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.

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

14 May 20192 code implementations

Many optimization problems can be cast into the maximum satisfiability (MAX-SAT) form, and many solvers have been developed for tackling such problems.

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.


Quantum adiabatic machine learning with zooming

13 Aug 20191 code implementation

Recent work has shown that quantum annealing for machine learning (QAML) can perform comparably to state-of-the-art machine learning methods with a specific application to Higgs boson classification.

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.

Reinforcement Quantum Annealing: A Quantum-Assisted Learning Automata Approach

1 Jan 2020no code implementations

We introduce the reinforcement quantum annealing (RQA) scheme in which an intelligent agent interacts with a quantum annealer that plays the stochastic environment role of learning automata and tries to iteratively find better Ising Hamiltonians for the given problem of interest.

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.

Optimizing Geometry Compression using Quantum Annealing

30 Mar 2020no code implementations

The compression of geometry data is an important aspect of bandwidth-efficient data transfer for distributed 3d computer vision applications.

Quantum Semantic Learning by Reverse Annealing an Adiabatic Quantum Computer

25 Mar 2020no code implementations

Moreover, to accelerate the learning, we implement a semantic quantum search which, contrary to previous proposals, takes the input data as initial boundary conditions to start each learning step of the RBM, thanks to a reverse annealing schedule.