Stochastic search for approximate compilation of unitaries

Compilation of unitaries into a sequence of physical quantum gates is a critical prerequisite for execution of quantum algorithms. This work introduces STOQ, a stochastic search protocol for approximate unitary compilation into a sequence of gates from an arbitrary gate alphabet. We demonstrate STOQ by comparing its performance to existing product-formula compilation techniques for time-evolution unitaries on system sizes up to eight qubits. The compilations generated by STOQ are less accurate than those from product-formula techniques, but they are similar in runtime and traverse significantly different paths in state space. We also use STOQ to generate compilations of randomly-generated unitaries, and we observe its ability to generate approximately-equivalent compilations of unitaries corresponding to shallow random circuits. Finally, we discuss the applicability of STOQ to tasks such as characterization of near-term quantum devices

Sampling-Based Methods for Factored Task and Motion Planning

This paper presents a general-purpose formulation of a large class of discrete-time planning problems, with hybrid state and control-spaces, as factored transition systems. Factoring allows state transitions to be described as the intersection of several constraints each affecting a subset of the state and control variables. Robotic manipulation problems with many movable objects involve constraints that only affect several variables at a time and therefore exhibit large amounts of factoring. We develop a theoretical framework for solving factored transition systems with sampling-based algorithms. The framework characterizes conditions on the submanifold in which solutions lie, leading to a characterization of robust feasibility that incorporates dimensionality-reducing constraints. It then connects those conditions to corresponding conditional samplers that can be composed to produce values on this submanifold. We present two domain-independent, probabilistically complete planning algorithms that take, as input, a set of conditional samplers. We demonstrate the empirical efficiency of these algorithms on a set of challenging task and motion planning problems involving picking, placing, and pushing

A roadmap to robot motion planning software development

"This is the peer reviewed version of the following article: Pérez, A. and Rosell, J. (2010), A roadmap to robot motion planning software development. Comput. Appl. Eng. Educ., 18: 651-660. doi:doi.org/10.1002/cae.20269, which has been published in final form at https://doi.org/10.1002/cae.20269. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."PhD programs and graduate studies in robotics usually include motion planning among its main subjects. Students that focus their research in this subject find themselves trapped in the necessity of programming an environment where to test and validate their theoretic contributions. The programming of this robot motion planning environment is a big challenge. It requires on the one hand good programming skills involving the use of software development tools, programming paradigms, or the knowledge of computational complexity and efficiency issues. On the other hand it requires coping with different related issues like the modeling of objects, computational geometry problems and graphical representations and interfaces. The mastering of all these techniques is good for the curricula of roboticists with a motion planning profile. Nevertheless, the time and effort devoted to this end must remain reasonable. Within this framework, the aim of this paper is to provide the students with a roadmap to help them in the development of the software tools needed to test and validate their robot motion planners. The proposals are made within the scope of multi-platform open source codePeer ReviewedPostprint (author's final draft