Simulating Markovian open quantum systems using higher-order series
expansion
- URL: http://arxiv.org/abs/2212.02051v2
- Date: Sun, 9 Jul 2023 13:12:36 GMT
- Title: Simulating Markovian open quantum systems using higher-order series
expansion
- Authors: Xiantao Li, Chunhao Wang
- Abstract summary: We present an efficient quantum algorithm for simulating the dynamics of Markovian open quantum systems.
Our algorithm is conceptually cleaner, and it only uses simple quantum primitives without compressed encoding.
- Score: 1.713291434132985
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present an efficient quantum algorithm for simulating the dynamics of
Markovian open quantum systems. The performance of our algorithm is similar to
the previous state-of-the-art quantum algorithm, i.e., it scales linearly in
evolution time and poly-logarithmically in inverse precision. However, our
algorithm is conceptually cleaner, and it only uses simple quantum primitives
without compressed encoding. Our approach is based on a novel mathematical
treatment of the evolution map, which involves a higher-order series expansion
based on Duhamel's principle and approximating multiple integrals using scaled
Gaussian quadrature. Our method easily generalizes to simulating quantum
dynamics with time-dependent Lindbladians. Furthermore, our method of
approximating multiple integrals using scaled Gaussian quadrature could
potentially be used to produce a more efficient approximation of time-ordered
integrals, and therefore can simplify existing quantum algorithms for
simulating time-dependent Hamiltonians based on a truncated Dyson series.
Related papers
- 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) - Efficient and practical Hamiltonian simulation from time-dependent product formulas [1.2534672170380357]
We propose an approach for implementing time-evolution of a quantum system using product formulas.
Our algorithms generate a decomposition of the evolution operator into a product of simple unitaries that are directly implementable on a quantum computer.
Although the theoretical scaling is suboptimal compared with state-of-the-art algorithms, the performance of the algorithms we propose is highly competitive in practice.
arXiv Detail & Related papers (2024-03-13T17:29:05Z) - Variational Quantum Algorithms for Simulation of Lindblad Dynamics [0.0]
We introduce a variational hybrid classical-quantum algorithm to simulate the Lindblad master equation and its adjoint for time-evolving Markovian open quantum systems and quantum observables.
We design and optimize low-depth variational quantum circuits that efficiently capture the unitary and non-unitary dynamics of the solutions.
arXiv Detail & Related papers (2023-05-04T13:25:44Z) - A hybrid quantum-classical algorithm for multichannel quantum scattering
of atoms and molecules [62.997667081978825]
We propose a hybrid quantum-classical algorithm for solving the Schr"odinger equation for atomic and molecular collisions.
The algorithm is based on the $S$-matrix version of the Kohn variational principle, which computes the fundamental scattering $S$-matrix.
We show how the algorithm could be scaled up to simulate collisions of large polyatomic molecules.
arXiv Detail & Related papers (2023-04-12T18:10:47Z) - Quantum Clustering with k-Means: a Hybrid Approach [117.4705494502186]
We design, implement, and evaluate three hybrid quantum k-Means algorithms.
We exploit quantum phenomena to speed up the computation of distances.
We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version.
arXiv Detail & Related papers (2022-12-13T16:04:16Z) - Time Evolution of Uniform Sequential Circuits [0.16385815610837165]
We present a hybrid quantum-classical scaling algorithm for time evolving a one-dimensional uniform system in the thermodynamic limit.
We show numerically that this anatzs requires a number of parameters in the simulation time for a given accuracy.
All steps of the hybrid optimization are designed with near-term digital quantum computers in mind.
arXiv Detail & Related papers (2022-10-07T18:00:01Z) - Entanglement and coherence in Bernstein-Vazirani algorithm [58.720142291102135]
Bernstein-Vazirani algorithm allows one to determine a bit string encoded into an oracle.
We analyze in detail the quantum resources in the Bernstein-Vazirani algorithm.
We show that in the absence of entanglement, the performance of the algorithm is directly related to the amount of quantum coherence in the initial state.
arXiv Detail & Related papers (2022-05-26T20:32:36Z) - 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) - 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) - Low-depth Hamiltonian Simulation by Adaptive Product Formula [3.050399782773013]
Various Hamiltonian simulation algorithms have been proposed to efficiently study the dynamics of quantum systems on a quantum computer.
Here, we propose an adaptive approach to construct a low-depth time evolution circuit.
Our work sheds light on practical Hamiltonian simulation with noisy-intermediate-scale-quantum devices.
arXiv Detail & Related papers (2020-11-10T18:00:42Z) - Inverse iteration quantum eigensolvers assisted with a continuous
variable [0.0]
We propose inverse iteration quantum eigensolvers, which exploit the power of quantum computing for the classical inverse power iteration method.
A key ingredient is constructing an inverse Hamiltonian as a linear combination of coherent Hamiltonian evolution.
We demonstrate the quantum algorithm with numerical simulations under finite squeezing for various physical systems.
arXiv Detail & Related papers (2020-10-07T07:31:11Z)
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.