Noisy intermediate-scale quantum (NISQ) algorithms

By Kishor Bharti, Alba Cervera-Lierta, T. Kyaw, T. Haug, Sumner Alperin-Lea, A. Anand, M. Degroote, H. Heimonen, Jakob S. Kottmann, Tim Menke and 4 more. Reviews of Modern Physics, 2021

535 citations recorded by the source corpus.

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 review, we provide a thorough summary of NISQ computational paradigms and algorithms. We discuss the key structure of these algorithms, their limitations, and advantages. We additionally provide a comprehensive overview of various benchmarking and software tools useful for programming and testing NISQ devices.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/b1d13bb891f1e7bb459bbe5a1e83f884834fbdf8

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Citations in the corpus

Noise-induced barren plateaus in variational quantum algorithms (2020, background): “(4), and NO 1⁄4 jωj is the number of non-zero elements in the Pauli decomposition of O.”
Quantum convolutional neural network for classical data classification (2021, methodology): “[26] Kishor Bharti, Alba Cervera-Lierta, Thi Ha Kyaw,”
Absence of Barren Plateaus in Quantum Convolutional Neural Networks (2020, background): “[26] Kishor Bharti, Alba Cervera-Lierta, Thi Ha Kyaw, Tobias Haug, Sumner Alperin-Lea, Abhinav Anand, Matthias Degroote, Hermanni Heimonen, Jakob S Kottmann, Tim Menke, et al.”
Error mitigation with Clifford quantum-circuit data (2020, methodology): “It also includes variational quantum algorithms [3, 10, 18, 34, 40], some of which can be used to remove the effects of incoherent noise [9, 29–31, 42].”