Quantum Computing in the NISQ era and beyond

John Preskill

Institute for Quantum Information and Matter and Walter Burke Institute for Theoretical Physics, California Institute of Technology, Pasadena CA 91125, USA

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Abstract

Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future. Quantum computers with 50-100 qubits may be able to perform tasks which surpass the capabilities of today's classical digital computers, but noise in quantum gates will limit the size of quantum circuits that can be executed reliably. NISQ devices will be useful tools for exploring many-body quantum physics, and may have other useful applications, but the 100-qubit quantum computer will not change the world right away - we should regard it as a significant step toward the more powerful quantum technologies of the future. Quantum technologists should continue to strive for more accurate quantum gates and, eventually, fully fault-tolerant quantum computing.

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[209] Weijie Du, James P. Vary, Xingbo Zhao, and Wei Zuo, "Ab initio nuclear structure via quantum adiabatic algorithm", arXiv:2105.08910.

[210] Tanvi P. Gujarati, Tyler Takeshita, Andreas Hintennach, and Eunseok Lee, "A Heuristic Quantum-Classical Algorithm for Modeling Substitutionally Disordered Binary Crystalline Materials", arXiv:2004.00957.

[211] Thomas Häner and Mathias Soeken, "Lowering the T-depth of Quantum Circuits By Reducing the Multiplicative Depth Of Logic Networks", arXiv:2006.03845.

[212] Minsung Kim, Davide Venturelli, and Kyle Jamieson, "Towards Hybrid Classical-Quantum Computation Structures in Wirelessly-Networked Systems", arXiv:2010.00682.

[213] Poulami Das, Swamit Tannu, and Moinuddin Qureshi, "JigSaw: Boosting Fidelity of NISQ Programs via Measurement Subsetting", arXiv:2109.05314.

[214] Casey Duckering, Jonathan M. Baker, Andrew Litteken, and Frederic T. Chong, "Orchestrated Trios: Compiling for Efficient Communication in Quantum Programs with 3-Qubit Gates", arXiv:2102.08451.

[215] Jelmer J. Renema, "Simulability of Imperfect Gaussian and Superposition Boson Sampling", arXiv:1911.10112.

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[220] Leigh S. Martin, "Quantum feedback for measurement and control", arXiv:2004.09766.

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[222] Youle Wang, Benchi Zhao, and Xin Wang, "Quantum algorithms for estimating quantum entropies", arXiv:2203.02386.

[223] Bill Poirier and Jonathan Jerke, "Full-dimensional Schrödinger wavefunction calculations using tensors and quantum computers: the Cartesian component-separated approach", Physical Chemistry Chemical Physics (Incorporating Faraday Transactions) 24 7, 4437 (2022).

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[225] Haozhen Situ and Zhimin He, "Machine learning distributions of quantum ansatz with hierarchical structure", International Journal of Modern Physics B 34 20, 2050196-3746 (2020).

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[227] David W. Kribs, Ningping Cao, Chi-Kwong Li, Yiu-Tung Poon, Bei Zeng, and Mike Nelson, "Higher Rank Matricial Ranges and Hybrid Quantum Error Correction", arXiv:1911.12744.

[228] Yan Zhu, Ge Bai, Yuexuan Wang, Tongyang Li, and Giulio Chiribella, "Quantum autoencoders for communication-efficient quantum cloud computing", arXiv:2112.12369.

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The above citations are from SAO/NASA ADS (last updated successfully 2022-05-17 03:12:17). The list may be incomplete as not all publishers provide suitable and complete citation data.

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