Quantum machine learning: from physics to software engineering
Quantum machine learning is a rapidly growing field at the intersection of quantum
technology and artificial intelligence. This review provides a two-fold overview of several key …
technology and artificial intelligence. This review provides a two-fold overview of several key …
A survey of quantum computing for finance
Quantum computers are expected to surpass the computational capabilities of classical
computers during this decade and have transformative impact on numerous industry sectors …
computers during this decade and have transformative impact on numerous industry sectors …
QNLP in practice: Running compositional models of meaning on a quantum computer
Abstract Quantum Natural Language Processing (QNLP) deals with the design and
implementation of NLP models intended to be run on quantum hardware. In this paper, we …
implementation of NLP models intended to be run on quantum hardware. In this paper, we …
lambeq: An efficient high-level python library for quantum NLP
We present lambeq, the first high-level Python library for Quantum Natural Language
Processing (QNLP). The open-source toolkit offers a detailed hierarchy of modules and …
Processing (QNLP). The open-source toolkit offers a detailed hierarchy of modules and …
Benchmarking simulated and physical quantum processing units using quantum and hybrid algorithms
M Kordzanganeh, M Buchberger… - Advanced Quantum …, 2023 - Wiley Online Library
Powerful hardware services and software libraries are vital tools for quickly and affordably
designing, testing, and executing quantum algorithms. A robust large‐scale study of how the …
designing, testing, and executing quantum algorithms. A robust large‐scale study of how the …
Membership inference attack susceptibility of clinical language models
Deep Neural Network (DNN) models have been shown to have high empirical privacy
leakages. Clinical language models (CLMs) trained on clinical data have been used to …
leakages. Clinical language models (CLMs) trained on clinical data have been used to …
Quantum natural language processing: Challenges and opportunities
The meeting between Natural Language Processing (NLP) and Quantum Computing has
been very successful in recent years, leading to the development of several approaches of …
been very successful in recent years, leading to the development of several approaches of …
Variational inference with a quantum computer
Inference is the task of drawing conclusions about unobserved variables given observations
of related variables. Applications range from identifying diseases from symptoms to …
of related variables. Applications range from identifying diseases from symptoms to …
Quantum-enhanced support vector machine for sentiment classification
Quantum computers have potential computational abilities such as speeding up complex
computations, parallelism by superpositions, and handling large data sets. Moreover, the …
computations, parallelism by superpositions, and handling large data sets. Moreover, the …
A CCG-based version of the DisCoCat framework
While the DisCoCat model (Coecke et al., 2010) has been proved a valuable tool for
studying compositional aspects of language at the level of semantics, its strong dependency …
studying compositional aspects of language at the level of semantics, its strong dependency …