Quantum Language Processing

Abstract

We present a representation for linguistic structure that we call a Fock-space representation, which allows us to embed problems in language processing into small quantum devices. We further develop a formalism for understanding both classical as well as quantum linguistic problems and phrase them both as a Harmony optimization problem that can be solved on a quantum computer which we show is related to classifying vectors using quantum Boltzmann machines. We further provide a new training method for learning quantum Harmony operators that describe a language. This also provides a new algorithm for training quantum Boltzmann machines that requires no approximations and works in the presence of hidden units. We additionally show that quantum language processing is BQP-complete, meaning that it is polynomially equivalent to the circuit model of quantum computing which implies that quantum language models are richer than classical models unless BPP=BQP. It also implies that, under certain circumstances, quantum Boltzmann machines are more expressive than classical Boltzmann machines. Finally, we examine the performance of our approach numerically for the case of classical harmonic grammars and find that the method is capable of rapidly parsing even non-trivial grammars. This suggests that the work may have value as a quantum inspired algorithm beyond its initial motivation as a new quantum algorithm.

References in corpus (10)

• Quantum Annealing in the Transverse Ising Model
• Quantum support vector machine for big data classification
• Quantum Algorithms for Quantum Field Theories
• Quantum Simulations of Classical Annealing Processes
• Quantum gradient descent for linear systems and least squares
• Tomography and Generative Data Modeling via Quantum Boltzmann Training
• Quantum Algorithms for Fermionic Quantum Field Theories
• Quantum-inspired low-rank stochastic regression with logarithmic dependence on the dimension
• Universal adiabatic quantum computation via the space-time circuit-to-Hamiltonian construction
• Quantum Inspired Training for Boltzmann Machines

Cited by in corpus (9)

• QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer
• Generative training of quantum Boltzmann machines with hidden units
• A hybrid classical-quantum workflow for natural language processing
• Foundations for Near-Term Quantum Natural Language Processing
• Quantum Mathematics in Artificial Intelligence
• Near-Term Quantum-Classical Associative Adversarial Networks
• lambeq: An Efficient High-Level Python Library for Quantum NLP
• Quantum Machine Learning For Classical Data
• Should Semantic Vector Composition be Explicit? Can it be Linear?