The most successful algorithms in machine learning strike a balance between sample, model, and computational complexities. A low sample complexity means that the algorithm can work with only a little training data. Model complexity will influence the generalization performance, telling whether previously unseen data instances will be recognized correctly. A lower computational complexity will allow the algorithm to scale to larger amounts of data. Over the last two decades, several proposals have been put forward to perform machine learning using a quantum system. Advantages range from quadratic or even exponential speedup, reduced sample complexity and better generalization performance of the learned models. Examples include optimization through adiabatic annealing, quantum support vector machines based on a quantum random access memory, neural networks implemented with quantum dots or quantum optical systems, and quantum deep learning using state preparation and sampling. Some of these even have experimental demonstrations using actual quantum hardware. In this talk, we give an introduction to the most relevant concepts in quantum mechanics and quantum information theory to understand the current practical trade-offs. Then we overview the major research directions in quantum machine learning and discuss the feasibility of some of the proposed models.]]>

The most successful algorithms in machine learning strike a balance between sample, model, and computational complexities. A low sample complexity means that the algorithm can work with only a little training data. Model complexity will influence the generalization performance, telling whether previously unseen data instances will be recognized correctly. A lower computational complexity will allow the algorithm to scale to larger amounts of data. Over the last two decades, several proposals have been put forward to perform machine learning using a quantum system. Advantages range from quadratic or even exponential speedup, reduced sample complexity and better generalization performance of the learned models. Examples include optimization through adiabatic annealing, quantum support vector machines based on a quantum random access memory, neural networks implemented with quantum dots or quantum optical systems, and quantum deep learning using state preparation and sampling. Some of these even have experimental demonstrations using actual quantum hardware. In this talk, we give an introduction to the most relevant concepts in quantum mechanics and quantum information theory to understand the current practical trade-offs. Then we overview the major research directions in quantum machine learning and discuss the feasibility of some of the proposed models.]]>

Based on the Aristotelian concept of potentiality vs. actuality allowing for the study of energy and dynamics in language, we propose a field approach to lexical analysis. Falling back on the distributional hypothesis to statistically model word meaning, we used evolving fields as a metaphor to express time-dependent changes in a vector space model by a combination of random indexing and evolving self-organizing maps (ESOM). To monitor semantic drifts within the observation period, an experiment was carried out on the term space of a collection of 12.8 million Amazon book reviews. For evaluation, the semantic consistency of ESOM term clusters was compared with their respective neighbourhoods in WordNet, and contrasted with distances among term vectors by random indexing. We found that at 0.05 level of significance, the terms in the clusters showed a high level of semantic consistency. Tracking the drift of distributional patterns in the term space across time periods, we found that consistency decreased, but not at a statistically significant level. Our method is highly scalable, with interpretations in philosophy.]]>

Based on the Aristotelian concept of potentiality vs. actuality allowing for the study of energy and dynamics in language, we propose a field approach to lexical analysis. Falling back on the distributional hypothesis to statistically model word meaning, we used evolving fields as a metaphor to express time-dependent changes in a vector space model by a combination of random indexing and evolving self-organizing maps (ESOM). To monitor semantic drifts within the observation period, an experiment was carried out on the term space of a collection of 12.8 million Amazon book reviews. For evaluation, the semantic consistency of ESOM term clusters was compared with their respective neighbourhoods in WordNet, and contrasted with distances among term vectors by random indexing. We found that at 0.05 level of significance, the terms in the clusters showed a high level of semantic consistency. Tracking the drift of distributional patterns in the term space across time periods, we found that consistency decreased, but not at a statistically significant level. Our method is highly scalable, with interpretations in philosophy.]]>

In search for the right interpretation regarding a body of related content, we screened a small corpus of myths about Attis, a minor deity from the Hellenistic period in Asia Minor to identify the noncommutativity of key concepts used in storytelling. Looking at the protagonist’s typical features, our experiment showed incompatibility with regard to his gender and downfall. A crosscheck for entanglement found no violation of a Bell inequality, its best approximation being on the border of the local polytope.]]>

