QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer

Lorenz, Robin G.; Pearson, A. W.; Meichanetzidis, Konstantinos; Kartsaklis, Dimitri; Coecke, Bob

doi:10.48550/arxiv.2102.12846

Cited by 17 publications

(36 citation statements)

References 21 publications

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“…Because of this connection and the use of tensor operations, the group argued that this natural language model is "quantum-native," meaning it is more natural to run the DisCoCat model on a quantum computer instead of a classical one [95]. Coecke et al ran multiple experiments on NISQ devices for a variety of simple NLP tasks [106,223,235].…”

Section: Natural Language Modelingmentioning

confidence: 99%

A Survey of Quantum Computing for Finance

Herman¹,

Googin²,

Liu³

et al. 2022

Preprint

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Quantum computers are expected to surpass the computational capabilities of classical computers during this decade and have transformative impact on numerous industry sectors, particularly finance. In fact, finance is estimated to be the first industry sector to benefit from quantum computing, not only in the medium and long terms, but even in the short term. This survey paper presents a comprehensive summary of the state of the art of quantum computing for financial applications, with particular emphasis on Monte Carlo integration, optimization, and machine learning, showing how these solutions, adapted to work on a quantum computer, can help solve more efficiently and accurately problems such as derivative pricing, risk analysis, portfolio optimization, natural language processing, and fraud detection. We also discuss the feasibility of these algorithms on near-term quantum computers with various hardware implementations and demonstrate how they relate to a wide range of use cases in finance. We hope this article will not only serve as a reference for academic researchers and industry practitioners but also inspire new ideas for future research.

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Section: Natural Language Modelingmentioning

confidence: 99%

A Survey of Quantum Computing for Finance

Herman¹,

Googin²,

Liu³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…quantum picturalism [24], we know that such a presentation leads to a much more intuitive purely diagrammatic formalism, important computational advantages, e.g. in quantum computing [54,51,52,82], and bridges disciplines giving rise to new technologies such as quantum natural language processing [117,28,87]. The paper [35] lists recent compositionality-driven advances for the specific case of the ZX-calculus.…”

Section: Compositionality In Linguistics and Quantum Physicsmentioning

confidence: 99%

“…The crux was the fact that quantum compositional structure is, category-theoretically speaking, exactly the same as grammatical structure [21]. This correspondence has recently resulted in implementations of NLP on existing quantum computers, by ourselves at Cambridge Quantum [28,29,90,87,76]. We provide more details on this in Sections 5.1 and 6.…”

Section: Introductionmentioning

confidence: 99%

Compositionality as we see it, everywhere around us

Coecke¹

2021

Preprint

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There are different meanings of the term "compositionality" within science: what one researcher would call compositional, is not at all compositional for another researcher. The most established conception is usually attributed to Frege, and is characterised by a bottomup flow of meanings: the meaning of the whole can be derived from the meanings of the parts, and how these parts are structured together.Inspired by work on compositionality in quantum theory, and categorical quantum mechanics in particular, we propose the notions of Schrödinger, Whitehead, and complete compositionality. Accounting for recent important developments in quantum technology and artificial intelligence, these do not have the bottom-up meaning flow as part of their definitions.Schrödinger compositionality accommodates quantum theory, and also meaning-as-context. Complete compositionality further strengthens Schrödinger compositionality in order to single out theories like ZX-calculus, that are complete with regard to the intended model. All together, our new notions aim to capture the fact that compositionality is at its best when it is 'real', 'non-trivial', and even more when it also is 'complete'.At this point we only put forward the intuitive and/or restricted formal definitions, and leave a fully comprehensive definition to future collaborative work.

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“…Moreover, it is possible to directly evaluate the tensor by performing local rewrites. Using these local rewrites, ZX-calculus has been successfully applied to circuit compilation [3,4,16,30], measurement-based quantum computing [22,17], fusion-based quantum computing [6], quantum error correction [5], quantum natural language processing [12,25,21], and quantum foundations [1,11,10,18]. ZX-calculus can even be used as a concrete realisation of quantum theory [13].…”

Section: Introductionmentioning

confidence: 99%

Differentiating and Integrating ZX Diagrams

Wang¹,

Yeung²

2022

Preprint

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ZX-calculus has proved to be a useful tool for quantum technology with a wide range of successful applications. Most of these applications are of an algebraic nature. However, other tasks that involve differentiation and integration remain unreachable with current ZX techniques. Here we elevate ZX to an analytical perspective by realising differentiation and integration entirely within the framework of ZXcalculus. We explicitly illustrate the new analytic framework of ZX-calculus by applying it in context of quantum machine learning.

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QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer

Cited by 17 publications

References 21 publications

A Survey of Quantum Computing for Finance

A Survey of Quantum Computing for Finance

Compositionality as we see it, everywhere around us

Differentiating and Integrating ZX Diagrams

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