Quantum Inspired Adaptive Boosting

• URL: http://arxiv.org/abs/2102.00949v1
• Date: Mon, 1 Feb 2021 16:33:14 GMT
• Title: Quantum Inspired Adaptive Boosting
• Authors: B\'alint Dar\'oczy, Katalin Friedl, L\'aszl\'o Kab\'odi, Attila Pereszl\'enyi, D\'aniel Szab\'o
• Abstract summary: We show that the quantum ensemble method does not have advantage over classical algorithms. We propose methods inspired by combining the quantum ensemble method with adaptive boosting. The algorithms were tested and found to be comparable to the AdaBoost algorithm on publicly available data sets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Building on the quantum ensemble based classifier algorithm of Schuld and Petruccione [arXiv:1704.02146v1], we devise equivalent classical algorithms which show that this quantum ensemble method does not have advantage over classical algorithms. Essentially, we simplify their algorithm until it is intuitive to come up with an equivalent classical version. One of the classical algorithms is extremely simple and runs in constant time for each input to be classified. We further develop the idea and, as the main contribution of the paper, we propose methods inspired by combining the quantum ensemble method with adaptive boosting. The algorithms were tested and found to be comparable to the AdaBoost algorithm on publicly available data sets.

Related papers

• Hybrid Quantum-Classical Algorithms [0.0]
  This thesis explores hybrid algorithms that combine classical and quantum computing to enhance the performance of classical algorithms. Two approaches are studied: a hybrid search and sample optimization algorithm and a classical algorithm that assesses the cost and performance of quantum algorithms in chemistry.
  arXiv  Detail & Related papers  (2024-06-18T07:54:05Z)
• Generalized quantum Arimoto-Blahut algorithm and its application to quantum information bottleneck [55.22418739014892]
  We generalize the quantum Arimoto-Blahut algorithm by Ramakrishnan et al. We apply our algorithm to the quantum information bottleneck with three quantum systems. Our numerical analysis shows that our algorithm is better than their algorithm.
  arXiv  Detail & Related papers  (2023-11-19T00:06:11Z)
• Iterative Quantum Algorithms for Maximum Independent Set: A Tale of Low-Depth Quantum Algorithms [0.0]
  We study a new class of hybrid approaches to quantum optimization, termed Iterative Maximum Quantum Algorithms. We show that for QAOA with depth $p=1$, this algorithm performs exactly the same operations and selections as the classical greedy algorithm for MIS.
  arXiv  Detail & Related papers  (2023-09-22T18:00:03Z)
• 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)
• Efficient Quantum Agnostic Improper Learning of Decision Trees [0.18416014644193066]
  We give a poly agnostic(n,t,frac1varepsilon)$ quantum algorithm for learning size $t$ decision trees with uniform marginal over instances. Our algorithm is the first algorithm (classical or quantum) for learning decision trees in time without membership queries.
  arXiv  Detail & Related papers  (2022-10-01T07:11:19Z)
• 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)
• A Fully Single Loop Algorithm for Bilevel Optimization without Hessian Inverse [121.54116938140754]
  We propose a new Hessian inverse free Fully Single Loop Algorithm for bilevel optimization problems. We show that our algorithm converges with the rate of $O(epsilon-2)$.
  arXiv  Detail & Related papers  (2021-12-09T02:27:52Z)
• Provably Faster Algorithms for Bilevel Optimization [54.83583213812667]
  Bilevel optimization has been widely applied in many important machine learning applications. We propose two new algorithms for bilevel optimization. We show that both algorithms achieve the complexity of $mathcalO(epsilon-1.5)$, which outperforms all existing algorithms by the order of magnitude.
  arXiv  Detail & Related papers  (2021-06-08T21:05:30Z)
• 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.