arXiv subjects

Pratik Ghosal

Publications and source records attributed to Pratik Ghosal.

23 records · Page 2Linked to original sources

arXiv2023

Quantum superposition of causal structures as a universal resource for local implementation of nonlocal quantum operations

Spatial separation restricts the set of locally implementable quantum operations on distributed multipartite quantum systems. We propose that indefinite causal structure arising due to quantum superposition of different space-time geometries can be used as an independent universal resource for local implementation of any quantum operation on spatially distributed quantum systems. Consequently, all such quantum tasks that are not accomplishable by local operations and classical communication (LOCC) only also become locally accomplishable. We show that exploiting indefinite causal structure as the sole resource, it is possible to perfectly teleport the state of one agent's subsystem to the other distant laboratory in such a way that the agent at the distant laboratory can have access to the whole initially shared state in his or her laboratory and can perform any global quantum operation on the joint state locally. We further find that, after the teleportation process, the resource -- indefinite causal structure of the space-time does not get consumed. Hence, after implementing the desired quantum operation the state of the first agent's subsystem can be teleported back to its previous laboratory using the same resource. We show that this two-way teleportation is not always necessary for locally executing all nonlocal quantum tasks that are not realisable by LOCC only. Without invoking any kind of teleportation, we present a protocol for perfect local discrimination of the set of four Bell states that exploits indefinite causal structure as the sole resource. As immediate upshots, we present some more examples of such nonlocal tasks as local discrimination of the set of states exhibiting ``quantum nonlocality without entanglement" and activation of bound entangled states that are also achievable by our proposed protocol incorporating indefinite causal structure as a resource.

quant-ph↗

arXiv2022

Probability distribution for black hole evaporation

Non-thermal correction to the emission probability of particles from black holes can be obtained if the backreaction or self-gravitational effects of the emitted particles on the black hole spacetime are taken into consideration. These non-thermally emitted particles conserve the entropy of the black hole, i.e, the entropy of the system of radiated particles after complete evaporation of the black hole matches the initial entropy of the black hole. Using the non-thermal emission probability, we have determined the probability for a black hole of mass $M$ to be completely evaporated by a given number of particles $n$. This is done by first evaluating the number of possible ways in which the black hole can be evaporated by emitting $n$ number of particles, and then the total number of ways in which the black hole can be evaporated. The ratio of these two quantities gives us the desired probability. From the probability distribution, we get a displacement relation between the most probable number of particles exhausting the black hole and the temperature of the initial black hole. This relation resembles Wien's displacement law for blackbody radiation.

gr-qc↗

arXiv2020

The Dynamics of Rank-Maximal and Popular Matchings

Given a bipartite graph, where the two sets of vertices are applicants and posts and ranks on the edges represent preferences of applicants over posts, a {\em rank-maximal} matching is one in which the maximum number of applicants is matched to their rank one posts and subject to this condition, the maximum number of applicants is matched to their rank two posts, and so on. We study the dynamic version of the problem in which a new applicant or post may be added to the graph and we would like to maintain a rank-maximal matching. We show that after the arrival of one vertex, we are always able to update the existing rank-maximal matching in $\mathcal{O}(\min(c'n ,n^2) + m)$ time, where $n$ denotes the number of applicants, $m$ the number of edges and $c'$ the maximum rank of an edge in an optimal solution. Additionally, we update the matching using a minimal number of changes (replacements). All cases of a deletion of a vertex/edge and an addition of an edge can be reduced to the problem of handling the addition of a vertex. As a by-product, we also get an analogous $\mathcal{O}(m)$ result for the dynamic version of the (one-sided) popular matching problem. Our results are based on the novel use of the properties of the Edmonds-Gallai decomposition. The presented ideas may find applications in other (dynamic) matching problems.

cs.DS↗

arXiv2018

Manipulation Strategies for the Rank Maximal Matching Problem

We consider manipulation strategies for the rank-maximal matching problem. In the rank-maximal matching problem we are given a bipartite graph $G = (A \cup P, E)$ such that $A$ denotes a set of applicants and $P$ a set of posts. Each applicant $a \in A$ has a preference list over the set of his neighbours in $G$, possibly involving ties. Preference lists are represented by ranks on the edges - an edge $(a,p)$ has rank $i$, denoted as $rank(a,p)=i$, if post $p$ belongs to one of $a$'s $i$-th choices. A rank-maximal matching is one in which the maximum number of applicants is matched to their rank one posts and subject to this condition, the maximum number of applicants is matched to their rank two posts, and so on. A rank-maximal matching can be computed in $O(\min(c \sqrt{n},n) m)$ time, where $n$ denotes the number of applicants, $m$ the number of edges and $c$ the maximum rank of an edge in an optimal solution. A central authority matches applicants to posts. It does so using one of the rank-maximal matchings. Since there may be more than one rank- maximal matching of $G$, we assume that the central authority chooses any one of them randomly. Let $a_1$ be a manipulative applicant, who knows the preference lists of all the other applicants and wants to falsify his preference list so that he has a chance of getting better posts than if he were truthful. In the first problem addressed in this paper the manipulative applicant $a_1$ wants to ensure that he is never matched to any post worse than the most preferred among those of rank greater than one and obtainable when he is truthful. In the second problem the manipulator wants to construct such a preference list that the worst post he can become matched to by the central authority is best possible or in other words, $a_1$ wants to minimize the maximal rank of a post he can become matched to.

cs.DS↗

arXiv2015

Characterisation of Strongly Stable Matchings

An instance of a strongly stable matching problem (SSMP) is an undirected bipartite graph $G=(A \cup B, E)$, with an adjacency list of each vertex being a linearly ordered list of ties, which are subsets of vertices equally good for a given vertex. Ties are disjoint and may contain one vertex. A matching $M$ is a set of vertex-disjoint edges. An edge $(x,y) \in E \setminus M$ is a {\em blocking edge} for $M$ if $x$ is either unmatched or strictly prefers $y$ to its current partner in $M$, and $y$ is either unmatched or strictly prefers $x$ to its current partner in $M$ or is indifferent between them. A matching is {\em strongly stable} if there is no blocking edge with respect to it. We present an algorithm for the generation of all strongly stable matchings, thus solving an open problem already stated in the book by Gusfield and Irving \cite{GI}. It has previously been shown that strongly stable matchings form a distributive lattice and although the number of strongly stable matchings can be exponential in the number of vertices, we show that there exists a partial order with $O(m)$ elements representing all strongly stable matchings, where $m$ denotes the number of edges in the graph. We give two algorithms that construct two such representations: one in $O(nm^2)$ time and the other in $O(nm)$ time, where $n$ denotes the number of vertices in the graph. Note that the construction of the second representation has the same time complexity as that of computing a single strongly stable matching.

cs.DS↗