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Found 12 papers, 6 papers with code
Functorial Question Answering
Distributional compositional (DisCo) models are functors that compute the meaning of a sentence from the meaning of its words.
Meaning updating of density matrices
This result is of interest to quantum foundations, as it bridges the work in Categorical Quantum Mechanics (CQM) with that on conditional quantum states.
Quantum Natural Language Processing on Near-Term Quantum Computers
In this work, we describe a full-stack pipeline for natural language processing on near-term quantum computers, aka QNLP.
Grammar-aware sentence classification on quantum computers
In this work, we focus on the capabilities of noisy intermediate-scale quantum (NISQ) hardware and perform the first implementation of an NLP task on a NISQ processor, using the DisCoCat framework.
Foundations for Near-Term Quantum Natural Language Processing
We recall how the quantum model for natural language that we employ canonically combines linguistic meanings with rich linguistic structure, most notably grammar.
QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer
Our aim in doing this is to take the first small steps in this unexplored research territory and pave the way for practical Quantum Natural Language Processing.
How to make qubits speak
This is a story about making quantum computers speak, and doing so in a quantum-native, compositional and meaning-aware manner.
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).
A Quantum Natural Language Processing Approach to Musical Intelligence
In particular, we are importing methods from the Distributional Compositional Categorical (DisCoCat) modelling framework for Natural Language Processing (NLP), motivated by musical grammars.
Equivariant Quantum Graph Circuits
We investigate quantum circuits for graph representation learning, and propose equivariant quantum graph circuits (EQGCs), as a class of parameterized quantum circuits with strong relational inductive bias for learning over graph-structured data.
Peptide Binding Classification on Quantum Computers
By constructing quantum models based on parameterised quantum circuits we perform sequence classification on a task relevant to the design of therapeutic proteins, and find competitive performance with classical baselines of similar scale.
Quantum Circuit Optimization with AlphaTensor
A key challenge in realizing fault-tolerant quantum computers is circuit optimization.
