Computational Techniques for Stochastic Reachability

As automated control systems grow in prevalence and complexity, there is an increasing demand for verification and controller synthesis methods to ensure these systems perform safely and to desired specifications. In addition, uncertain or stochastic behaviors are often exhibited (such as wind affecting the motion of an aircraft), making probabilistic verification desirable. Stochastic reachability analysis provides a formal means of generating the set of initial states that meets a given objective (such as safety or reachability) with a desired level of probability, known as the reachable (or safe) set, depending on the objective. However, the applicability of reachability analysis is limited in the scope and size of system it can address. First, generating stochastic reachable or viable sets is computationally intensive, and most existing methods rely on an optimal control formulation that requires solving a dynamic program, and which scales exponentially in the dimension of the state space. Second, almost no results exist for extending stochastic reachability analysis to systems with incomplete information, such that the controller does not have access to the full state of the system. This thesis addresses both of the above limitations, and introduces novel computational methods for generating stochastic reachable sets for both perfectly and partially observable systems. We initially consider a linear system with additive Gaussian noise, and introduce two methods for computing stochastic reachable sets that do not require dynamic programming. The first method uses a particle approximation to formulate a deterministic mixed integer linear program that produces an estimate to reachability probabilities. The second method uses a convex chance-constrained optimization problem to generate an under-approximation to the reachable set. Using these methods we are able to generate stochastic reachable sets for a four-dimensional spacecraft docking example in far less time than it would take had we used a dynamic program. We then focus on discrete time stochastic hybrid systems, which provide a flexible modeling framework for systems that exhibit mode-dependent behavior, and whose state space has both discrete and continuous components. We incorporate a stochastic observation process into the hybrid system model, and derive both theoretical and computational results for generating stochastic reachable sets subject to an observation process. The derivation of an information state allows us to recast the problem as one of perfect information, and we prove that solving a dynamic program over the information state is equivalent to solving the original problem. We then demonstrate that the dynamic program to solve the reachability problem for a partially observable stochastic hybrid system shares the same properties as for a partially observable Markov decision process (POMDP) with an additive cost function, and so we can exploit approximation strategies designed for POMDPs to solve the reachability problem. To do so, however, we first generate approximate representations of the information state and value function as either vectors or Gaussian mixtures, through a finite state approximation to the hybrid system or using a Gaussian mixture approximation to an indicator function defined over a convex region. For a system with linear dynamics and Gaussian measurement noise, we show that it exhibits special properties that do not require an approximation of the information state, which enables much more efficient computation of the reachable set. In all cases we provide convergence results and numerical examples

Development of efficient and low-scaling methods to compute molecular properties at MP2 and double-hybrid DFT levels

This thesis introduces new methods to compute molecular properties at the level of second-order Møller-Plesset perturbation theory (MP2) and double-hybrid density functional theory, building on a reformulation in atomic orbitals and exploiting the rank deficiency of the (pseudo-)density matrices, thus reducing the scaling behavior with respect to the size of the basis set. By furthermore employing the resolution-of-the-identity approximation, low-scaling and efficient MP2 energy gradients are presented, where significant two-electron integrals are screened using a distance-including integral estimation technique. With this, the forces and the hyperfine coupling constants of systems larger than previously computable at the MP2-level are obtained. In the second part of this thesis, the locality of the spin density in many molecular systems is exploited in the computation of the hyperfine coupling constants, leading to further speed-ups and allowing for a thorough investigation of the effect of the protein environment on the hyperfine coupling within the core region of a pyruvate formate lyase. With this efficient method, studying the effect of nuclear motion on the accuracy of the computed hyperfine coupling constants is possible. The study presented in this thesis demonstrates that both electron correlation and vibrational motion are crucial for an accurate theoretical description. When calculating magnetic properties, the dependence on the choice of gauge origins needs to be considered. This effect is studied systematically, and in detail, in a fourth project of this thesis for the computation of electronic g-tensors, for which it was previously assumed that the computation is largely independent of the choice of the gauge-origin. The study clearly contradicts this assumption and motivates the use of gauge including atomic orbitals in future work on electronic g-tensors. In a last part, this work transfers the algorithmic developments on the computation of analytic gradients to the computation of nuclear magnetic resonance (NMR) shieldings at the MP2-level. Though a sublinear scaling ansatz to compute the NMR shielding tensor per nucleus is available, the lack of an efficient implementation and the large dependency on the size of the basis sets prohibits the accurate computation of the shielding tensor of medium- to large-sized molecules. Furthermore, while this ansatz in theory scales linearly when all nuclei in a system are computed, it is inefficient due to the dependence of the rate-determining steps on the nuclear magnetic moments. This thesis therefore presents a new all-nuclei ansatz and introduces the methodology for the efficient computation of the energy gradients developed in this thesis, highlighting significant computational savings

A Framework for Worst-Case and Stochastic Safety Verification Using Barrier Certificates

This paper presents a methodology for safety verification of continuous and hybrid systems in the worst-case and stochastic settings. In the worst-case setting, a function of state termed barrier certificate is used to certify that all trajectories of the system starting from a given initial set do not enter an unsafe region. No explicit computation of reachable sets is required in the construction of barrier certificates, which makes it possible to handle nonlinearity, uncertainty, and constraints directly within this framework. In the stochastic setting, our method computes an upper bound on the probability that a trajectory of the system reaches the unsafe set, a bound whose validity is proven by the existence of a barrier certificate. For polynomial systems, barrier certificates can be constructed using convex optimization, and hence the method is computationally tractable. Some examples are provided to illustrate the use of the method

Hybrid quantum computing with ancillas

In the quest to build a practical quantum computer, it is important to use efficient schemes for enacting the elementary quantum operations from which quantum computer programs are constructed. The opposing requirements of well-protected quantum data and fast quantum operations must be balanced to maintain the integrity of the quantum information throughout the computation. One important approach to quantum operations is to use an extra quantum system - an ancilla - to interact with the quantum data register. Ancillas can mediate interactions between separated quantum registers, and by using fresh ancillas for each quantum operation, data integrity can be preserved for longer. This review provides an overview of the basic concepts of the gate model quantum computer architecture, including the different possible forms of information encodings - from base two up to continuous variables - and a more detailed description of how the main types of ancilla-mediated quantum operations provide efficient quantum gates.Comment: Review paper. An introduction to quantum computation with qudits and continuous variables, and a review of ancilla-based gate method