Quadratization in discrete optimization and quantum mechanics

14 Jan 20191 code implementation

This book is therefore useful for writing compilers to transform general optimization problems, into a form that quantum annealing or universal adiabatic quantum computing hardware requires; or for transforming quantum chemistry problems written in the Jordan-Wigner or Bravyi-Kitaev form, into a form where all multi-qubit interactions become 2-qubit pairwise interactions, without changing the desired ground state.

Quadratic Unconstrained Binary Optimization Problem Preprocessing: Theory and Empirical Analysis

27 May 20171 code implementation

The Quadratic Unconstrained Binary Optimization problem (QUBO) has become a unifying model for representing a wide range of combinatorial optimization problems, and for linking a variety of disciplines that face these problems.

COMBINATORIAL OPTIMIZATION

Generating Weighted MAX-2-SAT Instances of Tunable Difficulty with Frustrated Loops

14 May 20191 code implementation

For the structured-loop algorithm, we show that it offers an improvement in difficulty of the generated instances over the random-loop algorithm, with the improvement factor scaling super-exponentially with respect to the frustration index for instances at high loop density.

Quantum Annealing for Clustering

9 Aug 2014no code implementations

This paper studies quantum annealing (QA) for clustering, which can be seen as an extension of simulated annealing (SA).

Quantum Annealing for Variational Bayes Inference

9 Aug 2014no code implementations

This paper presents studies on a deterministic annealing algorithm based on quantum annealing for variational Bayes (QAVB) inference, which can be seen as an extension of the simulated annealing for variational Bayes (SAVB) inference.

Deterministic Quantum Annealing Expectation-Maximization Algorithm

19 Apr 2017no code implementations

Maximum likelihood estimation (MLE) is one of the most important methods in machine learning, and the expectation-maximization (EM) algorithm is often used to obtain maximum likelihood estimates.

Quantum-assisted associative adversarial network: Applying quantum annealing in deep learning

23 Apr 2019no code implementations

We present an algorithm for learning a latent variable generative model via generative adversarial learning where the canonical uniform noise input is replaced by samples from a graphical model.

Bayesian Network Structure Learning Using Quantum Annealing

15 Jul 2014no code implementations

The logical structure resulting from the mapping has the appealing property that it is instance-independent for a given number of Bayesian network variables, as well as being independent of the number of data cases.

Relaxation of the EM Algorithm via Quantum Annealing

5 Jun 2016no code implementations

The EM algorithm is a novel numerical method to obtain maximum likelihood estimates and is often used for practical calculations.

Comparison of D-Wave Quantum Annealing and Classical Simulated Annealing for Local Minima Determination

8 Nov 2019no code implementations

Apparently, the size of the BoA is not or at least is less important for QA search compared to the classical search, allowing QA to easily find many potentially important (e. g., wide and deep) LVs missed by even prohibitively lengthy classical searches.