Polynomial-time certification of fidelity for many-body mixed states and mixed-state universality classes
- URL: http://arxiv.org/abs/2601.13333v1
- Date: Mon, 19 Jan 2026 19:13:28 GMT
- Title: Polynomial-time certification of fidelity for many-body mixed states and mixed-state universality classes
- Authors: Yuhan Liu, Yijian Zou,
- Abstract summary: We introduce a-time algorithm to compute certified lower and upper bounds for fidelity between matrix product density operators.<n>Our results offer an exponential improvement in precision over known moment-based bounds.
- Score: 23.9304612104967
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Computation of Uhlmann fidelity between many-body mixed states generally involves full diagonalization of exponentially large matrices. In this work, we introduce a polynomial-time algorithm to compute certified lower and upper bounds for the fidelity between matrix product density operators (MPDOs). Our method maps the fidelity estimation problem to a variational optimization of sequential quantum circuits, allowing for systematic improvement of the lower bounds by increasing the circuit depth. Complementarily, we obtain certified upper bounds on fidelity by variational lower bounds on the trace distance through the same framework. We demonstrate the power of this approach with two examples: fidelity correlators in critical mixed states, and codeword distinguishability in an approximate quantum error-correcting code. Remarkably, the variational lower bound accurately track the universal scaling behavior of the fidelity with a size-consistent relative error, allowing for the extraction of previously unknown critical exponents. Our results offer an exponential improvement in precision over known moment-based bounds and establish a scalable framework for the verification of many-body quantum systems.
Related papers
- A Posteriori Certification Framework for Generalized Quantum Arimoto-Blahut Algorithms [41.15017547767954]
We introduce an a posteriori certification viewpoint for the generalized quantum Arimoto-Blahut (QAB) algorithm.<n>We prove a global convergence theorem showing that, under convexity and a substantially weaker numerically verifiable condition, the QAB iteration converges to the global minimizer.<n>As an application, we develop a certified iterative scheme for computing the quantum relative entropy of channels.
arXiv Detail & Related papers (2026-01-14T09:10:41Z) - Multi-Fidelity Delayed Acceptance: hierarchical MCMC sampling for Bayesian inverse problems combining multiple solvers through deep neural networks [0.3499870393443268]
Inverse uncertainty quantification (UQ) tasks are computationally demanding when dealing with physics-based models.<n>Data-driven surrogate models may help reduce evaluation costs, but their utility is often limited by the expense of generating high-fidelity data.<n>We propose a Multi-Fidelity Delayed Acceptance scheme for Bayesian inverse problems.
arXiv Detail & Related papers (2025-12-18T11:32:16Z) - Quartic quantum speedups for community detection [84.14713515477784]
We develop a quantum algorithm for hypergraph community detection that achieves a quartic quantum speedup.<n>Our algorithm is based on the Kikuchi method, which we extend beyond previously considered problems such as PCA and $p$XORSAT.
arXiv Detail & Related papers (2025-10-09T17:35:17Z) - Heisenberg limited multiple eigenvalue estimation via off-the-grid compressed sensing [0.0]
Quantum phase estimation is the flagship algorithm for quantum simulation on fault-tolerant quantum computers.<n>We demonstrate that an emphoff-grid compressed sensing protocol, combined with a state-of-the-art signal classification method, enables the simultaneous estimation of multiple eigenvalues.<n>We argue that this algorithm may offer a potential quantum advantage by analyzing its resilience with respect to the quality of the initial input state.
arXiv Detail & Related papers (2025-07-16T17:27:01Z) - Fidelity decay and error accumulation in random quantum circuits [0.3562485774739681]
fidelity decays exponentially with both circuit depth and the number of qubits raised to an architecture-dependent power.<n>We establish a robust linear relationship between fidelity and the heavy output frequency used in Quantum Volume tests to benchmark quantum processors.
arXiv Detail & Related papers (2024-04-17T14:53:26Z) - Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram
Iteration [122.51142131506639]
We introduce a precise, fast, and differentiable upper bound for the spectral norm of convolutional layers using circulant matrix theory.
We show through a comprehensive set of experiments that our approach outperforms other state-of-the-art methods in terms of precision, computational cost, and scalability.
It proves highly effective for the Lipschitz regularization of convolutional neural networks, with competitive results against concurrent approaches.
arXiv Detail & Related papers (2023-05-25T15:32:21Z) - Fidelity-based distance bounds for $N$-qubit approximate quantum error
correction [0.0]
Eastin-Knill theorem states that a quantum code cannot correct errors exactly, possess continuous symmetries, and implement a universal set of gates transversely.
It is common to employ a complementary measure of fidelity as a way to quantify quantum state distinguishability and benchmark approximations in error correction.
We address two distance measures based on the sub- and superfidelities as a way to bound error approximations, which in turn require a lower computational cost.
arXiv Detail & Related papers (2022-12-08T16:10:58Z) - Improved Quantum Algorithms for Fidelity Estimation [77.34726150561087]
We develop new and efficient quantum algorithms for fidelity estimation with provable performance guarantees.
Our algorithms use advanced quantum linear algebra techniques, such as the quantum singular value transformation.
We prove that fidelity estimation to any non-trivial constant additive accuracy is hard in general.
arXiv Detail & Related papers (2022-03-30T02:02:16Z) - Scalable Variational Gaussian Processes via Harmonic Kernel
Decomposition [54.07797071198249]
We introduce a new scalable variational Gaussian process approximation which provides a high fidelity approximation while retaining general applicability.
We demonstrate that, on a range of regression and classification problems, our approach can exploit input space symmetries such as translations and reflections.
Notably, our approach achieves state-of-the-art results on CIFAR-10 among pure GP models.
arXiv Detail & Related papers (2021-06-10T18:17:57Z) - Amortized Conditional Normalized Maximum Likelihood: Reliable Out of
Distribution Uncertainty Estimation [99.92568326314667]
We propose the amortized conditional normalized maximum likelihood (ACNML) method as a scalable general-purpose approach for uncertainty estimation.
Our algorithm builds on the conditional normalized maximum likelihood (CNML) coding scheme, which has minimax optimal properties according to the minimum description length principle.
We demonstrate that ACNML compares favorably to a number of prior techniques for uncertainty estimation in terms of calibration on out-of-distribution inputs.
arXiv Detail & Related papers (2020-11-05T08:04:34Z) - Efficient and robust certification of genuine multipartite entanglement
in noisy quantum error correction circuits [58.720142291102135]
We introduce a conditional witnessing technique to certify genuine multipartite entanglement (GME)
We prove that the detection of entanglement in a linear number of bipartitions by a number of measurements scales linearly, suffices to certify GME.
We apply our method to the noisy readout of stabilizer operators of the distance-three topological color code and its flag-based fault-tolerant version.
arXiv Detail & Related papers (2020-10-06T18:00:07Z) - Total Deep Variation: A Stable Regularizer for Inverse Problems [71.90933869570914]
We introduce the data-driven general-purpose total deep variation regularizer.
In its core, a convolutional neural network extracts local features on multiple scales and in successive blocks.
We achieve state-of-the-art results for numerous imaging tasks.
arXiv Detail & Related papers (2020-06-15T21:54:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.