Quantum adiabatic machine learning

Pudenz, Kristen L.; Lidar, Daniel A.

doi:10.1007/s11128-012-0506-4

Cited by 111 publications

(80 citation statements)

References 48 publications

(93 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The possibility of using quantum mechanics for machine learning has been considered theoretically [3][4][5][6][7][8][9][10][11][12][13][14]. With the development of quantum annealing processors [15], it has become possible to test machine-learning ideas with an actual quantum hardware [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Quantum Boltzmann Machine

et al. 2018

View full text Add to dashboard Cite

Inspired by the success of Boltzmann machines based on classical Boltzmann distribution, we propose a new machine-learning approach based on quantum Boltzmann distribution of a quantum Hamiltonian. Because of the noncommutative nature of quantum mechanics, the training process of the quantum Boltzmann machine (QBM) can become nontrivial. We circumvent the problem by introducing bounds on the quantum probabilities. This allows us to train the QBM efficiently by sampling. We show examples of QBM training with and without the bound, using exact diagonalization, and compare the results with classical Boltzmann training. We also discuss the possibility of using quantum annealing processors for QBM training and application.

show abstract

Section: Introductionmentioning

confidence: 99%

Quantum Boltzmann Machine

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The use of quantum computing technologies for sampling and machine learning applications has attracted increasing interest from the research community in recent years [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Although the main focus of the quantum annealing computational paradigm [17][18][19] has been on solving discrete optimization problems in a wide variety of application domains [20][21][22][23][24][25][26][27], it has been also introduced as a potential candidate to speed up computations in sampling applications.…”

Section: Introductionmentioning

confidence: 99%

Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning

et al. 2016

View full text Add to dashboard Cite

An increase in the efficiency of sampling from Boltzmann distributions would have a significant impact on deep learning and other machine-learning applications. Recently, quantum annealers have been proposed as a potential candidate to speed up this task, but several limitations still bar these state-of-the-art technologies from being used effectively. One of the main limitations is that, while the device may indeed sample from a Boltzmann-like distribution, quantum dynamical arguments suggest it will do so with an instance-dependent effective temperature, different from its physical temperature. Unless this unknown temperature can be unveiled, it might not be possible to effectively use a quantum annealer for Boltzmann sampling. In this work, we propose a strategy to overcome this challenge with a simple effective-temperature estimation algorithm. We provide a systematic study assessing the impact of the effective temperatures in the learning of a special class of a restricted Boltzmann machine embedded on quantum hardware, which can serve as a building block for deep-learning architectures. We also provide a comparison to k-step contrastive divergence (CD-k) with k up to 100. Although assuming a suitable fixed effective temperature also allows us to outperform one step contrastive divergence (CD-1), only when using an instance-dependent effective temperature do we find a performance close to that of CD-100 for the case studied here.

show abstract

“…This finite-temperature, open system model is expected to more accurately describe the dynamics underlying the QPU [8]. Nevertheless, several experimental tests of the D-Wave QPU have been carried out including applications of machine learning, binary classification, protein folding, graph analysis, and network analysis [9][10][11][12][13][14][15][16][17][18]. Demonstrations of enhanced performance using the D-Wave QPU have been found only for a few selected and highly contrived problem instances [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Recall Performance for Content-Addressable Memory Using Adiabatic Quantum Optimization

Schrock

McCaskey

Hamilton

et al. 2017

Entropy

View full text Add to dashboard Cite

A content-addressable memory (CAM) stores key-value associations such that the key is recalled by providing its associated value. While CAM recall is traditionally performed using recurrent neural network models, we show how to solve this problem using adiabatic quantum optimization. Our approach maps the recurrent neural network to a commercially available quantum processing unit by taking advantage of the common underlying Ising spin model. We then assess the accuracy of the quantum processor to store key-value associations by quantifying recall performance against an ensemble of problem sets. We observe that different learning rules from the neural network community influence recall accuracy but performance appears to be limited by potential noise in the processor. The strong connection established between quantum processors and neural network problems supports the growing intersection of these two ideas.

show abstract

Quantum adiabatic machine learning

Cited by 111 publications

References 48 publications

Quantum Boltzmann Machine

Quantum Boltzmann Machine

Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning

Recall Performance for Content-Addressable Memory Using Adiabatic Quantum Optimization

Contact Info

Product

Resources

About