Estimating properties of a quantum state is an indispensable task in various applications of quantum information processing. To predict properties in the postprocessing stage, it is inherent to first perceive the quantum state with a measurement protocol and store the information acquired. In this paper, we propose a general framework for constructing classical approximations of arbitrary quantum states with quantum reservoirs. A key advantage of our method is that only a single local measurement setting is required for estimating arbitrary properties, while most of the previous methods need an exponentially increasing number of measurement settings. To estimate M properties simultaneously, the size of the classical approximation scales as lnM . Moreover, this estimation scheme is extendable to higher-dimensional systems and hybrid systems with nonidentical local dimensions, which makes it exceptionally generic. We support our theoretical findings with extensive numerical simulations. Published by the American Physical Society 2024
Estimating properties of a quantum state is an indispensable task in various applications of quantum information processing. To predict properties in the postprocessing stage, it is inherent to first perceive the quantum state with a measurement protocol and store the information acquired. In this paper, we propose a general framework for constructing classical approximations of arbitrary quantum states with quantum reservoirs. A key advantage of our method is that only a single local measurement setting is required for estimating arbitrary properties, while most of the previous methods need an exponentially increasing number of measurement settings. To estimate M properties simultaneously, the size of the classical approximation scales as lnM . Moreover, this estimation scheme is extendable to higher-dimensional systems and hybrid systems with nonidentical local dimensions, which makes it exceptionally generic. We support our theoretical findings with extensive numerical simulations. Published by the American Physical Society 2024
We address how one can empirically infer properties of quantum states generated by dynamics involving measurements. Our focus is on many-body settings where the number of measurements is extensive, making brute-force approaches based on postselection intractable due to their exponential sample complexity. We introduce a general-purpose scheme that can be used to infer any property of the postmeasurement ensemble of states (e.g., the average entanglement entropy, or frame potential) using a scalable number of experimental repetitions. We first identify a general class of “estimable properties” that can be directly extracted from experimental data. Then, based on empirical observations of such quantities, we show how one can indirectly infer information about any particular given nonestimable quantity of interest through classical postprocessing. Our approach is based on an optimization task, where one asks what are the minimum and maximum values that the desired quantity could possibly take, while ensuring consistency with observations. The true value of this quantity must then lie within a feasible range between these extrema, resulting in two-sided bounds. Narrow feasible ranges can be obtained by using a classical simulation of the device to determine which estimable properties one should measure. Even in cases where this simulation is inaccurate, unambiguous information about the true value of a given quantity realized on the quantum device can be learned. As an immediate application, we show that our method can be used to verify the emergence of quantum state designs in experiments. We identify some fundamental obstructions that in some cases prevent sharp knowledge of a given quantity from being inferred, and discuss what can be learned in cases where classical simulation is too computationally demanding to be feasible. In particular, we prove that any observer who cannot perform a classical simulation cannot distinguish the output states from those sampled from a maximally structureless ensemble. Published by the American Physical Society 2024
In quantum information theory, the accurate estimation of observables is pivotal for quantum information processing, playing a crucial role in computational and communication protocols. This work introduces a technique for estimating such objects, leveraging an underutilized resource in the inversion map of classical shadows that greatly refines the estimation cost of target observables without incurring any additional overhead. A generalized framework for computing and optimizing additional degrees of freedom in the homogeneous space of the shadow inversion is given that may be adapted to a variety of near-term problems. In the special case of local measurement strategies, we show feasible optimization leading to an exponential separation in sample complexity versus the standard approach and, in an exceptional case, we give nontrivial examples of optimized postprocessing for local measurements, achieving the same efficiency as the global Cliffords shadows. Published by the American Physical Society 2024
Using quantum systems as sensors or probes has been shown to greatly improve the precision of parameter estimation by exploiting unique quantum features such as entanglement. A major task in quantum sensing is to design the optimal protocol, i.e., the most precise one. It has been solved for some specific instances of the problem, but in general even numerical methods are not known. Here, we focus on the single-shot Bayesian setting, where the goal is to find the optimal initial state of the probe (which can be entangled with an auxiliary system), the optimal measurement, and the optimal estimator function. We leverage the formalism of higher-order operations to develop a method based on semidefinite programming that finds a protocol that is close to the optimal one with arbitrary precision. Crucially, our method is not restricted to any specific quantum evolution, cost function, or prior distribution, and thus can be applied to any estimation problem. Moreover, it can be applied to both single-parameter or multiparameter estimation tasks. We demonstrate our method with three examples, consisting of unitary phase estimation, thermometry in a bosonic bath, and multiparameter estimation of an SU(2) transformation. Exploiting our methods, we extend several results from the literature. For example, in the thermometry case, we find the optimal protocol at any finite time and quantify the usefulness of entanglement. Published by the American Physical Society 2024
We present a technique for enhancing the estimation of quantum state properties by incorporating approximate prior knowledge about the quantum state. This consists in performing randomized measurements on a quantum processor and comparing the results with those obtained from a classical computer that stores an approximation of the quantum state. We provide unbiased estimators for expectation values of multicopy observables and present performance guarantees in terms of variance bounds that depend on the prior knowledge accuracy. We demonstrate the effectiveness of our approach through experimental and numerical examples detecting mixed-state entanglement, and estimating polynomial approximations of the von Neumann entropy and state fidelities. Published by the American Physical Society 2024
Estimating global properties of many-body quantum systems such as entropy or bipartite entanglement is a notoriously difficult task, typically requiring a number of measurements or classical postprocessing resources growing exponentially in the system size. In this work, we address the problem of estimating global entropies and mixed-state entanglement via partial-transposed (PT) moments and show that efficient estimation strategies exist under the assumption that all the spatial correlation lengths are finite. Focusing on one-dimensional systems, we identify a set of approximate factorization conditions (AFCs) on the system density matrix, which allow us to reconstruct entropies and PT moments from information on local subsystems. This identification yields a simple and efficient strategy for entropy and entanglement estimation. Our method could be implemented in different ways, depending on how information on local subsystems is extracted. Focusing on randomized measurements providing a practical and common measurement scheme, we prove that our protocol requires only polynomially many measurements and postprocessing operations, assuming that the state to be measured satisfies the AFCs. We prove that the AFCs hold for finite-depth quantum-circuit states and translation-invariant matrix-product density operators and provide numerical evidence that they are satisfied in more general, physically interesting cases, including thermal states of local Hamiltonians. We argue that our method could be practically useful to detect bipartite mixed-state entanglement for large numbers of qubits available in today’s quantum platforms. Published by the American Physical Society 2024
Quantum state estimation is a crucial component of many quantum computing applications, particularly for analyzing large datasets. Current classical algorithms struggle to efficiently estimate the state of quantum systems, limiting the practical utility of quantum computation. This paper explores the implementation of a novel quantum-enhanced algorithm to address this challenge, aiming to significantly improve the accuracy and efficiency of state estimation for large-scale data. We detail the core mechanism of utilizing quantum phenomena to reduce computational complexity and explore potential advancements in this field. The study focuses on demonstrating the algorithm's effectiveness through theoretical analysis and preliminary experimental validation, highlighting its potential to unlock new capabilities in quantum data analysis.