Efficient Quantum Tomography of Two-Mode Wigner Functions

• URL: http://arxiv.org/abs/1912.12222v1
• Date: Fri, 27 Dec 2019 16:29:40 GMT
• Title: Efficient Quantum Tomography of Two-Mode Wigner Functions
• Abstract summary: We introduce an efficient method to reconstruct the Wigner function of many-mode continuous variable systems. A key ingredient of the proposed approach is the representation of the state in a truncated Fock basis.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce an efficient method to reconstruct the Wigner function of many-mode continuous variable systems. It is based on convex optimization with semidefinite programs, and also includes a version of the maximum entropy principle, in order to yield unbiased states. A key ingredient of the proposed approach is the representation of the state in a truncated Fock basis. As a bonus, the discrete finite representation allows to easily quantify the entanglement.

Related papers

• Preconditioned Multivariate Quantum Solution Extraction [0.0]
  Quantum computers can potentially provide high-degree speed-ups for solving PDEs.<n>Many algorithms simply end with preparing the quantum state encoding the solution in its amplitudes.<n>We present a technique for extracting a smooth positive function encoded in the amplitudes of a quantum state.
  arXiv  Detail & Related papers  (2026-01-08T16:21:49Z)
• Quantum simulations of Green's functions for small superfluid systems [5.901027348002322]
  An end-to-end strategy for hybrid quantum-classical computations of Green's functions in many-body systems is presented.<n>The scheme makes explicit use of the spectral representation of the Green's function.<n>Different ansatzes for the ground-state wave function, originating from either classical or quantum approaches, are tested and compared to exact calculations.
  arXiv  Detail & Related papers  (2025-09-02T12:48:21Z)
• Outcome-Based Online Reinforcement Learning: Algorithms and Fundamental Limits [58.63897489864948]
  Reinforcement learning with outcome-based feedback faces a fundamental challenge.<n>How do we assign credit to the right actions?<n>This paper provides the first comprehensive analysis of this problem in online RL with general function approximation.
  arXiv  Detail & Related papers  (2025-05-26T17:44:08Z)
• On the Schrödingerization method for linear non-unitary dynamics with optimal dependence on matrix queries [42.104910612491885]
  The Schr"odingerization method converts linear partial and ordinary differential equations with non-unitary dynamics into systems of Schr"odinger-type equations with unitary evolution.<n>The original proposal used a particular initial function in the auxiliary space that did not achieve optimal scaling in precision.<n>Here we show that, by choosing smoother initial functions in auxiliary space, Schr"odingerization textitcan in fact achieve near optimal and even optimal scaling in matrix queries.
  arXiv  Detail & Related papers  (2025-05-01T07:46:50Z)
• Uncertainty Quantification with Bayesian Higher Order ReLU KANs [0.0]
  We introduce the first method of uncertainty quantification in the domain of Kolmogorov-Arnold Networks, specifically focusing on (Higher Order) ReLUKANs. We validate our method through a series of closure tests, including simple one-dimensional functions. We demonstrate the method's ability to correctly identify functional dependencies introduced through the inclusion of a term.
  arXiv  Detail & Related papers  (2024-10-02T15:57:18Z)
• Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity [59.75300530380427]
  We consider the problem of optimizing second-order smooth and strongly convex functions where the algorithm is only accessible to noisy evaluations of the objective function it queries. We provide the first tight characterization for the rate of the minimax simple regret by developing matching upper and lower bounds.
  arXiv  Detail & Related papers  (2024-06-28T02:56:22Z)
• Accelerated Cyclic Coordinate Dual Averaging with Extrapolation for Composite Convex Optimization [20.11028799145883]
  We propose an Accelerated Cyclic Coordinate Dual Averaging with Extrapolation (A-CODER) method for composite convex optimization. We show that A-CODER attains the optimal convergence rate with improved dependence on the number of blocks compared to prior work. For the setting where the smooth component of the objective function is expressible in a finite sum form, we introduce a variance-reduced variant of A-CODER, VR-A-CODER, with state-of-the-art complexity guarantees.
  arXiv  Detail & Related papers  (2023-03-28T19:46:30Z)
• D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory [79.50644650795012]
  We propose a deep learning approach to solve Kohn-Sham Density Functional Theory (KS-DFT) We prove that such an approach has the same expressivity as the SCF method, yet reduces the computational complexity. In addition, we show that our approach enables us to explore more complex neural-based wave functions.
  arXiv  Detail & Related papers  (2023-03-01T10:38:10Z)
• Fast Computation of Optimal Transport via Entropy-Regularized Extragradient Methods [75.34939761152587]
  Efficient computation of the optimal transport distance between two distributions serves as an algorithm that empowers various applications. This paper develops a scalable first-order optimization-based method that computes optimal transport to within $varepsilon$ additive accuracy.
  arXiv  Detail & Related papers  (2023-01-30T15:46:39Z)
• Quantum state preparation without coherent arithmetic [5.478764356647437]
  We introduce a versatile method for preparing a quantum state whose amplitudes are given by some known function.magnitude existing approaches. We use a template quantum eigenvalue transformation circuit to convert a low cost block encoding of the sine function into the desired function.
  arXiv  Detail & Related papers  (2022-10-26T17:48:31Z)
• Efficient and Flexible Sublabel-Accurate Energy Minimization [62.50191141358778]
  We address the problem of minimizing a class of energy functions consisting of data and smoothness terms. Existing continuous optimization methods can find sublabel-accurate solutions, but they are not efficient for large label spaces. We propose an efficient sublabel-accurate method that utilizes the best properties of both continuous and discrete models.
  arXiv  Detail & Related papers  (2022-06-20T06:58:55Z)
• Sparse Quadratic Optimisation over the Stiefel Manifold with Application to Permutation Synchronisation [71.27989298860481]
  We address the non- optimisation problem of finding a matrix on the Stiefel manifold that maximises a quadratic objective function. We propose a simple yet effective sparsity-promoting algorithm for finding the dominant eigenspace matrix.
  arXiv  Detail & Related papers  (2021-09-30T19:17:35Z)
• Algorithm for initializing a generalized fermionic Gaussian state on a quantum computer [0.0]
  We present explicit expressions for the central piece of a variational method developed by Shi et al. We derive iterative analytical expressions for the evaluation of expectation values of products of fermionic creation and subroutine operators. We present a simple gradient-descent-based algorithm that can be used as an optimization in combination with imaginary time evolution.
  arXiv  Detail & Related papers  (2021-05-27T10:31:45Z)
• SGB: Stochastic Gradient Bound Method for Optimizing Partition Functions [15.33098084159285]
  This paper addresses the problem of optimizing partition functions in a learning setting. We propose a variant of the bound majorization algorithm that relies on upper-bounding the partition function with a quadratic surrogate.
  arXiv  Detail & Related papers  (2020-11-03T04:42:51Z)
• Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies [88.0813215220342]
  Some modalities can more easily contribute to the classification results than others. We develop a method based on the log-Sobolev inequality, which bounds the functional entropy with the functional-Fisher-information. On the two challenging multi-modal datasets VQA-CPv2 and SocialIQ, we obtain state-of-the-art results while more uniformly exploiting the modalities.
  arXiv  Detail & Related papers  (2020-10-21T07:40:33Z)
• Kernel Taylor-Based Value Function Approximation for Continuous-State Markov Decision Processes [5.894659354028797]
  We propose a principled kernel-based policy iteration algorithm to solve the continuous-state Markov Decision Processes (MDPs) We have validated the proposed method through extensive simulations in both simplified and realistic planning scenarios.
  arXiv  Detail & Related papers  (2020-06-03T01:48:43Z)

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.