In search for the right interpretation regarding a body of related content, we screened a small corpus of myths about Attis, a minor deity from the Hellenistic period in Asia Minor to identify the noncommutativity of key concepts used in storytelling. Looking at the protagonist’s typical features, our experiment showed incompatibility with regard to his gender and downfall. A crosscheck for entanglement found no violation of a Bell inequality, its best approximation being on the border of the local polytope.]]>

Quantum learning of a unitary transformation estimates a quantum channel in a process similar to quantum process tomography. The classical counterpart of this goal, finding an unknown function, is regression, although the methodology hardly resembles the outline of classical algorithms. To gain a better understanding what such a methodology means to learning theory, we anchor it to the familiar concepts of active learning and transduction. Learning the unitary transformation translates to optimally storing it in quantum memory, but the quantum learning procedure also requires an optimal, maximally entangled input state. We argue that this is akin to active learning. Two different retrieval strategies apply when we would like to use the learned unitary transformation: a coherent strategy, which stores the unitary in quantum memory, and an incoherent one, which measures the unitary and stores it in classical memory; the latter strategy is considered optimal. We further argue that the incoherent strategy is a blend of inductive and transductive learning, as the optimal input state depends on the number of target states on which the transformation should be applied, yet once it is learned, the transformation can be used an arbitrary number of times. On the other hand, the sub-optimal coherent strategy of storing and applying the unitary is a form of transduction with no inductive element.]]>

Quantum learning of a unitary transformation estimates a quantum channel in a process similar to quantum process tomography. The classical counterpart of this goal, finding an unknown function, is regression, although the methodology hardly resembles the outline of classical algorithms. To gain a better understanding what such a methodology means to learning theory, we anchor it to the familiar concepts of active learning and transduction. Learning the unitary transformation translates to optimally storing it in quantum memory, but the quantum learning procedure also requires an optimal, maximally entangled input state. We argue that this is akin to active learning. Two different retrieval strategies apply when we would like to use the learned unitary transformation: a coherent strategy, which stores the unitary in quantum memory, and an incoherent one, which measures the unitary and stores it in classical memory; the latter strategy is considered optimal. We further argue that the incoherent strategy is a blend of inductive and transductive learning, as the optimal input state depends on the number of target states on which the transformation should be applied, yet once it is learned, the transformation can be used an arbitrary number of times. On the other hand, the sub-optimal coherent strategy of storing and applying the unitary is a form of transduction with no inductive element.]]>

The very fact that physics can model learning, a cognitive activity, tells us that we do not understand something fundamental about language, humans, and ultimately, the role of information in the universe. Namely in order for a set of rules to work to similar ends in two absolutely unrelated domains suggests that they must be based on the very same principles and are therefore not at all unrelated. Below we depart from this assumption and model an index term vocabulary over the Reuters-21578 document collection as a vector field. We use an emergent self-organizing map with approximately five nodes per index term to interpolate a potential field to study lexical gaps in distributional patterns. Our finding paves the way to model this vector field on physical fields, and thereby model lexical cohesion on forces.]]>

The very fact that physics can model learning, a cognitive activity, tells us that we do not understand something fundamental about language, humans, and ultimately, the role of information in the universe. Namely in order for a set of rules to work to similar ends in two absolutely unrelated domains suggests that they must be based on the very same principles and are therefore not at all unrelated. Below we depart from this assumption and model an index term vocabulary over the Reuters-21578 document collection as a vector field. We use an emergent self-organizing map with approximately five nodes per index term to interpolate a potential field to study lexical gaps in distributional patterns. Our finding paves the way to model this vector field on physical fields, and thereby model lexical cohesion on forces.]]>

A hierarchy of semidefinite programming (SDP) relaxations approximates the global optimum of polynomial optimization problems of noncommuting variables. Typical applications include determining genuine multipartite entanglement, finding mutually unbiased bases, and solving ground-state energy problems. Generating the relaxations, however, is a computationally demanding task, and only problems of commuting variables have efficient generators. A closer look at the applications reveals sparse structures: either the monomial base can be reduced or the moment matrix has an inherent structure. Exploiting sparsity leads to scalable generation of relaxations, and the resulting SDP problem is also easier to solve. The current algorithm is able to generate relaxations of optimization problems of a hundred noncommuting variables and a quadratic number of constraints.]]>

