Gaussian states. The main goal is to understand how different probe states and receiver designs affect the detection of a phase change in a noisy optical channel. The phase change problem is written as a quantum state discrimination problem. The pre-change and post-change states are Gaussian states described by displacement vectors and covariance matrices. A thermal-loss channel is used to model optical loss and background noise, and a phase shift is applied to the signal mode. The main quantum benchmark is the quantum relative entropy (QRE) between the post-change and pre-change states. A pure two-mode Gaussian probe is studied in detail. The probe energy is split between squeezing and coherent displacement. This split is controlled by a power allocation ratio ?. By optimizing over ?, the probe can balance quantum correlations and coherent amplitude under a fixed photon-number constraint. Amplified spontaneous emission (ASE), coherent state, and two-mode squeezed vacuum (TMSV) probes are also considered as comparison cases. Practical receiver designs are then studied using classical relative entropy (CRE). The receivers include ideal photon-number-resolving (PNR) detection, finite-resolution PNR detection, a binary single-photon detector (SPD), an ASE receiver, and a homodyne jointdetection receiver. The finite-resolution study distinguishes binary SPD, with outcomes 0 and ? 1, from 1-PNR, with outcomes 0, 1, and ? 2. The receiver-level classical relative entropy is compared with the quantum relative entropy bound to show how much information each receiver keeps after measurement. Finally, the thesis connects these relative entropy results to the cumulative sum (CUSUM) and quantum CUSUM (QUSUM) frameworks for quickest change detection. In this framework, a larger relative entropy means a shorter detection delay for the same false alarm requirement. The results show that both probe design and receiver design are important for quantum-enhanced phase change detection.
The aim of this thesis is to develop a framework for assessing performance in quantum information processing with continuous variables. In particular, we focus on quantifying the fundamental limitations on communication and computation over bosonic Gaussian systems. Due to their infinite-dimensional structure, we make a realistic assumption of energy constraints on the input states of continuous-variable (CV) quantum operations. Our first contribution is to show that energy-constrained distinguishability measures can be used to establish tight upper bounds on the communication capacities of phase-insensitive, bosonic Gaussian channels -- thermal, amplifier, and additive-noise channels. We then prove that an optimal Gaussian input state for the energy-constrained, generalized channel divergence of two particular Gaussian channels is the two-mode squeezed vacuum state that saturates the energy constraint. Next, we develop theoretical and numerical tools based on energy-constrained distinguishability measures to quantify the accuracy in implementing Gaussian unitary operations. Finally, we propose an optimal test for the performance of CV quantum teleportation in terms of the energy-constrained channel fidelity between ideal CV teleportation and its experimental implementation. Here we prove that the optimal state for testing CV teleportation is an entangled superposition of twin-Fock states. These results are relevant for experiments that make use of Gaussian unitaries and CV teleportation.
Quantum control plays a crucial role in quantum inference tasks such as parameter estimation and state discrimination. Despite this, the fields of quantum control and quantum inference have remained largely disjoint. We bridge these fields, revealing that (i) quantum control determines the fundamental performance limits of quantum estimation and state discrimination, and (ii) control of statistical information measures enables performance approaching these limits. This thesis begins by establishing an axiomatic formalization of quantum control theory and developing a mathematical framework for analyzing, approximating, and controlling the evolution of quantum systems. A self-contained treatment of optimal quantum control is presented. This treatment exploits the unique structure of quantum dynamics rather than merely borrowing notions from classical control. Quantum control systems are shown to be universally contractive, which plays an important role in the treatment of optimal quantum control and in the derivation of various approximation results. Next, the thesis proceeds by addressing statistical inference problems, with emphasis on parameter estimation and state discrimination. Controlled information measures are introduced, providing more sensible alternatives to existing information measures for quantum inference, e.g., quantum Fisher information. Controlled information measures are shown to exhibit useful properties, e.g., data-processing inequalities. Methods for deriving achievability and converse bounds on controlled information measures are developed, and adaptive control techniques for information maximization are proposed. This thesis leverages a cross-pollination of control theory, statistical inference, and quantum information towards the development of an application-focused theory of quantum inference.
