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Materials Opportunities and Challenges for Quantum Information Processors

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Quantum Materials".

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 11848

Special Issue Editor

Institute for Quantum Computing,Waterloo Institute for Nanotechnology,University of Waterloo,Waterloo, ON, Canada
Interests: solid-state quantum simulators; semiconductor cavity quantum electrodynamics; quantum electronics and optics; quantum nanoelectronics and nanophotonics; carbon nanotubes; microcavity exciton-polaritons; 2D materials; sensor and antenna technology

Special Issue Information

Dear Colleagues,

Our modern lifestyle at the heart of the Information Age demands unprecedentedly powerful computation and communication technologies to handle torrents of information and data flooding, to address complex resourceful tasks and problems efficiently, and to make optimal decisions for innovation-based winning societies with an emphasis on harnessing information and communication technologies. General-purpose quantum processors are one of the most advanced smart machines to not only expand human knowledge boundaries but also to tackle intractable problems, such as optimization problems and sophisticated computational modelling that are not possible using the currently available classical computers. The global race to harness enabling technologies for building powerful quantum processors has begun, and several commercial quantum computers and their cloud services are open to the public. Despite this remarkable progress, the technical challenges toward large-scale fault-tolerant quantum processors are still prevalent. In particular, we admit the essential need to search for novel and innovative materials that overcome the challenges.

This Special Issue is a venue to collect the exciting ongoing research activities based on various material platforms as a robust quantum information architecture. We also invite materials science research works that can feature as next-generation quantum processors.

Prof. Dr. Na Young Kim
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • semiconductors and defects for quantum processors
  • superconductors for quantum processors
  • topological materials for fault-tolerant quantum processors
  • 2D materials for quantum information processing
  • synthetic materials for quantum processors
  • nanomaterials for quantum electronics and photonics
  • materials for quantum architectures

Published Papers (4 papers)

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Research

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26 pages, 3004 KiB  
Article
Quantum Graph Neural Network Models for Materials Search
by Ju-Young Ryu, Eyuel Elala and June-Koo Kevin Rhee
Materials 2023, 16(12), 4300; https://doi.org/10.3390/ma16124300 - 10 Jun 2023
Cited by 1 | Viewed by 2066
Abstract
Inspired by classical graph neural networks, we discuss a novel quantum graph neural network (QGNN) model to predict the chemical and physical properties of molecules and materials. QGNNs were investigated to predict the energy gap between the highest occupied and lowest unoccupied molecular [...] Read more.
Inspired by classical graph neural networks, we discuss a novel quantum graph neural network (QGNN) model to predict the chemical and physical properties of molecules and materials. QGNNs were investigated to predict the energy gap between the highest occupied and lowest unoccupied molecular orbitals of small organic molecules. The models utilize the equivariantly diagonalizable unitary quantum graph circuit (EDU-QGC) framework to allow discrete link features and minimize quantum circuit embedding. The results show QGNNs can achieve lower test loss compared to classical models if a similar number of trainable variables are used, and converge faster in training. This paper also provides a review of classical graph neural network models for materials research and various QGNNs. Full article
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14 pages, 3042 KiB  
Article
Detecting Single Microwave Photons with NV Centers in Diamond
by Olivia Woodman, Abdolreza Pasharavesh, Christopher Wilson and Michal Bajcsy
Materials 2023, 16(8), 3274; https://doi.org/10.3390/ma16083274 - 21 Apr 2023
Viewed by 1965
Abstract
We propose a scheme for detecting single microwave photons using dipole-induced transparency (DIT) in an optical cavity resonantly coupled to a spin-selective transition of a negatively charged nitrogen-vacancy (NV) defect in diamond crystal lattices. In this scheme, the microwave photons control [...] Read more.
We propose a scheme for detecting single microwave photons using dipole-induced transparency (DIT) in an optical cavity resonantly coupled to a spin-selective transition of a negatively charged nitrogen-vacancy (NV) defect in diamond crystal lattices. In this scheme, the microwave photons control the interaction of the optical cavity with the NV center by addressing the spin state of the defect. The spin, in turn, is measured with high fidelity by counting the number of reflected photons when the cavity is probed by resonant laser light. To evaluate the performance of the proposed scheme, we derive the governing master equation and solve it through both direct integration and the Monte Carlo approach. Using these numerical simulations, we then investigate the effects of different parameters on the detection performance and find their corresponding optimized values. Our results indicate that detection efficiencies approaching 90% and fidelities exceeding 90% could be achieved when using realistic optical and microwave cavity parameters. Full article
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Review

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28 pages, 18599 KiB  
Review
Material-Inherent Noise Sources in Quantum Information Architecture
by HeeBong Yang and Na Young Kim
Materials 2023, 16(7), 2561; https://doi.org/10.3390/ma16072561 - 23 Mar 2023
Cited by 1 | Viewed by 1889
Abstract
NISQ is a representative keyword at present as an acronym for “noisy intermediate-scale quantum”, which identifies the current era of quantum information processing (QIP) technologies. QIP science and technologies aim to accomplish unprecedented performance in computation, communications, simulations, and sensing by exploiting the [...] Read more.
NISQ is a representative keyword at present as an acronym for “noisy intermediate-scale quantum”, which identifies the current era of quantum information processing (QIP) technologies. QIP science and technologies aim to accomplish unprecedented performance in computation, communications, simulations, and sensing by exploiting the infinite capacity of parallelism, coherence, and entanglement as governing quantum mechanical principles. For the last several decades, quantum computing has reached to the technology readiness level 5, where components are integrated to build mid-sized commercial products. While this is a celebrated and triumphant achievement, we are still a great distance away from quantum-superior, fault-tolerant architecture. To reach this goal, we need to harness technologies that recognize undesirable factors to lower fidelity and induce errors from various sources of noise with controllable correction capabilities. This review surveys noisy processes arising from materials upon which several quantum architectures have been constructed, and it summarizes leading research activities in searching for origins of noise and noise reduction methods to build advanced, large-scale quantum technologies in the near future. Full article
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26 pages, 14735 KiB  
Review
Carbon Nanotube Devices for Quantum Technology
by Andrey Baydin, Fuyang Tay, Jichao Fan, Manukumara Manjappa, Weilu Gao and Junichiro Kono
Materials 2022, 15(4), 1535; https://doi.org/10.3390/ma15041535 - 18 Feb 2022
Cited by 22 | Viewed by 5244
Abstract
Carbon nanotubes, quintessentially one-dimensional quantum objects, possess a variety of electrical, optical, and mechanical properties that are suited for developing devices that operate on quantum mechanical principles. The states of one-dimensional electrons, excitons, and phonons in carbon nanotubes with exceptionally large quantization energies [...] Read more.
Carbon nanotubes, quintessentially one-dimensional quantum objects, possess a variety of electrical, optical, and mechanical properties that are suited for developing devices that operate on quantum mechanical principles. The states of one-dimensional electrons, excitons, and phonons in carbon nanotubes with exceptionally large quantization energies are promising for high-operating-temperature quantum devices. Here, we discuss recent progress in the development of carbon-nanotube-based devices for quantum technology, i.e., quantum mechanical strategies for revolutionizing computation, sensing, and communication. We cover fundamental properties of carbon nanotubes, their growth and purification methods, and methodologies for assembling them into architectures of ordered nanotubes that manifest macroscopic quantum properties. Most importantly, recent developments and proposals for quantum information processing devices based on individual and assembled nanotubes are reviewed. Full article
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