Second Quantum Revolution: Sensing, Computing, and Transmitting

A special issue of Electronics (ISSN 2079-9292).

Deadline for manuscript submissions: 15 February 2026 | Viewed by 458

Special Issue Editors


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Guest Editor
Dipartimento di Ingegneria Enzo Ferrari, University of Modena and Reggio Emilia, 41125 Modena, Italy
Interests: RFIC; quantum gates; low frequency noise; analog electronics
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Guest Editor
CEA-Leti, Université Grenoble-Alpes, F-38054 Grenoble, France
Interests: CryoCMOS for quantum computing; analog computing; ultra-low power circuits for edge-AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Old Quantum Physics introduced concepts such as the quantum of action and the photon. The First Quantum Revolution saw the birth of the Quantum Mechanics that paved the way to the transistor, the laser, and the nuclear energy exploitation—inventions that deeply changed our society. The Second Quantum Revolution, currently underway, promises extremely sensitive sensors (quantum sensors), increasingly secure transmissions (quantum information), and unprecedented computing capabilities (quantum computing). The technological impact appears to be extraordinary in several fields, spanning from molecular engineering to cryptography, financial modeling, and environmental sustainability.

To improve the state of the current literature, which appears rather divided into specific sectors, the aim of this Special Issue is to collect theoretical, experimental, and technological contributions exploring the Second Quantum Revolution, with the purpose of depicting multi-disciplinary scenarios of the expected future of quantum technologies. Both original and review articles are welcome for submission. This Special Issue is focused on, but not limited to, the following topics:

  • Physical implementation of qubits and quantum gates;
  • Cryogenic microelectronics for qubit control and readout;
  • Quantum algorithms;
  • Quantum antennas;
  • Quantum optics;
  • Quantum clock ;
  • Quantum data processing and quantum radar;
  • Quantum sensing and imaging;
  • Quantum cryptography and cybersecurity;
  • Quantum internet;
  • Quantum information theory;
  • Quantum neural network .

We look forward to receiving your contributions.

Dr. Mattia Borgarino
Dr. Franck Badets
Guest Editors

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Keywords

  • qubit physics
  • CryoCMOS
  • quantum gates
  • tomography
  • quantum CPU
  • entanglement
  • fidelity
  • quantum AI

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Published Papers (1 paper)

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Research

23 pages, 3777 KB  
Article
Quantum Down-Sampling Filter for Variational Autoencoder
by Farina Riaz, Fakhar Zaman, Hajime Suzuki, Alsharif Abuadbba and David Nguyen
Electronics 2025, 14(23), 4626; https://doi.org/10.3390/electronics14234626 - 25 Nov 2025
Viewed by 246
Abstract
Variational Autoencoders (VAEs) are fundamental for generative modeling and image reconstruction, yet their performance often struggles to maintain high fidelity in reconstructions. This study introduces a hybrid model, Quantum Variational Autoencoder (Q-VAE), which integrates quantum encoding within the encoder while utilizing fully connected [...] Read more.
Variational Autoencoders (VAEs) are fundamental for generative modeling and image reconstruction, yet their performance often struggles to maintain high fidelity in reconstructions. This study introduces a hybrid model, Quantum Variational Autoencoder (Q-VAE), which integrates quantum encoding within the encoder while utilizing fully connected layers to extract meaningful representations. The decoder uses transposed convolution layers for up-sampling. The Q-VAE is evaluated against the classical VAE and the classical direct-passing VAE, which utilizes windowed pooling filters. Results on the MNIST and USPS datasets demonstrate that Q-VAE consistently outperforms classical approaches, achieving lower Fréchet Inception Distance scores, thereby indicating superior image fidelity and enhanced reconstruction quality. These findings highlight the potential of Q-VAE for high-quality synthetic data generation and improved image reconstruction in generative models. Full article
(This article belongs to the Special Issue Second Quantum Revolution: Sensing, Computing, and Transmitting)
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