Gaussian states, operations, and measurements are central building blocks for continuous-variable quantum information processing which paves the way for abundant applications, especially including network-based quantum computation and communication. To make the most use of the Gaussian processes, it is required to understand and utilize suitable mathematical tools such as the symplectic space, symplectic algebra, and Wigner representation. Applying these mathematical tools to practical quantum scenarios, we developed various schemes for quantum transduction, interference-based bosonic mode permutation and bosonic sensing. We demonstrated that generic coupler characterized by Gaussian unitary process can be transformed into a high-fidelity transducer, assuming the access to infinite squeezing and adaptive feedforward with homodyne measurements. To address the practical limitation of finite squeezing, we explored the interference-based protocols. These protocols let us freely permute bosonic modes only assuming the access to single-mode Gaussian operations and multiple uses of a given multi-mode Gaussian process. Thus, such a scheme not only enables universal decoupling for bosonic systems, which is useful for suppressing undesired coupling between the system and the environment, but also faithful bidirectional single-mode quantum transduction. Moreover, noticing that the Gaussian processes are appropriate theoretical models for optical sensors, we studied the quantum noise theory for optical parameter sensing and its potential in providing great measurement precision enhancement. We also extended the Gaussian theories to discrete variable systems, with several examples such as quantum (gate) teleportation. All the analyses originated from the fundamental quantum commutation relations, and therefore are widely applicable.
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.
Quantum information processing (QIP) studies the representation, manipulation, and transmission of information using quantum systems. By leveraging counterintuitive quantum phenomena such as superposition and entanglement, QIP enables new paradigms for computation, communication, and learning that can surpass classical limits in certain regimes. Foundational tasks in QIP include quantum data compression, quantum communication, and quantum learning and optimization, which together form the basis for scalable quantum technologies. This thesis develops new frameworks and algorithms at the intersection of quantum information theory, quantum learning, and quantum-inspired modeling. The thesis is organized into three parts. The first part studies quantum compression problems; the second addresses quantum communication; and the third develops quantum learning and optimization frameworks with applications to complex systems. In the first part, a new formulation for lossy quantum-classical source coding, termed rate-channel theory, is introduced, replacing the conventional distortion-based formulation with a posterior-channel-based framework under a block-error criterion. The framework is further generalized to incorporate quantum side information at the decoder. A connection between rate-channel theory and classical rate-distortion theory is also established, showing that optimal rate-distortion performance can be recovered under suitable conditions. These results provide a unified and flexible framework for data compression, which is essential for scalable quantum computing infrastructure, enabling efficient storage of quantum data, such as outputs from quantum sensors (e.g., MRI) and information encoded in photonic systems for satellite and optical communication. In the second part, the problem of computing functions of distributed sources over classical-quantum multiple access channels is studied. A new coding framework is developed that combines algebraic structured codes with unstructured random coding techniques, enabling efficient communication of functions without reconstructing individual sources. The resulting achievable conditions, expressed in terms of single-letter quantum information quantities, strictly enlarge the class of sources for which reliable computation is possible. These results contribute to the theoretical foundations of quantum network communication and are directly relevant to deep-space satellite-based quantum communication, distributed quantum computing, and the design of scalable quantum network infrastructures. In the final part, new approaches to quantum learning and optimization are developed. A geometry-aware quantum optimization framework is proposed based on a novel quantum information metric and an unbiased estimator constructed from single-shot measurements. This leads to an optimization method with constant sample complexity per iteration and provable exponential convergence under mild conditions, while avoiding saddle points and local minima. Such advances are particularly relevant for challenging optimization tasks in quantum sensing and quantum computational modeling of many-body systems. Building on this optimization framework, a Hamiltonian learning approach is introduced to infer interaction structures from time-resolved measurement data, with finite-sample guarantees and polynomial scaling with system size. This framework is further extended to develop a quantum-inspired generative model for gene regulatory network inference, where interactions are encoded as a parameterized Hamiltonian evolving over pseudotime. The proposed methods demonstrate strong performance on synthetic benchmarks and reveal biologically meaningful structures in real-world data, with applications to multi-omics integration, cancer genomics, and systems biology. Overall, this thesis advances the theoretical and algorithmic foundations of quantum information processing, with contributions spanning compression, communication, and learning. These developments support the design of resource-efficient and scalable quantum technologies, enabling applications in quantum sensing, quantum networks, distributed quantum computation, and the modeling of complex physical and biological systems.