A hierarchy of semidefinite programming (SDP) relaxations approximates the global optimum of polynomial optimization problems of noncommuting variables. Typical applications include determining genuine multipartite entanglement, finding mutually unbiased bases, and solving ground-state energy problems. Generating the relaxations, however, is a computationally demanding task, and only problems of commuting variables have efficient generators. A closer look at the applications reveals sparse structures: either the monomial base can be reduced or the moment matrix has an inherent structure. Exploiting sparsity leads to scalable generation of relaxations, and the resulting SDP problem is also easier to solve. The current algorithm is able to generate relaxations of optimization problems of a hundred noncommuting variables and a quadratic number of constraints.]]>

Support vector machines on quantum computers can be exponentially faster. We take a look at the connections between machine learning and quantum computing, trying to understand learning in a quantum context. We focus on least squares support vector machines.]]>

Support vector machines on quantum computers can be exponentially faster. We take a look at the connections between machine learning and quantum computing, trying to understand learning in a quantum context. We focus on least squares support vector machines.]]>

A forager in a patchy environment faces two types of uncertainty: ambiguity regarding the quality of the current patch and risk associated with the background opportunities. We argue that the order in which the forager deals with these uncertainties has an impact on the decision whether to stay at the current patch. The order effect is formalised with a context-dependent quantum probabilistic framework. Using Heisenberg’s uncertainty principle, we demonstrate the two types of uncertainty cannot be simultaneously minimised, hence putting a formal limit on rationality in decision making. We show the applicability of the contextual decision function with agent-based modelling. The simulations reveal order-dependence. Given that foraging is a universal pattern that goes beyond animal behaviour, the findings help understand similar phenomena in other fields.]]>

A forager in a patchy environment faces two types of uncertainty: ambiguity regarding the quality of the current patch and risk associated with the background opportunities. We argue that the order in which the forager deals with these uncertainties has an impact on the decision whether to stay at the current patch. The order effect is formalised with a context-dependent quantum probabilistic framework. Using Heisenberg’s uncertainty principle, we demonstrate the two types of uncertainty cannot be simultaneously minimised, hence putting a formal limit on rationality in decision making. We show the applicability of the contextual decision function with agent-based modelling. The simulations reveal order-dependence. Given that foraging is a universal pattern that goes beyond animal behaviour, the findings help understand similar phenomena in other fields.]]>

Complex numbers are a fundamental aspect of the mathematical formalism of quantum physics. Quantum-like models developed outside physics often overlooked the role of complex numbers. Specifically, previous models in Information Retrieval (IR) ignored complex numbers. We argue that to advance the use of quantum models of IR, one has to lift the constraint of real-valued representations of the information space, and package more information within the representation by means of complex numbers. As a first attempt, we propose a complex-valued representation for IR, which explicitly uses complex valued Hilbert spaces, and thus where terms, documents and queries are represented as complex-valued vectors. The proposal consists of integrating distributional semantics evidence within the real component of a term vector; whereas, ontological information is encoded in the imaginary component. Our proposal has the merit of lifting the role of complex numbers from a computational byproduct of the model to the very mathematical texture that unifies different levels of semantic information. An empirical instantiation of our proposal is tested in the TREC Medical Record task of retrieving cohorts for clinical studies. ]]>

Complex numbers are a fundamental aspect of the mathematical formalism of quantum physics. Quantum-like models developed outside physics often overlooked the role of complex numbers. Specifically, previous models in Information Retrieval (IR) ignored complex numbers. We argue that to advance the use of quantum models of IR, one has to lift the constraint of real-valued representations of the information space, and package more information within the representation by means of complex numbers. As a first attempt, we propose a complex-valued representation for IR, which explicitly uses complex valued Hilbert spaces, and thus where terms, documents and queries are represented as complex-valued vectors. The proposal consists of integrating distributional semantics evidence within the real component of a term vector; whereas, ontological information is encoded in the imaginary component. Our proposal has the merit of lifting the role of complex numbers from a computational byproduct of the model to the very mathematical texture that unifies different levels of semantic information. An empirical instantiation of our proposal is tested in the TREC Medical Record task of retrieving cohorts for clinical studies. ]]>

