Exploring the Horizon of Practical Utility in Near-Term Quantum Computing
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Quantum Information".
Deadline for manuscript submissions: 30 December 2024 | Viewed by 2685
Special Issue Editors
Interests: quantum machine learning; quantum computation; ion trap quantum computers; variational algorithms; quantum software architecture
Interests: NISQ algorithms; quantum machine learning; quantum circuit dynamics; quantum state preparation; tensor network methods; ion trap quantum computing; strongly correlated electrons
Special Issue Information
Dear Colleagues,
Over the past few decades, the field of quantum computing and quantum information processing has witnessed significant strides in fundamental theory and hardware development. Diverse quantum computing architectures—ranging from photonic and superconducting circuits to trapped-ion configurations— have transitioned from small-scale prototypes to sophisticated devices housing tens, and even hundreds, of interconnected qubits. These advancements have not only facilitated groundbreaking showcases of quantum computational advantages in scientific experiments like boson sampling and cross-entropy benchmarks, but have also spurred the exploration of quantum applications poised to provide practical value in near-term quantum devices.
Presently there is a huge amount of activity examining numerous pathways to achieving this overarching objective. Much of the effort has focused on variational algorithms which distribute the computational load onto classical resources, thereby alleviating the demands on quantum hardware. Two popular examples of these hybrid quantum algorithms are the quantum approximate optimization algorithms (QAOA) and variational quantum eigensolvers (VQE). On top of this, the design and application of error mitigation techniques have demonstrated an impressive ability to extract useful information from noisy outputs, thereby enhancing the potential usefulness of near-term devices. On the other hand, the exploration of ‘no-go’ theorems and classical methods for emulating quantum processes offer invaluable perspectives for calibrating our expectations of future quantum algorithms.
This Special Issue focuses on the roadmap, as well as the boundary, of the pursuit of applications with practical advantages on near-term quantum computers. Papers and review articles are invited to address the following topics:
- Algorithms/applications that are suitable for near-term devices include variational algorithms and quantum machine learning;
- Error mitigation techniques;
- Optimization techniques for variational quantum algorithms;
- Classical simulation of quantum algorithms;
- Hardware-inspired design of algorithm/applications;
- Limitations of quantum algorithms for solving classical problems;
- Limitations of near-term quantum devices.
Dr. Daiwei Zhu
Dr. Jason Iaconis
Dr. Torin F. Stetina
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.
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Keywords
- quantum error mitigation
- quantum machine learning
- near-term quantum computers
- quantum algorithms
- variational algorithms
- heuristic algorithms
- practical quantum advantages
- quantum-inspired algorithms
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