Locality and nonlocality in quantum pure-state identification problems

Suppose we want to identify an input state with one of two unknown reference states, where the input state is guaranteed to be equal to one of the reference states. We assume that no classical knowledge of the reference states is given, but a certain number of copies of them are available instead. Two reference states are independently and randomly chosen from the state space in a unitary invariant way. This is called the quantum state identification problem, and the task is to optimize the mean identification success probability. In this paper, we consider the case where each reference state is pure and bipartite, and generally entangled. The question is whether the maximum mean identification success probability can be attained by means of a local operations and classical communication (LOCC) measurement scheme. Two types of identification problems are considered when a single copy of each reference state is available. We show that a LOCC scheme attains the globally achievable identification probability in the minimum-error identification problem. In the unambiguous identification problem, however, the maximal success probability by means of LOCC is shown to be less than the globally achievable identification probability.Comment: 11 pages, amalgamation of arXiv:0712.2906 and arXiv:0801.012

Unambiguous pure state identification without classical knowledge

We study how to unambiguously identify a given quantum pure state with one of the two reference pure states when no classical knowledge on the reference states is given but a certain number of copies of each reference quantum state are presented. By the unambiguous identification, we mean that we are not allowed to make a mistake but our measurement can produce an inconclusive result. Assuming the two reference states are independently distributed over the whole pure state space in a unitary invariant way, we determine the optimal mean success probability for an arbitrary number of copies of the reference states and a general dimension of the state space. It is explicitly shown that the obtained optimal mean success probability asymptotically approaches that of the unambiguous discrimination as the number of the copies of the reference states increases.Comment: v3: 8 pages, minor corrections, journal versio

Frequency domain state-space system identification

An algorithm for identifying state-space models from frequency response data of linear systems is presented. A matrix-fraction description of the transfer function is employed to curve-fit the frequency response data, using the least-squares method. The parameters of the matrix-fraction representation are then used to construct the Markov parameters of the system. Finally, state-space models are obtained through the Eigensystem Realization Algorithm using Markov parameters. The main advantage of this approach is that the curve-fitting and the Markov parameter construction are linear problems which avoid the difficulties of nonlinear optimization of other approaches. Another advantage is that it avoids windowing distortions associated with other frequency domain methods

Identification of Structured LTI MIMO State-Space Models

The identification of structured state-space model has been intensively studied for a long time but still has not been adequately addressed. The main challenge is that the involved estimation problem is a non-convex (or bilinear) optimization problem. This paper is devoted to developing an identification method which aims to find the global optimal solution under mild computational burden. Key to the developed identification algorithm is to transform a bilinear estimation to a rank constrained optimization problem and further a difference of convex programming (DCP) problem. The initial condition for the DCP problem is obtained by solving its convex part of the optimization problem which happens to be a nuclear norm regularized optimization problem. Since the nuclear norm regularized optimization is the closest convex form of the low-rank constrained estimation problem, the obtained initial condition is always of high quality which provides the DCP problem a good starting point. The DCP problem is then solved by the sequential convex programming method. Finally, numerical examples are included to show the effectiveness of the developed identification algorithm.Comment: Accepted to IEEE Conference on Decision and Control (CDC) 201

Identification of Mental States and Interpersonal Functioning in Borderline Personality Disorder

Atypical identification of mental states in the self and others has been proposed to underlie interpersonal difficulties in borderline personality disorder (BPD), yet no previous empirical research has directly examined associations between these constructs. We examine 3 mental state identification measures and their associations with experience-sampling measures of interpersonal functioning in participants with BPD relative to a healthy comparison (HC) group. We also included a clinical comparison group diagnosed with avoidant personality disorder (APD) to test the specificity of this constellation of difficulties to BPD. When categorizing blended emotional expressions, the BPD group identified anger at a lower threshold than did the HC and APD groups, but no group differences emerged in the threshold for identifying happiness. These results are consistent with enhanced social threat identification and not general negativity biases in BPD. The Reading the Mind in the Eyes Test (RMET) showed no group differences in general mental state identification abilities. Alexithymia scores were higher in both BPD and APD relative to the HC group, and difficulty identifying one’s own emotions was higher in BPD compared to APD and HC. Within the BPD group, lower RMET scores were associated with lower anger identification thresholds and higher alexithymia scores. Moreover, lower anger identification thresholds, lower RMET scores, and higher alexithymia scores were all associated with greater levels of interpersonal difficulties in daily life. Research linking measures of mental state identification with experience-sampling measures of interpersonal functioning can help clarify the role of mental state identification in BPD symptoms

Sequential Monte Carlo Methods for System Identification

One of the key challenges in identifying nonlinear and possibly non-Gaussian state space models (SSMs) is the intractability of estimating the system state. Sequential Monte Carlo (SMC) methods, such as the particle filter (introduced more than two decades ago), provide numerical solutions to the nonlinear state estimation problems arising in SSMs. When combined with additional identification techniques, these algorithms provide solid solutions to the nonlinear system identification problem. We describe two general strategies for creating such combinations and discuss why SMC is a natural tool for implementing these strategies.Comment: In proceedings of the 17th IFAC Symposium on System Identification (SYSID). Added cover pag

Non linear system identification : a state-space approach

In this thesis, new system identication methods are presented for three particular types of nonlinear systems: linear parameter-varying state-space systems, bilinear state-space systems, and local linear state-space systems. Although most work on nonlinear system identication deals with nonlinear input-output descriptions, this thesis focuses on state-space descriptions. State-space systems are considered, because they are especially suitable for dealing with multiple inputs and outputs, and they usually require less parameters to describe a system than input-output descriptions do. Equally important, the starting point of many nonlinear control methods is a state-space model of the system to be controlled