We introduce Claude Lévi Strauss’ canonical formula (CF), an attempt to rigorously formalise the general narrative structure of myth. This formula utilises the Klein group as its basis, but a recent work draws attention to its natural quaternion form, which opens up the possibility that it may require a quantum inspired interpretation. We present the CF in a form that can be understood by a non-anthropological audience, using the formalisation of a key myth (that of Adonis) to draw attention to its mathematical structure. The future potential formalisation of mythological structure within a quantum inspired framework is proposed and discussed, with a probabilistic interpretation further generalising the formula.]]>

We introduce Claude Lévi Strauss’ canonical formula (CF), an attempt to rigorously formalise the general narrative structure of myth. This formula utilises the Klein group as its basis, but a recent work draws attention to its natural quaternion form, which opens up the possibility that it may require a quantum inspired interpretation. We present the CF in a form that can be understood by a non-anthropological audience, using the formalisation of a key myth (that of Adonis) to draw attention to its mathematical structure. The future potential formalisation of mythological structure within a quantum inspired framework is proposed and discussed, with a probabilistic interpretation further generalising the formula.]]>

The aim of this seminar is to provide students with basic knowledge on developing applications for processors with massively parallel computing resources. In general, we refer to a processor as massively parallel if it has the ability to complete more than 64 arithmetic operations per clock cycle. Graphics processing units (GPUs) fall into this category, but other massively parallel architectures are emergent. Effectively programming these processors will require in-depth knowledge about parallel programming principles, as well as the parallelism models, communication models, memory hierarchy, and resource limitations of these processors. We will also overview some tools that reduce the initial difficulties of CUDA programming.]]>

The aim of this seminar is to provide students with basic knowledge on developing applications for processors with massively parallel computing resources. In general, we refer to a processor as massively parallel if it has the ability to complete more than 64 arithmetic operations per clock cycle. Graphics processing units (GPUs) fall into this category, but other massively parallel architectures are emergent. Effectively programming these processors will require in-depth knowledge about parallel programming principles, as well as the parallelism models, communication models, memory hierarchy, and resource limitations of these processors. We will also overview some tools that reduce the initial difficulties of CUDA programming.]]>

Digital preservation deals with the problem of retaining the meaning of digital information over time to ensure its accessibility. The process often involves a workflow which transforms the digital objects. The workflow defines document pipelines containing transformations and validation checkpoints, either to facilitate migration for persistent archival or to extract metadata. The transformations, nevertheless, are computationally expensive, and therefore digital preservation can be out of reach for an organization whose core operation is not in data conservation. The operations described the document workflow, however, do not frequently reoccur. This paper combines an implementation-independent workflow designer with cloud computing to support small institution in their ad-hoc peak computing needs that stem from their efforts in digital preservation.]]>

Digital preservation deals with the problem of retaining the meaning of digital information over time to ensure its accessibility. The process often involves a workflow which transforms the digital objects. The workflow defines document pipelines containing transformations and validation checkpoints, either to facilitate migration for persistent archival or to extract metadata. The transformations, nevertheless, are computationally expensive, and therefore digital preservation can be out of reach for an organization whose core operation is not in data conservation. The operations described the document workflow, however, do not frequently reoccur. This paper combines an implementation-independent workflow designer with cloud computing to support small institution in their ad-hoc peak computing needs that stem from their efforts in digital preservation.]]>

The Aarne-Thompson-Uther Tale Type Catalog (ATU) is a bibliographic tool which uses metadata from tale content, called motifs, to define tale types as canonical motif sequences. The motifs themselves are listed in another bibliographic tool, the Aarne-Thompson Motif Index (AaTh). Tale types in ATU are defined in an abstracted fashion and can be processed like a corpus. We analyzed 219 types with 1202 motifs from the “Tales of magic” (types 300-749) segment to exemplify that motif sequences show signs of recombination in the storytelling process. Compared to chromosome mutations in genetics, we offer examples for insertion/deletion, duplication and, possibly, transposition, whereas the sample was not sufficient to find inverted motif strings as well. These initial findings encourage efforts to sequence motif strings like DNA in genetics, attempting to find for instance the longest common motif subsequences in tales. Expressing the network of motif connections by graphs suggests that tale plots as consolidated pathways of content help one memorize culturally engraved messages. We anticipate a connection between such networks and Waddington’s epigenetic landscape.]]>

