Estimation of Quantum Fisher Information via Stein's Identity in Variational Quantum Algorithms

• URL: http://arxiv.org/abs/2502.17231v1
• Date: Mon, 24 Feb 2025 15:10:36 GMT
• Title: Estimation of Quantum Fisher Information via Stein's Identity in Variational Quantum Algorithms
• Authors: Mourad Halla,
• Abstract summary: We introduce a novel estimation framework based on Stein's identity that reduces the computational complexity to a constant.<n> Numerical simulations on the Ising and Schwinger models demonstrate the efficiency and scalability of our approach.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Quantum Fisher Information matrix (QFIM) plays a crucial role in quantum optimization algorithms, such as Variational Quantum Imaginary Time Evolution and Quantum Natural Gradient Descent. However, computing the full QFIM incurs a quadratic computational cost of O(d^2) with respect to the number of parameters d, limiting its scalability for high-dimensional quantum systems. To address this bottleneck, we introduce a novel estimation framework based on Stein's identity that reduces the computational complexity to a constant. Numerical simulations on the Ising and Schwinger models demonstrate the efficiency and scalability of our approach, enabling effective optimization in Variational Quantum Algorithms.

Related papers

• Quantum Simulation for Dynamical Transition Rates in Open Quantum Systems [0.0]
  We introduce a novel and efficient quantum simulation method to compute dynamical transition rates in Markovian open quantum systems.<n>Our new approach holds the potential to surpass the bottlenecks of current quantum chemical research.
  arXiv  Detail & Related papers  (2024-12-23T02:53:05Z)
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
  Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties? We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• Quantum Natural Stochastic Pairwise Coordinate Descent [6.187270874122921]
  Quantum machine learning through variational quantum algorithms (VQAs) has gained substantial attention in recent years. This paper introduces the quantum natural pairwise coordinate descent (2QNSCD) optimization method. We develop a highly sparse unbiased estimator of the novel metric tensor using a quantum circuit with gate complexity $Theta(1)$ times that of the parameterized quantum circuit and single-shot quantum measurements.
  arXiv  Detail & Related papers  (2024-07-18T18:57:29Z)
• Low-Rank Variational Quantum Algorithm for the Dynamics of Open Quantum Systems [0.5120567378386615]
  A variational quantum algorithm is developed to simulate the real-time evolution of the density matrix governed by the Lindblad master equation.<n>The algorithm encodes each pure state of the statistical mixture as a parametrized quantum circuit.<n>Two variational Ans"atze are proposed, and their effectiveness is assessed in the simulation of the dynamics of a 2D dissipative transverse field Ising model.
  arXiv  Detail & Related papers  (2024-03-09T13:23:14Z)
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• Efficient criteria of quantumness for a large system of qubits [58.720142291102135]
  We discuss the dimensionless combinations of basic parameters of large, partially quantum coherent systems. Based on analytical and numerical calculations, we suggest one such number for a system of qubits undergoing adiabatic evolution.
  arXiv  Detail & Related papers  (2021-08-30T23:50:05Z)
• Quantum algorithms for quantum dynamics: A performance study on the spin-boson model [68.8204255655161]
  Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator. variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware. We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
  arXiv  Detail & Related papers  (2021-08-09T18:00:05Z)
• Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
  We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions. Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
  arXiv  Detail & Related papers  (2021-06-24T20:16:02Z)
• Fixed Depth Hamiltonian Simulation via Cartan Decomposition [59.20417091220753]
  We present a constructive algorithm for generating quantum circuits with time-independent depth. We highlight our algorithm for special classes of models, including Anderson localization in one dimensional transverse field XY model. In addition to providing exact circuits for a broad set of spin and fermionic models, our algorithm provides broad analytic and numerical insight into optimal Hamiltonian simulations.
  arXiv  Detail & Related papers  (2021-04-01T19:06:00Z)
• Resource-efficient encoding algorithm for variational bosonic quantum simulations [0.0]
  In the Noisy Intermediate Scale Quantum (NISQ) era of quantum computing, quantum resources are limited. We present a resource-efficient quantum algorithm for bosonic ground and excited state computations.
  arXiv  Detail & Related papers  (2021-02-23T19:00:05Z)
• Quantum Computation of Finite-Temperature Static and Dynamical Properties of Spin Systems Using Quantum Imaginary Time Evolution [0.0]
  We develop scalable quantum algorithms to study finite-temperature physics of quantum many-body systems. Our work demonstrates that the ansatz-independent QITE algorithm is capable of computing diverse finite-temperature observables on near-term quantum devices.
  arXiv  Detail & Related papers  (2020-09-08T06:49:08Z)

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.