Quantum computing algorithms for inverse problems on graphs and an
NP-complete inverse problem
- URL: http://arxiv.org/abs/2306.05253v2
- Date: Mon, 12 Feb 2024 11:50:09 GMT
- Title: Quantum computing algorithms for inverse problems on graphs and an
NP-complete inverse problem
- Authors: Joonas Ilmavirta, Matti Lassas, Jinpeng Lu, Lauri Oksanen, Lauri
Ylinen
- Abstract summary: We consider an inverse problem for a finite graph $(X,E)$ where we are given a subset of vertices.
We show that this problem has unique solution under certain conditions and develop quantum computing methods to solve it.
- Score: 2.107421958337625
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We consider an inverse problem for a finite graph $(X,E)$ where we are given
a subset of vertices $B\subset X$ and the distances $d_{(X,E)}(b_1,b_2)$ of all
vertices $b_1,b_2\in B$. The distance of points $x_1,x_2\in X$ is defined as
the minimal number of edges needed to connect two vertices, so all edges have
length 1. The inverse problem is a discrete version of the boundary rigidity
problem in Riemannian geometry or the inverse travel time problem in
geophysics. We will show that this problem has unique solution under certain
conditions and develop quantum computing methods to solve it. We prove the
following uniqueness result: when $(X,E)$ is a tree and $B$ is the set of
leaves of the tree, the graph $(X,E)$ can be uniquely determined in the class
of all graphs having a fixed number of vertices. We present a quantum computing
algorithm which produces a graph $(X,E)$, or one of those, which has a given
number of vertices and the required distances between vertices in $B$. To this
end we develop an algorithm that takes in a qubit representation of a graph and
combine it with Grover's search algorithm. The algorithm can be implemented
using only $O(|X|^2)$ qubits, the same order as the number of elements in the
adjacency matrix of $(X,E)$. It also has a quadratic improvement in
computational cost compared to standard classical algorithms. Finally, we
consider applications in theory of computation, and show that a slight
modification of the above inverse problem is NP-complete: all NP-problems can
be reduced to a discrete inverse problem we consider.
Related papers
- A Scalable Algorithm for Individually Fair K-means Clustering [77.93955971520549]
We present a scalable algorithm for the individually fair ($p$, $k$)-clustering problem introduced by Jung et al. and Mahabadi et al.
A clustering is then called individually fair if it has centers within distance $delta(x)$ of $x$ for each $xin P$.
We show empirically that not only is our algorithm much faster than prior work, but it also produces lower-cost solutions.
arXiv Detail & Related papers (2024-02-09T19:01:48Z) - Quantum Speedup for the Maximum Cut Problem [6.588073742048931]
We propose a quantum algorithm to solve the maximum cut problem for any graph $G$ with a quadratic speedup over its classical counterparts.
With respect to oracle-related quantum algorithms for NP-complete problems, we identify our algorithm as optimal.
arXiv Detail & Related papers (2023-05-26T05:31:25Z) - The Subgraph Isomorphism Problem for Port Graphs and Quantum Circuits [0.0]
We give an algorithm to perform pattern matching in quantum circuits for many patterns simultaneously.
In the case of quantum circuits, we can express the bound obtained in terms of the maximum number of qubits.
arXiv Detail & Related papers (2023-02-13T22:09:02Z) - Quantum Algorithm for Dynamic Programming Approach for DAGs and
Applications [0.0]
We present a quantum algorithm for the dynamic programming approach for problems on directed acyclic graphs (DAGs)
We show that we can solve problems that use OR, AND, NAND, MAX, and MIN functions as the main transition steps.
arXiv Detail & Related papers (2022-12-29T19:07:39Z) - Sketching Algorithms and Lower Bounds for Ridge Regression [65.0720777731368]
We give a sketching-based iterative algorithm that computes $1+varepsilon$ approximate solutions for the ridge regression problem.
We also show that this algorithm can be used to give faster algorithms for kernel ridge regression.
arXiv Detail & Related papers (2022-04-13T22:18:47Z) - A sublinear query quantum algorithm for s-t minimum cut on dense simple
graphs [1.0435741631709405]
An $soperatorname-t$ minimum cut in a graph corresponds to a minimum weight subset of edges whose removal disconnects $s$ and $t$.
In this work we describe a quantum algorithm for the minimum $soperatorname-t$ cut problem on undirected graphs.
arXiv Detail & Related papers (2021-10-29T07:35:46Z) - Fast Graph Sampling for Short Video Summarization using Gershgorin Disc
Alignment [52.577757919003844]
We study the problem of efficiently summarizing a short video into several paragraphs, leveraging recent progress in fast graph sampling.
Experimental results show that our algorithm achieves comparable video summarization as state-of-the-art methods, at a substantially reduced complexity.
arXiv Detail & Related papers (2021-10-21T18:43:00Z) - Improved Reconstruction of Random Geometric Graphs [3.930410971186142]
We consider the classic model of random geometric graphs where $n$ points are scattered uniformly in a square of area $n$.
We use a hybrid of graph distances and short-range estimates based on the number of common neighbors to estimate Euclidean distances.
Our method estimates Euclidean distances using a hybrid of graph distances and short-range estimates based on the number of common neighbors.
arXiv Detail & Related papers (2021-07-29T20:37:28Z) - Streaming Complexity of SVMs [110.63976030971106]
We study the space complexity of solving the bias-regularized SVM problem in the streaming model.
We show that for both problems, for dimensions of $frac1lambdaepsilon$, one can obtain streaming algorithms with spacely smaller than $frac1lambdaepsilon$.
arXiv Detail & Related papers (2020-07-07T17:10:00Z) - Private Learning of Halfspaces: Simplifying the Construction and
Reducing the Sample Complexity [63.29100726064574]
We present a differentially private learner for halfspaces over a finite grid $G$ in $mathbbRd$ with sample complexity $approx d2.5cdot 2log*|G|$.
The building block for our learner is a new differentially private algorithm for approximately solving the linear feasibility problem.
arXiv Detail & Related papers (2020-04-16T16:12:10Z) - On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems [86.92205445270427]
We consider non-con minimax problems, $min_mathbfx max_mathhidoty f(mathbfdoty)$ efficiently.
arXiv Detail & Related papers (2019-06-02T03:03:45Z)
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.