Recall Performance for Content-Addressable Memory Using Adiabatic Quantum Optimization

Schrock, Jonathan; McCaskey, Alexander; Hamilton, Kathleen E.; Humble, Travis S.; Imam, Neena

doi:10.3390/e19090500

Cited by 8 publications

(7 citation statements)

References 42 publications

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“…Coupling strengths are rescaled by factor of 3 4W max in accordance with previous studies and preliminary simulations. W max is the maximum value of the weights matrix and the additional factor is chosen to ensure that intrachain ferromagnetic coupling strengths for logical qubits remain dominant [15]. Note that this scale factor is chosen for QAMM and QCAM models, and generally conveys favorable recall performance for all models and noise parameters considered.…”

Section: Hardware Platform and Methodsmentioning

confidence: 99%

“…The projection rule will be used to train the QAMM, while a bipartite variation of the rule will be used to encode patterns into the QCAM model [15]. In principle, the projection rule can induce a fully connected graph between qubits.…”

Section: Projection Rule Learningmentioning

confidence: 99%

“…QA is an computational method that exploits quantum mechanical phenomena to find the global minimum of an objective function [9][10][11][12]. Previous studies have shown that both AMM [13,14] and CAM [15] can be performed via QA. Potential computational advantages offered by QA-based methods have been suggested in the form of improvements to the theoretical learning capacity of the model.…”

Section: Introductionmentioning

confidence: 99%

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Particle Track Classification Using Quantum Associative Memory

Quiroz,

Ice,

Delgado

et al. 2020

Preprint

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Pattern recognition algorithms are commonly employed to simplify the challenging and necessary step of track reconstruction in sub-atomic physics experiments. Aiding in the discrimination of relevant interactions, pattern recognition seeks to accelerate track reconstruction by isolating signals of interest. In high collision rate experiments, such algorithms can be particularly crucial for determining whether to retain or discard information from a given interaction even before the data is transferred to tape. As data rates, detector resolution, noise, and inefficiencies increase, pattern recognition becomes more computationally challenging, motivating the development of higher efficiency algorithms and techniques. Quantum associative memory is an approach that seeks to exploits quantum mechanical phenomena to gain advantage in learning capacity, or the number of patterns that can be stored and accurately recalled. Here, we study quantum associative memory based on quantum annealing and apply it to the particle track classification. We focus on discrimination models based on Ising formulations of quantum associative memory model (QAMM) recall and quantum content-addressable memory (QCAM) recall. We characterize classification performance of these approaches as a function detector resolution, pattern library size, and detector inefficiencies, using the D-Wave 2000Q processor as a testbed. Discrimination criteria is set using both solution-state energy and classification labels embedded in solution states. We find that energy-based QAMM classification performs well in regimes of small pattern density and low detector inefficiency. In contrast, state-based QCAM achieves reasonably high accuracy recall for large pattern density and the greatest recall accuracy robustness to a variety of detector noise sources.

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Section: Hardware Platform and Methodsmentioning

confidence: 99%

Section: Projection Rule Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Particle Track Classification Using Quantum Associative Memory

Quiroz,

Ice,

Delgado

et al. 2020

Preprint

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show abstract

“…An odd cycle transversal is a set of vertices whose removal leaves a bipartite graph. Thus, graphs with "small" odd cycle transversals, or OCT sets, are referred to as "near-bipartite," and this notion naturally arises in many applications [7,17,21]. We use O to generally denote an OCT set, and given an OCT set for a graph G = (V, E), we let B = V \ O.…”

Section: Odd Cycle Transversalsmentioning

confidence: 99%

Approximating Vertex Cover using Structural Rounding

Lavallee¹,

Russell²,

Sullivan

et al. 2020

2020 Proceedings of the Twenty-Second Workshop on Algorithm Engineering and Experiments (ALENEX)

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In this work, we provide the first practical evaluation of the structural rounding framework for approximation algorithms. Structural rounding works by first editing to a wellstructured class, efficiently solving the edited instance, and "lifting" the partial solution to recover an approximation on the input. We focus on the well-studied Vertex Cover problem, and edit to the class of bipartite graphs (where Vertex Cover has an exact polynomial time algorithm). In addition to the naïve lifting strategy for Vertex Cover described by Demaine et al. in the paper describing structural rounding, we introduce a suite of new lifting strategies and measure their effectiveness on a large corpus of synthetic graphs. We find that in this setting, structural rounding significantly outperforms standard 2-approximations. Further, simpler lifting strategies are extremely competitive with the more sophisticated approaches. The implementations are available as an open-source Python package, and all experiments are replicable. *

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“…Kloster et al [17] extended techniques for bipartite graphs to the general setting using odd cycle transversals, a form of "near-bipartiteness" which arises naturally in many applications [10,24,27]. This work resulted in OCT-MIB, an algorithm for enumerating MIBs in a general graph, parameterized by the size of a given OCT set.…”

Section: Related Workmentioning

confidence: 99%

Faster Biclique Mining in Near-Bipartite Graphs

Sullivan

Poel

Woodlief

2019

Lecture Notes in Computer Science

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Identifying dense bipartite subgraphs is a common graph data mining task. Many applications focus on the enumeration of all maximal bicliques (MBs), though sometimes the stricter variant of maximal induced bicliques (MIBs) is of interest. Recent work of Kloster et al. introduced a MIB-enumeration approach designed for "near-bipartite" graphs, where the runtime is parameterized by the size k of an odd cycle transversal (OCT), a vertex set whose deletion results in a bipartite graph. Their algorithm was shown to outperform the previously best known algorithm even when k was logarithmic in |V |. In this paper, we introduce two new algorithms optimized for near-bipartite graphs -one which enumerates MIBs in time O(MI |V ||E|k), and another based on the approach of Alexe et al. which enumerates MBs in time O(MB|V ||E|k), where MI and MB denote the number of MIBs and MBs in the graph, respectively. We implement all of our algorithms in open-source C++ code and experimentally verify that the OCT-based approaches are faster in practice than the previously existing algorithms on graphs with a wide variety of sizes, densities, and OCT decompositions.

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Recall Performance for Content-Addressable Memory Using Adiabatic Quantum Optimization

Cited by 8 publications

References 42 publications

Particle Track Classification Using Quantum Associative Memory

Particle Track Classification Using Quantum Associative Memory

Approximating Vertex Cover using Structural Rounding

Faster Biclique Mining in Near-Bipartite Graphs

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