• Corpus ID: 119073684

Quantum Language Processing

@article{Wiebe2019QuantumLP,
  title={Quantum Language Processing},
  author={Nathan Wiebe and Alex Bocharov and Paul Smolensky and Matthias Troyer and Krysta Marie Svore},
  journal={arXiv: Quantum Physics},
  year={2019}
}
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… 

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References

SHOWING 1-10 OF 44 REFERENCES

Tomography and generative training with quantum Boltzmann machines

It is demonstrated that quantum Boltzmann machines enable a form of quantum state tomography that not only estimates a state but provides a prescription for generating copies of the reconstructed state as well as evidence that quantum models outperform their classical counterparts.

Quantum Algorithms for Quantum Field Theories

A quantum algorithm to compute relativistic scattering probabilities in a massive quantum field theory with quartic self-interactions in spacetime of four and fewer dimensions is developed and achieves exponential speedup over the fastest known classical algorithm.

Quantum Boltzmann Machine

This work proposes a new machine learning approach based on quantum Boltzmann distribution of a transverse-field Ising Hamiltonian that allows the QBM efficiently by sampling and discusses the possibility of using quantum annealing processors like D-Wave for QBM training and application.

Simulation of electronic structure Hamiltonians using quantum computers

Over the last century, a large number of physical and mathematical developments paired with rapidly advancing technology have allowed the field of quantum chemistry to advance dramatically. However,

A quantum-inspired classical algorithm for recommendation systems

  • Ewin Tang
  • Computer Science
    Electron. Colloquium Comput. Complex.
  • 2018
A classical analogue to Kerenidis and Prakash’s quantum recommendation system is given, previously believed to be one of the strongest candidates for provably exponential speedups in quantum machine learning, which produces recommendations exponentially faster than previous classical systems, which run in time linear in m and n.

Optimizing quantum optimization algorithms via faster quantum gradient computation

A quantum algorithm that computes the gradient of a multi-variate real-valued function by evaluating it at only a logarithmic number of points in superposition is developed, and it is shown that for low-degree multivariate polynomials the algorithm can provide exponential speedups compared to Jordan's algorithm in terms of the dimension $d.

Universal adiabatic quantum computation via the space-time circuit-to-Hamiltonian construction.

The Hamiltonian is shown how to perform universal adiabatic quantum computation using a Hamiltonian which describes a set of particles with local interactions on a two-dimensional grid and the time evolution is equivalent to the exactly solvable quantum walk on Young's lattice.

Quantum annealing in the transverse Ising model

We introduce quantum fluctuations into the simulated annealing process of optimization problems, aiming at faster convergence to the optimal state. Quantum fluctuations cause transitions between

Incremental parsing in a continuous dynamical system: sentence processing in Gradient Symbolic Computation

A Gradient Symbolic Computation parser is introduced, a continuous-state, continuous-time stochastic dynamical-system model of symbolic processing, which builds up a discrete symbolic structure gradually by dynamically strengthening a discreteness constraint.

Efficient Quantum Algorithms for Simulating Sparse Hamiltonians

We present an efficient quantum algorithm for simulating the evolution of a quantum state for a sparse Hamiltonian H over a given time t in terms of a procedure for computing the matrix entries of H.