Noisy Intermediate-Scale Quantum algorithms

Abstract

A universal fault-tolerant quantum computer that can solve efficiently problems such as integer factorization and unstructured database search requires millions of qubits with low error rates and long coherence times. While the experimental advancement towards realizing such devices will potentially take decades of research, noisy intermediate-scale quantum (NISQ) computers already exist. These computers are composed of hundreds of noisy qubits, i.e. qubits that are not error-corrected, and therefore perform imperfect operations in a limited coherence time. In the search for quantum advantage with these devices, algorithms have been proposed for applications in various disciplines spanning physics, machine learning, quantum chemistry and combinatorial optimization. The goal of such algorithms is to leverage the limited available resources to perform classically challenging tasks. In this talk, I provide an overview of NISQ algorithms, their limitations and potential advantages.

Date
Jul 15, 2021
Location
Perspectives on Quantum Sensing and Computation for Particle Physics, CERN, Virtual

Based on the reference:

  • “Noisy intermediate-scale quantum (NISQ) algorithms”, Kishor Bharti, Alba Cervera-Lierta, Thi Ha Kyaw, Tobias Haug, Sumner Alperin-Lea, Abhinav Anand, Matthias Degroote, Hermanni Heimonen, Jakob S. Kottmann, Tim Menke, Wai-Keong Mok, Sukin Sim, Leong-Chuan Kwek, Alán Aspuru-Guzik, arXiv:2101.08448 [quant-ph] (2021).
  • “Meta-Variational Quantum Eigensolver (Meta-VQE): Learning energy profiles of parameterized Hamiltonians for quantum simulation”, Alba Cervera-Lierta, Jakob S. Kottmann, Alán Aspuru-Guzik PRX Quantum 2, 020329 (2021), arXiv:2009.13545 [quant-ph]
  • “Data re-uploading for a universal quantum classifier”, Adrián Pérez-Salinas, Alba Cervera-Lierta, Elies Gil-Fuster, and José I. Latorre, Quantum 4, 226 (2020).
Alba Cervera-Lierta
Alba Cervera-Lierta
Senior Researcher

Quantum Computing scientist.