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Vatan, F.

Publications and source records attributed to Vatan, F..

Optimal design of two-qubit quantum circuits

In order to demonstrate non-trivial quantum computations experimentally, such as the synthesis of arbitrary entangled states, it will be useful to nderstand how to decompose a desired quantum computation into the shortest possible sequence of one-qubit and two-qubit gates. We contribute to this effort by providing a method to construct an optimal quantum circuit for a general two-qubit gate that requires at most 3 CNOT gates and 15 elementary one qubit gates. We then prove that these constructions are optimal with respect to the family of CNOT, y-rotation, z-rotation, and phase gates.

quantum circuit unitary operation

An advanced model-based diagnosis engine

We have developed a new and powerful diagnosis engine that overcomes the limitations of the existing systematic methods of general diagnosis through a two-fold approach. First, we propose a novel and compact reconstruction of the General Diagnosis Engine, one of the most fundamental approaches to model-based diagnoses. We then present a novel algorithmic approach for calculation of minimal diagnosis set.

model-based diagnosis hitting set problem integer

A novel model-based diagnosis engine: theory and applications

Systematic methods of general diagnosis exist in literature, but they all suffer from two major drawbacks that severely limit their practical applications. In this paper, we propose a two-fold approach to overcome these limitations.

diagnosis integer programming hitting set problem

Distribution functions of probabilistic automata

Each probabilistic automaton M over an alphabet A defines a probability measure Prob sub(M) on the set of all finite and infinite words over A. We can identify a k letter alphabet A with the set {0, 1,..., k-1}, and, hence, we can consider every finite or infinite word w over A as a radix k expansion of a real number X(w) in the interval [0, 1]. This makes X(w) a random variable and the distribution function of M is defined as usual: F(x) := Prob sub(M) { w: X(w) < x }. Utilizing the fixed-point semantics (denotational semantics), extended to probabilistic computations, we investigate the distribution functions of probabilistic automata in detail. Automata with continuous distribution functions are characterized. By a new, and much more easier method, it is shown that the distribution function F(x) is an analytic function if it is a polynomial. Finally, answering a question posed by D. Knuth and A. Yao, we show that a polynomial distribution function F(x) on [0, 1] can be generated by a prob abilistic automaton iff all the roots of F'(x) = 0 in this interval, if any, are rational numbers. For this, we define two dynamical systems on the set of polynomial distributions and study attracting fixed points of random composition of these two systems.

Denotational semantics