On Hitting Times for General Quantum Markov Processes
- URL: http://arxiv.org/abs/2210.10188v3
- Date: Thu, 6 Jul 2023 15:11:24 GMT
- Title: On Hitting Times for General Quantum Markov Processes
- Authors: Lorenzo Laneve, Francesco Tacchino, Ivano Tavernelli
- Abstract summary: We use the density-matrix formalism to define a quantum Markov chain model which directly generalizes classical walks.
We show that a common tools such as hitting times can be computed with a similar formula as the one found in the classical theory.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Random walks (or Markov chains) are models extensively used in theoretical
computer science. Several tools, including analysis of quantities such as
hitting and mixing times, are helpful for devising randomized algorithms. A
notable example is Sch\"oning's algorithm for the satisfiability (SAT) problem.
In this work, we use the density-matrix formalism to define a quantum Markov
chain model which directly generalizes classical walks, and we show that a
common tools such as hitting times can be computed with a similar formula as
the one found in the classical theory, which we then apply to known quantum
settings such as Grover's algorithm.
Related papers
- Sum-of-Squares inspired Quantum Metaheuristic for Polynomial Optimization with the Hadamard Test and Approximate Amplitude Constraints [76.53316706600717]
Recently proposed quantum algorithm arXiv:2206.14999 is based on semidefinite programming (SDP)
We generalize the SDP-inspired quantum algorithm to sum-of-squares.
Our results show that our algorithm is suitable for large problems and approximate the best known classicals.
arXiv Detail & Related papers (2024-08-14T19:04:13Z) - Hybrid Quantum-Classical Machine Learning with String Diagrams [49.1574468325115]
This paper develops a formal framework for describing hybrid algorithms in terms of string diagrams.
A notable feature of our string diagrams is the use of functor boxes, which correspond to a quantum-classical interfaces.
arXiv Detail & Related papers (2024-07-04T06:37:16Z) - Robust Dequantization of the Quantum Singular value Transformation and
Quantum Machine Learning Algorithms [0.0]
We show how many techniques from randomized linear algebra can be adapted to work under this weaker assumption.
We also apply these results to obtain a robust dequantization of many quantum machine learning algorithms.
arXiv Detail & Related papers (2023-04-11T02:09:13Z) - Discrete-time Semiclassical Szegedy Quantum Walks [0.0]
We introduce the semiclassical walks in discrete time, which are algorithms that combines classical and quantum dynamics.
We have demonstrated experimentally that the semiclassical walks can be applied on real quantum computers using the platform IBM Quantum.
arXiv Detail & Related papers (2023-03-31T16:57:13Z) - 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) - Simulating Markovian open quantum systems using higher-order series
expansion [1.713291434132985]
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.
arXiv Detail & Related papers (2022-12-05T06:02:50Z) - Quantum algorithm for Markov Random Fields structure learning by
information theoretic properties [5.263910852465186]
We propose a quantum algorithm for structure learning of an $r$-wise Markov Random Field on quantum computers.
Our work demonstrates the potential merits of quantum computation over classical computation in solving some problems in machine learning.
arXiv Detail & Related papers (2022-08-24T09:00:56Z) - 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) - Hybrid Quantum-Classical Search Algorithms [0.0]
We show that classical computation, unless it by itself can solve the Search problem, cannot assist quantum computation.
We generalize this result to algorithms with subconstant success probabilities.
arXiv Detail & Related papers (2022-02-23T11:43:17Z) - Generalization Metrics for Practical Quantum Advantage in Generative
Models [68.8204255655161]
Generative modeling is a widely accepted natural use case for quantum computers.
We construct a simple and unambiguous approach to probe practical quantum advantage for generative modeling by measuring the algorithm's generalization performance.
Our simulation results show that our quantum-inspired models have up to a $68 times$ enhancement in generating unseen unique and valid samples.
arXiv Detail & Related papers (2022-01-21T16:35:35Z) - Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra [53.46106569419296]
We create classical (non-quantum) dynamic data structures supporting queries for recommender systems and least-squares regression.
We argue that the previous quantum-inspired algorithms for these problems are doing leverage or ridge-leverage score sampling in disguise.
arXiv Detail & Related papers (2020-11-09T01:13:07Z)
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.