The Aarne-Thompson-Uther Tale Type Catalog (ATU) is a bibliographic tool which uses metadata from tale content, called motifs, to define tale types as canonical motif sequences. The motifs themselves are listed in another bibliographic tool, the Aarne-Thompson Motif Index (AaTh). Tale types in ATU are defined in an abstracted fashion and can be processed like a corpus. We analyzed 219 types with 1202 motifs from the “Tales of magic” (types 300-749) segment to exemplify that motif sequences show signs of recombination in the storytelling process. Compared to chromosome mutations in genetics, we offer examples for insertion/deletion, duplication and, possibly, transposition, whereas the sample was not sufficient to find inverted motif strings as well. These initial findings encourage efforts to sequence motif strings like DNA in genetics, attempting to find for instance the longest common motif subsequences in tales. Expressing the network of motif connections by graphs suggests that tale plots as consolidated pathways of content help one memorize culturally engraved messages. We anticipate a connection between such networks and Waddington’s epigenetic landscape.]]>

In this poster we introduce a MapReduce-based implementation of self-organizing maps that performs compute-bound operations on distributed GPUs. The kernels are optimized to ensure coalesced memory access and effective use of shared memory. We have performed extensive tests of our algorithms on a cluster of eight nodes with two NVidia Tesla M2050 attached to each, and we achieve a 10x speedup for self-organizing maps over a distributed CPU algorithm.]]>

In this poster we introduce a MapReduce-based implementation of self-organizing maps that performs compute-bound operations on distributed GPUs. The kernels are optimized to ensure coalesced memory access and effective use of shared memory. We have performed extensive tests of our algorithms on a cluster of eight nodes with two NVidia Tesla M2050 attached to each, and we achieve a 10x speedup for self-organizing maps over a distributed CPU algorithm.]]>

Based on a computed toy example, we offer evidence that by plugging in similarity of word meaning as a force plus a small modification of Newton’s 2nd law, one can acquire specific "mass" values for index terms in a Saltonesque dynamic library environment. The model can describe two types of change which affect the semantic composition of document collections: the expansion of a corpus due to its update, and fluctuations of the gravitational potential energy field generated by normative language use as an attractor juxtaposed with actual language use yielding time-dependent term frequencies. By the evolving semantic potential of a vocabulary and concatenating the respective term "mass" values, one can model sentences or longer strings of symbols as vector-valued functions. Since the line integral of such functions is used to express the work of a particle in a gravitational field, the work equivalent of strings can be calculated.]]>

Based on a computed toy example, we offer evidence that by plugging in similarity of word meaning as a force plus a small modification of Newton’s 2nd law, one can acquire specific "mass" values for index terms in a Saltonesque dynamic library environment. The model can describe two types of change which affect the semantic composition of document collections: the expansion of a corpus due to its update, and fluctuations of the gravitational potential energy field generated by normative language use as an attractor juxtaposed with actual language use yielding time-dependent term frequencies. By the evolving semantic potential of a vocabulary and concatenating the respective term "mass" values, one can model sentences or longer strings of symbols as vector-valued functions. Since the line integral of such functions is used to express the work of a particle in a gravitational field, the work equivalent of strings can be calculated.]]>

New concepts like agent-based modelling are providing social scientists with new tools, more suited to their background than other simulation techniques. The success of this new trend will be strongly related to the existence of simulation tools capable of fulfilling the needs of these disciplines. Given the computational requirement of realistic agent-based models, high-performance computing infrastructure is often necessary to perform the calculations. At present, such resources are unlikely to be available to humanities researchers. Having developed Pandora, an open-source framework designed to create and execute large-scale social simulations in high-performance computing environments, this work presents an evaluation of the impact of cloud computing within this context. We find that the constraints of the cloud environment do not have a significant impact on the generic pattern of execution, providing a cost-effective solution for social scientists.]]>

New concepts like agent-based modelling are providing social scientists with new tools, more suited to their background than other simulation techniques. The success of this new trend will be strongly related to the existence of simulation tools capable of fulfilling the needs of these disciplines. Given the computational requirement of realistic agent-based models, high-performance computing infrastructure is often necessary to perform the calculations. At present, such resources are unlikely to be available to humanities researchers. Having developed Pandora, an open-source framework designed to create and execute large-scale social simulations in high-performance computing environments, this work presents an evaluation of the impact of cloud computing within this context. We find that the constraints of the cloud environment do not have a significant impact on the generic pattern of execution, providing a cost-effective solution for social scientists.]]>

