Efficiently estimating the quantum Fisher information (QFI) is pivotal in quantum information science but remains an outstanding challenge for large systems due to its high nonlinearity. In this Letter, we tackle this long-standing challenge by integrating the Krylov subspace method-a celebrated tool from applied mathematics-into the framework of shadow tomography. The integrated technique, dubbed Krylov shadow tomography, enables us to formulate a strict hierarchy of nonpolynomial lower bounds on the QFI, among which the highest one matches the QFI exactly. We show that all the bounds can be expressed as expected values of the inverses of Hankel matrices, which are accessible via shadow tomography. Our Krylov shadow tomography therefore opens up a resource-efficient and experimentally feasible avenue to estimate not only nonpolynomial lower bounds but also the QFI itself.
Efficiently estimating the quantum Fisher information (QFI) is pivotal in quantum information science but remains an outstanding challenge for large systems due to its high nonlinearity. In this Letter, we tackle this long-standing challenge by integrating the Krylov subspace method-a celebrated tool from applied mathematics-into the framework of shadow tomography. The integrated technique, dubbed Krylov shadow tomography, enables us to formulate a strict hierarchy of nonpolynomial lower bounds on the QFI, among which the highest one matches the QFI exactly. We show that all the bounds can be expressed as expected values of the inverses of Hankel matrices, which are accessible via shadow tomography. Our Krylov shadow tomography therefore opens up a resource-efficient and experimentally feasible avenue to estimate not only nonpolynomial lower bounds but also the QFI itself.
Estimating the quantum Fisher information (QFI) is a crucial yet challenging task with widespread applications across quantum science and technologies. The recently proposed Krylov shadow tomography (KST) opens a new avenue for this task by introducing a series of Krylov bounds on the QFI. In this work, we address the practical applicability of the KST, unveiling that the Krylov bounds of low orders already enable efficient and accurate estimation of the QFI. We show that the Krylov bounds converge to the QFI exponentially fast with increasing order and can surpass the state-of-the-art polynomial lower bounds known to date. Moreover, we show that certain low-order Krylov bound can already match the QFI exactly for low-rank states prevalent in practical settings. Such exact match is beyond the reach of polynomial lower bounds proposed previously. These theoretical findings, solidified by extensive numerical simulations, demonstrate practical advantages over existing polynomial approaches, holding promise for fully unlocking the effectiveness of QFI-based applications.
Estimating the quantum Fisher information (QFI) is a central task in quantum science and technology, underpinning the benchmarking of measurement devices and the certification of metrological advantages. Owing to the highly nonlinear dependence of the QFI on the quantum state, achieving efficient estimates that reliably converge to the true value, which is a property known as gap closing, has remained a challenge. Here, we address it experimentally by implementing the recently proposed Krylov shadow tomography (KST) protocol on a versatile photonic platform capable of generating Greenberger-Horne-Zeilinger states of up to six qubits with controllable noise. By comparing KST with leading methods under matched resources, we show that it exhibits the predicted gap-closing behavior and yields a substantially lower mean absolute error. Moreover, we demonstrate that the resulting high-accuracy QFI estimates correctly predict the ultimate precision achievable in a phase estimation experiment, as dictated by the quantum Cramér-Rao bound. These results establish KST as a scalable and practical experimental tool for the efficient characterization and utilization of quantum resources in metrological applications.
Shadow Tomography is a fundamental problem in quantum information theory. Given multiple copies of an unknown d-dimensional quantum state ρ and a known collection of observables E1,…,EM, the goal is to estimate all expectation values {Tr(ρEi)}i=1M to additive accuracy ε with probability at least 1−δ.
An elusive open question from the seminal shadow tomography work of Aaronson is whether this task admits a dimension-independent sample complexity with only polylogarithmic dependence on M, as suggested by the best-known lower bounds. In this work, we propose two different quantum protocols for shadow tomography with the best sample complexity O(ε2log(M)log(M/δ)), which is polylogarithmic in the number of observables and independent of the dimension of the unknown state, thereby answering Aaronson's original question while also providing an exponential improvement in the prior best dimension independent sample complexity of shadow tomography from Sinha (STOC 2025) and, more recently, Chen, O'Donnell, Pelecanos, and Wright.
Our approach first reduces the general shadow tomography problem to a finite-ensemble estimation problem via a minimax argument. We then develop an observable-independent protocol that repeatedly applies the pretty-good measurement while updating the prior distribution over the finite ensemble according to the measurement outcomes. A tail analysis of the resulting estimation error yields simultaneous accuracy guarantees for all observables and a cubic-logarithmic upper bound. We also introduce a refined recovery-label measurement for the same finite ensemble, which yields the bound in our main theorem.
Estimating the quantum Fisher information (QFI) is a central task in quantum science and technology, underpinning the benchmarking of measurement devices and the certification of metrological advantages. Owing to the highly nonlinear dependence of the QFI on the quantum state, achieving efficient estimates that reliably converge to the true value, which is a property known as gap closing, has remained a challenge. Here, we address it experimentally by implementing the recently proposed Krylov shadow tomography (KST) protocol on a versatile photonic platform capable of generating Greenberger-Horne-Zeilinger states of up to six qubits with controllable noise. By comparing KST with leading methods under matched resources, we show that it exhibits the predicted gap-closing behavior and yields a substantially lower mean squared error. Moreover, we demonstrate that the resulting high-accuracy QFI estimates correctly predict the ultimate precision achievable in a phase estimation experiment, as dictated by the quantum Cram\'{e}r-Rao bound. These results establish KST as a scalable and practical experimental tool for the efficient characterization and utilization of quantum resources in metrological applications.
Scalable quantum Fisher information (QFI) estimation becomes actionable when the numerical estimate is paired with a trustworthy stopping decision. Krylov-shadow QFI estimation has two resource directions: the Krylov order sets the population resolution, whereas the sample count controls statistical uncertainty at that resolution. We show that treating these directions as one can produce false stops, where a width-based rule reports a narrow interval around a biased low-order estimate. We turn adaptive stopping into a two-component reliability problem, separating Krylov truncation from finite-sample uncertainty, and introduce AKS-QFI, a component-aware stopping interface for Krylov-shadow estimators. On a noisy mixed-state benchmark at n=4 qubits, width-only stopping has false-stop rates from 0.16 to 0.68. Under the same resource limit, AKS-QFI returns no false success declarations; after recalibrating Krylov resolution and sample counts, it returns accurate success declarations at true 5% relative tolerance. These results make adaptive stopping a reliability layer for shadow-based QFI estimation.
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