Quantum adiabatic machine learning

Abstract

We develop an approach to machine learning and anomaly detection via quantum adiabatic evolution. In the training phase we identify an optimal set of weak classifiers, to form a single strong classifier. In the testing phase we adiabatically evolve one or more strong classifiers on a superposition of inputs in order to find certain anomalous elements in the classification space. Both the training and testing phases are executed via quantum adiabatic evolution. We apply and illustrate this approach in detail to the problem of software verification and validation.

21 pages, 9 figures

References in corpus (12)

• Bounds for the adiabatic approximation with applications to quantum computation
• Experimental Investigation of an Eight Qubit Unit Cell in a Superconducting Optimization Processor
• Simple proof of equivalence between adiabatic quantum computation and the circuit model
• Realizable Hamiltonians for Universal Adiabatic Quantum Computers
• Quantum Adiabatic Brachistochrone
• Error correcting codes for adiabatic quantum computation
• Adiabatic approximation with exponential accuracy for many-body systems and quantum computation
• Towards Fault Tolerant Adiabatic Quantum Computation
• Size dependence of the minimum excitation gap in the Quantum Adiabatic Algorithm
• Perturbative Gadgets at Arbitrary Orders
• Intrinsic geometry of quantum adiabatic evolution and quantum phase transitions
• Accuracy vs run time in adiabatic quantum search

Cited by in corpus (45)

• Quantum support vector machine for big data classification
• Adiabatic Quantum Computing
• An introduction to quantum machine learning
• Quantum Boltzmann Machine
• Strawberry Fields: A Software Platform for Photonic Quantum Computing
• Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
• Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning
• Quantum speedup for active learning agents
• Support vector machines on the D-Wave quantum annealer
• Tomography and Generative Data Modeling via Quantum Boltzmann Training
• Quantum annealing versus classical machine learning applied to a simplified computational biology problem
• Quantum Variational Autoencoder
• Quantum Machine Learning for Chemistry and Physics
• Fidelity-based Probabilistic Q-learning for Control of Quantum Systems
• Nonnegative/binary matrix factorization with a D-Wave quantum annealer
• Prospects for Quantum Enhancement with Diabatic Quantum Annealing
• Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models
• Quantum Machine Learning in High Energy Physics
• Quantum machine learning over infinite dimensions
• Quantum assisted Gaussian process regression
• Quantum-assisted Helmholtz machines: A quantum-classical deep learning framework for industrial datasets in near-term devices
• Recent advances for quantum classifiers
• Quantum learning robust to noise
• Basic Elements for Simulations of Standard Model Physics with Quantum Annealers: Multigrid and Clock States
• Hybrid quantum-classical machine learning for generative chemistry and drug design
• Quantum processor-inspired machine learning in the biomedical sciences
• Machine learning \& artificial intelligence in the quantum domain
• Quantum-enhanced deliberation of learning agents using trapped ions
• New Trends in Quantum Machine Learning
• Quantum learning of coherent states
• Quantum Relief Algorithm
• An Integrated Programming and Development Environment for Adiabatic Quantum Optimization
• Advantages of Unfair Quantum Ground-State Sampling
• Quantum adiabatic machine learning with zooming
• Adiabatic Quantum Optimization for Associative Memory Recall
• Quantum algorithms for training Gaussian Processes
• Entanglement-based quantum deep learning
• Garden optimization problems for benchmarking quantum annealers
• Determining system Hamiltonian from eigenstate measurements without correlation functions
• Experimental implementation of quantum algorithm for association rules mining
• The role of entanglement for enhancing the efficiency of quantum kernels towards classification
• Near-Term Quantum-Classical Associative Adversarial Networks
• A QUBO formulation for top-$\tau$ eigencentrality nodes
• Energy Landscape Structure of Small Graph Isomorphism Under Variational Optimization
• Performance Models for Split-execution Computing Systems