Archaeological studies on battlefields may see great benefits from simulated military engagements: simulations help testing hypotheses based on historical data and may also help with validating methodologies used on the site. Such methods, however, require high-performance computing expertise and considerable computational power. With the emergence of on-demand computing instances in the cloud, distributed computations have become available to technically every organization or individual. This puts large-scale battlefield simulations within the reach of archaeologists, and the cloud paradigm also lowers the required technological expertise, potentially leading to a more widespread adoption of such simulation methods.]]>

Archaeological studies on battlefields may see great benefits from simulated military engagements: simulations help testing hypotheses based on historical data and may also help with validating methodologies used on the site. Such methods, however, require high-performance computing expertise and considerable computational power. With the emergence of on-demand computing instances in the cloud, distributed computations have become available to technically every organization or individual. This puts large-scale battlefield simulations within the reach of archaeologists, and the cloud paradigm also lowers the required technological expertise, potentially leading to a more widespread adoption of such simulation methods.]]>

With insight from linguistics that degrees of text cohesion are similar to forces in physics, and the frequent use of the energy concept in text categorization by machine learning, we consider the applicability of particle-wave duality to semantic content inherent in index terms. Wave-like interpretations go back to the regional nature of such content, utilizing functions for its representation, whereas content as a particle can be conveniently modelled by position vectors. Interestingly, wave packets behave like particles, lending credibility to the duality hypothesis. We show in a classical mechanics framework how metaphorical term mass can be computed.]]>

With insight from linguistics that degrees of text cohesion are similar to forces in physics, and the frequent use of the energy concept in text categorization by machine learning, we consider the applicability of particle-wave duality to semantic content inherent in index terms. Wave-like interpretations go back to the regional nature of such content, utilizing functions for its representation, whereas content as a particle can be conveniently modelled by position vectors. Interestingly, wave packets behave like particles, lending credibility to the duality hypothesis. We show in a classical mechanics framework how metaphorical term mass can be computed.]]>

High-performance computational resources and distributed systems are crucial for the success of real-world language technology applications. The novel paradigm of general-purpose computing on graphics processors (GPGPU) offers a feasible and economical alternative: it has already become a common phenomenon in scientific computation, with many algorithms adapted to the new paradigm. However, applications in language technology do not readily adapt to this approach. Recent advances show the applicability of quantum metaphors in language representation, and many algorithms in quantum mechanics have already been adapted to GPGPU computing. SQUALAR aims to match quantum algorithms with heterogeneous computing to develop new formalisms of information representation for natural language processing in quantum ]]>

High-performance computational resources and distributed systems are crucial for the success of real-world language technology applications. The novel paradigm of general-purpose computing on graphics processors (GPGPU) offers a feasible and economical alternative: it has already become a common phenomenon in scientific computation, with many algorithms adapted to the new paradigm. However, applications in language technology do not readily adapt to this approach. Recent advances show the applicability of quantum metaphors in language representation, and many algorithms in quantum mechanics have already been adapted to GPGPU computing. SQUALAR aims to match quantum algorithms with heterogeneous computing to develop new formalisms of information representation for natural language processing in quantum ]]>

Spectral theory in mathematics is key to the success of as diverse application domains as quantum mechanics and latent semantic indexing, both relying on eigenvalue decomposition for the localization of their respective entities in observation space. This points at some implicit "energy" inherent in semantics and in need of quantification. We show how the structure of atomic emission spectra, and meaning in concept space, go back to the same compositional principle, plus propose a tentative solution for the computation of term, document and collection "energy" content.]]>

Spectral theory in mathematics is key to the success of as diverse application domains as quantum mechanics and latent semantic indexing, both relying on eigenvalue decomposition for the localization of their respective entities in observation space. This points at some implicit "energy" inherent in semantics and in need of quantification. We show how the structure of atomic emission spectra, and meaning in concept space, go back to the same compositional principle, plus propose a tentative solution for the computation of term, document and collection "energy" content.]]>

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