Entanglement as a semantic resource

The characteristic holistic features of the quantum theoretic formalism and the intriguing notion of entanglement can be applied to a field that is far from microphysics: logical semantics. Quantum computational logics are new forms of quantum logic that have been suggested by the theory of quantum logical gates in quantum computation. In the standard semantics of these logics, sentences denote quantum information quantities: systems of qubits (quregisters) or, more generally, mixtures of quregisters (qumixes), while logical connectives are interpreted as special quantum logical gates (which have a characteristic reversible and dynamic behavior). In this framework, states of knowledge may be entangled, in such a way that our information about the whole determines our information about the parts; and the procedure cannot be, generally, inverted. In spite of its appealing properties, the standard version of the quantum computational semantics is strongly "Hilbert-space dependent". This certainly represents a shortcoming for all applications, where real and complex numbers do not generally play any significant role (as happens, for instance, in the case of natural and of artistic languages). We propose an abstract version of quantum computational semantics, where abstract qumixes, quregisters and registers are identified with some special objects (not necessarily living in a Hilbert space), while gates are reversible functions that transform qumixes into qumixes. In this framework, one can give an abstract definition of the notions of superposition and of entangled pieces of information, quite independently of any numerical values. We investigate three different forms of abstract holistic quantum computational logic

Support vector machines with quantum state discrimination

We analyze possible connections between quantum-inspired classifications and support vector machines. Quantum state discrimination and optimal quantum measurement are useful tools for classification problems. In order to use these tools, feature vectors have to be encoded in quantum states represented by density operators. Classification algorithms inspired by quantum state discrimination and implemented on classic computers have been recently proposed. We focus on the implementation of a known quantum-inspired classifier based on Helstrom state discrimination showing its connection with support vector machines and how to make the classification more efficient in terms of space and time acting on quantum encoding. In some cases, traditional methods provide better results. Moreover, we discuss the quantum-inspired nearest mean classification

Quantum-Inspired Classification Based on Voronoi Tessellation and Pretty-Good Measurements

In quantum machine learning, feature vectors are encoded into quantum states. Measurements for the discrimination of states are useful tools for classification problems. Classification algorithms inspired by quantum state discrimination have recently been implemented on classical computers. We present a local approach combining Vonoroi-type tessellation of a training set with pretty-good measurements for quantum state discrimination

A quantum computational semantics for epistemic logical operators. Part I: epistemic structures

Some critical open problems of epistemic logics can be investigated in the framework of a quantum computational approach. The basic idea is to interpret sentences like “Alice knows that Bob does not understand that π is irrational” as pieces of quantum information (generally represented by density operators of convenient Hilbert spaces). Logical epistemic operators (to understand, to know. . .) are dealt with as (generally irreversible) quantum operations, which are, in a sense, similar to measurement-procedures. This approach permits us to model some characteristic epistemic processes, that concern both human and artificial intelligence. For instance, the operation of “memorizing and retrieving information” can be formally represented, in this framework, by using a quantum teleportation phenomenon

A first-order epistemic quantum computational semantics with relativistic-like epistemic effects

Quantum computation has suggested new forms of quantum logic, called quantum computational logics. In these logics well-formed formulas are supposed to denote pieces of quantum information: possible pure states of quantum systems that can store the information in question. At the same time, the logical connectives are interpreted as quantum logical gates: unitary operators that process quantum information in a reversible way, giving rise to quantum circuits. Quantum computational logics have been mainly studied as sentential logics (whose alphabet consists of atomic sentences and of logical connectives). In this article we propose a semantic characterization for a first-order epistemic quantum computational logic, whose language can express sentences like "Alice knows that everybody knows that she is pretty". One can prove that (unlike the case of logical connectives) both quantifiers and epistemic operators cannot be generally represented as (reversible) quantum logical gates. The "act of knowing" and the use of universal (or existential) assertions seem to involve some irreversible "theoretic jumps", which are similar to quantum measurements. Since all epistemic agents are characterized by specific epistemic domains (which contain all pieces of information accessible to them), the unrealistic phenomenon of logical omniscience is here avoided: knowing a given sentence does not imply knowing all its logical consequences

Epistemic quantum computational structures in a Hilbert-space environment

Some critical open problems of epistemic logics can be investigated in the framework of a quantum computational approach. The basic idea is to interpret sentences like “Alice knows that Bob does not understand that π is irrational” as pieces of quantum information (generally represented by density operators of convenient Hilbert spaces). Logical epistemic operators (to understand, to know ...) are dealt with as (generally irreversible) quantum operations, which are, in a sense, similar to measurement-procedures. This approach permits us to model some characteristic epistemic processes, that concern both human and artificial intelligence. For instance, the operation of “memorizing and retrieving information” can be formally represented, in this framework, by using a quantum teleportation phenomenon

Quantum information, cognition, and music

Parallelism represents an essential aspect of human mind/brain activities. One can recognize some common features between psychological parallelism and the characteristic parallel structures that arise in quantum theory and in quantum computation. The article is devoted to a discussion of the following questions: 1. a comparison between classical probabilistic Turing machines and quantum Turing machines. 2. possible applications of the quantum computational semantics to cognitive problems. 3. parallelism in music

Ceiba speciosa (A. St.-Hil.) seeds oil: Fatty acids profiling by GC-MS and NMR and bioactivity

This study aimed to evaluate the chemical composition by gas chromatography-mass spectrometry (GC-MS) and Nuclear Magnetic Resonance (NMR) analyses, the antioxidant activities evaluated by different in vitro assays namely 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,20-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS), Ferric Reducing Ability Power (FRAP), and β-carotene bleaching tests, and the inhibitory effects of enzymes linked to obesity (lipase, α-amylase, and α-glucosidase) of fixed seed oil of Ceiba speciosa (A. St.-Hil.). Fourteen compounds were identified. Linoleic acid (28.22%) was the most abundant followed by palmitic acid (19.56%). Malvalic acid (16.15%), sterculic acid (11.11%), and dihydrosterculic acid (2.74%) were also detected. C. speciosa fixed oil exerted a promising ABTS radicals scavenging activity with an IC50 value of 10.21 µg/mL, whereas an IC50 of 77.44 µg/mL against DPPH+ radicals was found. C. speciosa fixed oil inhibited lipase with an IC50 value of 127.57 µg/mL. The present investigation confirmed the functional properties of C. speciosa fixed oil, and proposes its use as valuable source of bioactive constituents

Using accessible digital resources for teaching database design: towards an inclusive distance learning proposal

[Proceedings of] 13th Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE'08), Madrid, Spain, June 30-July 2, 2008This paper introduces a pilot experience in teaching database using accessible digital resources in 3er course of Computer Science degree at Universidad Carlos III de Madrid. A platform containing learning material in different formats (video, audio, slides presentation) has been designed allowing students accessing resources as well as to be evaluated by means of tests. Preliminary results show that 46.81% of the students have already interacted with the system and 97.12% of the students passed the tests.Publicad