A reduced complexity numerical method for optimal gate synthesis

Although quantum computers have the potential to efficiently solve certain problems considered difficult by known classical approaches, the design of a quantum circuit remains computationally difficult. It is known that the optimal gate design problem is equivalent to the solution of an associated optimal control problem, the solution to which is also computationally intensive. Hence, in this article, we introduce the application of a class of numerical methods (termed the max-plus curse of dimensionality free techniques) that determine the optimal control thereby synthesizing the desired unitary gate. The application of this technique to quantum systems has a growth in complexity that depends on the cardinality of the control set approximation rather than the much larger growth with respect to spatial dimensions in approaches based on gridding of the space, used in previous literature. This technique is demonstrated by obtaining an approximate solution for the gate synthesis on $SU(4)$- a problem that is computationally intractable by grid based approaches.Comment: 8 pages, 4 figure

Stochastic Game Approach to Air Operations

A Command and Control (C2) problem for Military Air Operations is addressed. Specifically, we consider C2 problems for air vehicles against ground based targets and defensive systems. The problem is viewed as a stochastic game. In this paper, we restrict our attention to the C2 level where the problem may consist of a few UCAVs or aircraft (or possibly teams of vehicles); less than say, a half-dozen enemy SAMs; a few enemy assets (viewed as targets from our standpoint); and some enemy decoys (assumed to mimic SAM radar signatures). At this low level, some targets are mapped out and possible SAM sites that are unavoidably part of the situation are known. One may then employ a discrete stochastic game problem formulation to determine which of these SAMs should optimally be engaged (if any), and by what series of air vehicle operations. Since this is a game model, the optimal opponent strategy is also determined. We provide analysis, numerical implementation, and simulation for full state feedback and measurement feedback control within this C2 context

The principle of least action and two-point boundary value problems in orbital mechanics

Abstract-We consider a two-point boundary value problem (TPBVP) in orbital mechanics involving a small body (e.g., a spacecraft or asteroid) and N larger bodies. The least action principle TPBVP formulation is converted into an initial value problem via the addition of an appropriate terminal cost to the action functional. The latter formulation is used to obtain a fundamental solution, which may be used to solve the TPBVP for a variety of boundary conditions within a certain class. In particular, the method of convex duality allows one to interpret the least action principle as a differential game, where an opposing player maximizes over an indexed set of quadratics to yield the gravitational potential. The fundamental solution is obtained as a set of solutions of associated Riccati equations

Convergence rate for a curse-ofdimensionality-free method for a class of HJB PDEs

Abstract. In previous work of the first author and others, max-plus methods have been explored for solution of firstorder, nonlinear Hamilton-Jacobi-Bellman partial differential equations (HJB PDEs) and corresponding nonlinear control problems. Although max-plus basis expansion and max-plus finite-element methods can provide substantial computationalspeed advantages, they still generally suffer from the curse-of-dimensionality. Here we consider HJB PDEs where the Hamiltonian takes the form of a (pointwise) maximum of linear/quadratic forms. The approach to solution will be rather general, but in order to ground the work, we consider only constituent Hamiltonians corresponding to long-run averagecost-per-unit-time optimal control problems for the development. We consider a previously obtained numerical method not subject to the curse-of-dimensionality. The method is based on construction of the dual-space semigroup corresponding to the HJB PDE. This dual-space semigroup is constructed from the dual-space semigroups corresponding to the constituent linear/quadratic Hamiltonians. The dual-space semigroup is particularly useful due to its form as a max-plus integral operator with kernel obtained from the originating semigroup. One considers repeated application of the dual-space semigroup to obtain the solution. Although previous work indicated that the method was not subject to the curse-of-dimensionality, it did not indicate any error bounds or convergence rate. Here, we obtain specific error bounds. Key words. partial differential equations, curse-of-dimensionality, dynamic programming, max-plus algebra, Legendre transform, Fenchel transform, semiconvexity, Hamilton-Jacobi-Bellman equations, idempotent analysis. AMS subject classifications. 49LXX, 93C10, 35B37, 35F20, 65N99, 47D99 1. Introduction. A robust approach to the solution of nonlinear control problems is through the general method of dynamic programming. For the typical class of problems in continuous time and continuous space, with the dynamics governed by finite-dimensional, ordinary differential equations, this leads to a representation of the problem as a first-order, nonlinear partial differential equation, the Hamilton-Jacobi-Bellman equation -or the HJB PDE. If one has an infinite time-horizon problem, then the HJB PDE is a steady-state equation, and this PDE is over a space (or some subset thereof) whose dimension is the dimension of the state variable of the control problem. Due to the nonlinearity, the solutions are generally nonsmooth, and one must use the theory of viscosity solution