Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Information Processing Letters
The Strength of Weak Learnability
Machine Learning
Geometric Bounds for Generalization in Boosting
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
An introduction to boosting and leveraging
Advanced lectures on machine learning
Equivalences and Separations Between Quantum and Classical Learnability
SIAM Journal on Computing
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Chapter I: Notes on structured programming
Structured programming
Design by Contract to Improve Software Vigilance
IEEE Transactions on Software Engineering
Adiabatic Quantum Computation is Equivalent to Standard Quantum Computation
SIAM Journal on Computing
Minor-embedding in adiabatic quantum computation: I. The parameter setting problem
Quantum Information Processing
ACM Computing Surveys (CSUR)
On the use of computational geometry to detect software faults at runtime
Proceedings of the 7th international conference on Autonomic computing
Investigating the performance of an adiabatic quantum optimization processor
Quantum Information Processing
Machine learning in a quantum world
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
SP 800-142. Practical Combinatorial Testing
SP 800-142. Practical Combinatorial Testing
A Survey of Automated Techniques for Formal Software Verification
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Quantum decision tree classifier
Quantum Information Processing
Adiabatic quantum programming: minor embedding with hard faults
Quantum Information Processing
Hi-index | 0.00 |
We develop an approach to machine learning and anomaly detection via quantum adiabatic evolution. This approach consists of two quantum phases, with some amount of classical preprocessing to set up the quantum problems. 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. All quantum processing is strictly limited to two-qubit interactions so as to ensure physical feasibility. We apply and illustrate this approach in detail to the problem of software verification and validation, with a specific example of the learning phase applied to a problem of interest in flight control systems. Beyond this example, the algorithm can be used to attack a broad class of anomaly detection problems.