arXiv subjects

Liubov A. Markovich

Publications and source records attributed to Liubov A. Markovich.

6 recordsLinked to original sources

arXiv2026

From Discrete to Continuous-Variable Systems via Jordan-Schwinger Tomographic Transformation

Hybrid quantum systems that combine discrete-variable (DV) and continuous-variable (CV) architectures represent a promising direction in quantum information science. However, transferring concepts, information, and states between these platforms poses practical and theoretical challenges, as their formalisms assign different properties and physical interpretations to many shared notions. We construct a bridge between DV spin systems and restricted CV bosonic systems within corresponding sectors using the tomographic probability representation of quantum states and the Jordan--Schwinger and Holstein--Primakoff maps. Although these maps are well known at the operator level, their action on tomograms and other probability representations has not been addressed in the literature. To the best of our knowledge, we provide the first explicit demonstration of their action on tomographic probability distributions and Wigner functions, establishing direct correspondences between the measurement statistics of DV systems and the corresponding restricted CV bosonic representations. Our tomographic mapping transfers measurement data between quantum architectures through an intrinsic data compression kernel that selects the Hilbert space sector relevant to the target representation. It yields the tomogram of a target sector directly from experimental data in another representation, without reconstructing the density matrix. This provides a unified framework for transferring and comparing quantum information across different hardware platforms within the corresponding restricted sectors, with applications to hybrid protocols, device benchmarking, and the validation of error correction schemes based on mappings between finite-dimensional systems and sector restricted bosonic representations.

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arXiv2026

Spin-Boson Mappings in the Formalism of $f$-Deformations

We develop a unified algebraic approach to spin-boson transformations based on the formalism of $f$-deformed oscillators. In the single-mode case, we show that the Holstein-Primakoff and Dyson-Maleev transformations, together with the interpolating $α$-family, arise as different factorizations of the same algebraically determined object. The standard spin-boson mappings can thus be interpreted as realizations of a common structure, which makes it possible to clearly separate the exact algebraic content on the physical subspace from effects associated with non-Hermiticity, the choice of metric, and extensions beyond the physical subspace. In the two-mode case, the same approach yields both deformed versions of the Jordan-Schwinger transformation and new exact two-mode realizations. Our results provide a unified description of known spin-boson transformations and naturally lead to new bosonic representations.

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arXiv2026

Optimal Nonparametric Estimation of Phase-Space Representations for Non-Gaussian Continuous Variable Quantum States

We further develop Kernel Quantum State Estimation (KQSE), a fully data-driven nonparametric method for continuous variable quantum state reconstruction and characterization, introduced in our recent work and rooted in nonparametric kernel density estimation (KDE). Unlike approaches relying on finite-dimensional basis truncations, parametric ansätze, or prior models, KQSE combines a new kernel estimator of the characteristic function of the tomographic quadrature distribution with suitable kernel integral transformations. The characteristic function is estimated directly from experimental homodyne or heterodyne data and subsequently transformed to estimate quantum state representations and characteristics, including the Wigner function and the density matrix kernel. Observing that several other physically relevant representations and characteristics admit transformations of a closely related form, we derive convergence rates for the corresponding broad class of KQSE-based estimators. The resulting framework covers all phase-space representations, the photon-number tomogram, trace products of quantum states, and purity. We derive mean squared error convergence rates for these kernel transformed estimators and show that the corresponding KQSE applications inherit the near optimal rate O(T^{-1}), where $T$ is the total number of measurements. The proposed theory applies equally to Gaussian and non-Gaussian continuous variable quantum states without imposing a Fock space cutoff. Numerical experiments on simulated Gaussian and non-Gaussian states and real homodyne data demonstrate the advantages of KQSE over state-of-the-art methods, establishing it as a statistically consistent and computationally modest framework for continuous variable quantum state estimation and characterization, particularly in the non-Gaussian regime.

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arXiv2026

Nonparametric Learning Non-Gaussian Quantum States of Continuous Variable Systems

Continuous-variable quantum systems are foundational to quantum computation, communication, and sensing. While traditional representations using wave functions or density matrices are often impractical, the tomographic picture of quantum mechanics provides an accessible alternative by associating quantum states with classical probability distribution functions called tomograms. Despite its advantages, including compatibility with classical statistical methods, tomographic method remain underutilized due to a lack of robust estimation techniques. This work addresses this gap by introducing a non-parametric \emph{kernel quantum state estimation} (KQSE) framework for reconstructing quantum states and their trace characteristics from noisy data, without prior knowledge of the state. In contrast to existing methods, KQSE yields estimates of the density matrix in various bases, as well as trace quantities such as purity, higher moments, overlap, and trace distance, with a near-optimal convergence rate of $\tilde{O}\bigl(T^{-1}\bigr)$, where $T$ is the total number of measurements. KQSE is robust for multimodal, non-Gaussian states, making it particularly well suited for characterizing states essential for quantum science.

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arXiv2025

Quantum memory assisted observable estimation

The estimation of many-qubit observables is an essential task of quantum information processing. The generally applicable approach is to decompose the observables into weighted sums of multi-qubit Pauli strings, i.e., tensor products of single-qubit Pauli matrices, which can readily be measured with single qubit rotations. The accumulation of shot noise in this approach, however, severely limits the achievable variance for a finite number of measurements. We introduce a novel method, dubbed Coherent Pauli Summation (CPS) that circumvents this limitation by exploiting access to a single-qubit quantum memory in which measurement information can be stored and accumulated. Our algorithm offers a reduction in the required number of measurements for a given variance that scales linearly with the number of Pauli strings of the decomposed observable. Our work demonstrates how a single long-coherence qubit memory can assist the operation of noisy many-qubit quantum devices in a cardinal task.

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arXiv2022

Efficient and robust estimation of many-qubit Hamiltonians

Characterizing the interactions and dynamics of quantum mechanical systems is an essential task in the development of quantum technologies. We propose an efficient protocol based on the estimation of the time derivatives of few qubit observables using polynomial interpolation for characterizing the underlying Hamiltonian dynamics and Markovian noise of a multi-qubit device. For finite range dynamics, our protocol exponentially relaxes the necessary time resolution of the measurements and quadratically reduces the overall sample complexity compared to previous approaches. Furthermore, we show that our protocol can characterize the dynamics of systems with algebraically decaying interactions. The implementation of the protocol requires only the preparation of product states and single-qubit measurements. Furthermore, we develop a shadow tomography method for quantum channels that is of independent interest. This protocol can be used to parallelize to learn the Hamiltonian, rendering it applicable for the characterization of both current and future quantum devices.

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