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Variational Quantum Factoring

  • Eric Anschuetz
  • Jonathan Olson
  • Alán Aspuru-Guzik
  • Yudong CaoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11413)

Abstract

Integer factorization has been one of the cornerstone applications of the field of quantum computing since the discovery of an efficient algorithm for factoring by Peter Shor. Unfortunately, factoring via Shor’s algorithm is well beyond the capabilities of today’s noisy intermediate-scale quantum (NISQ) devices. In this work, we revisit the problem of factoring, developing an alternative to Shor’s algorithm, which employs established techniques to map the factoring problem to the ground state of an Ising Hamiltonian. The proposed variational quantum factoring (VQF) algorithm starts by simplifying equations over Boolean variables in a preprocessing step to reduce the number of qubits needed for the Hamiltonian. Then, it seeks an approximate ground state of the resulting Ising Hamiltonian by training variational circuits using the quantum approximate optimization algorithm (QAOA). We benchmark the VQF algorithm on various instances of factoring and present numerical results on its performance.

Keywords

Integer factorization Quantum computation Discrete optimization 

Notes

Acknowledgments

We would like to acknowledge the Zapata Computing scientific team, including Peter Johnson, Jhonathan Romero, Borja Peropadre, and Hannah Sim for their insightful and inspiring comments.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Eric Anschuetz
    • 1
  • Jonathan Olson
    • 1
  • Alán Aspuru-Guzik
    • 1
  • Yudong Cao
    • 1
    Email author
  1. 1.Zapata Computing Inc.CambridgeUSA

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