Introduction to the Special issue on the Techniques of Programming Languages, Logic, and Formal Methods in Quantum Computing

2021 ◽  
Vol 2 (4) ◽  
pp. 1-3
Author(s):  
Xiaodi Wu
2006 ◽  
Vol 16 (3) ◽  
pp. 373-374
Author(s):  
PETER SELINGER

This special issue of Mathematical Structures in Computer Science grew out of the 2nd International Workshop on Quantum Programming Languages (QPL 2004), which was held July 12–13, 2004 in Turku, Finland. The purpose of the workshop was to bring together researchers working on mathematical formalisms and programming languages for quantum computing. It was the second in a series of workshops aimed at addressing a growing interest in logical tools, languages, and semantical methods for analysing quantum computation.


2013 ◽  
Vol 23 (4) ◽  
pp. 675-675
Author(s):  
AZER BESTAVROS ◽  
ASSAF KFOURY

The papers included in this special issue of Mathematical Structures in Computer Science were selected from a larger set we solicited from leading research groups on both sides of the Atlantic. They cover a wide spectrum of tutorials, recent results and surveys in the area of lightweight and practical formal methods in the design and analysis of safety-critical systems. All the papers we received were submitted to a rigorous process of review and revision, based on which we made our final selection.


2011 ◽  
Vol 76 (2) ◽  
pp. 63-64
Author(s):  
Darren Cofer ◽  
Alessandro Fantechi ◽  
Stefan Leue ◽  
Pedro Merino

Author(s):  
Abhinav Verma

We study the problem of generating interpretable and verifiable policies for Reinforcement Learning (RL). Unlike the popular Deep Reinforcement Learning (DRL) paradigm, in which the policy is represented by a neural network, the aim of this work is to find policies that can be represented in highlevel programming languages. Such programmatic policies have several benefits, including being more easily interpreted than neural networks, and being amenable to verification by scalable symbolic methods. The generation methods for programmatic policies also provide a mechanism for systematically using domain knowledge for guiding the policy search. The interpretability and verifiability of these policies provides the opportunity to deploy RL based solutions in safety critical environments. This thesis draws on, and extends, work from both the machine learning and formal methods communities.


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