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Quantum Machine Learning: Foundational Principles to Practical Applications

IEEE Access
Quantum Machine Learning (QML) has emerged as a promising interdisciplinary field that combines quantum computing with machine learning to address complex computational problems.
Ashis Kumar Pati, Rajesh Vayyala, Kuldip Mohanty, Uttamita Das   +3 more
doaj   +1 more source

From the Discovery of the Giant Magnetocaloric Effect to the Development of High‐Power‐Density Systems

Advanced Materials Technologies, EarlyView.
The article overviews past and current efforts on caloric materials and systems, highlighting the contributions of Ames National Laboratory to the field. Solid‐state caloric heat pumping is an innovative method that can be implemented in a wide range of cooling and heating applications.
Agata Czernuszewicz, Yaroslav Mudryk, Jun Cui, Lucas Griffith, Duane D. Johnson, Julie Slaughter   +5 more
wiley   +1 more source

VQE-generated quantum circuit dataset for machine learning

Physical Review Research
Quantum machine learning has the potential to computationally outperform classical machine learning, but it is not yet clear whether it will actually be valuable for practical problems.
Akimoto Nakayama, Kosuke Mitarai, Leonardo Placidi, Takanori Sugimoto, Keisuke Fujii   +4 more
doaj   +1 more source

Machine learning by unitary tensor network of hierarchical tree structure

New Journal of Physics, 2019
The resemblance between the methods used in quantum-many body physics and in machine learning has drawn considerable attention. In particular, tensor networks (TNs) and deep learning architectures bear striking similarities to the extent that TNs can be ...
Ding Liu, Shi-Ju Ran, Peter Wittek, Cheng Peng, Raul Blázquez García, Gang Su, Maciej Lewenstein   +6 more
doaj   +1 more source

Leveraging Mechanical Resonances for the Selection of Promising Materials in Complex Phase Spaces

Advanced Materials Technologies, EarlyView.
A central challenge in materials science is the design of new functional materials with enhanced performance for targeted applications. This paper demonstrates that “listening” to materials through measuring their mechanical resonances is a rich, and largely untapped, platform for the rapid identification of promising novel compounds and a powerful ...
Christopher A. Mizzi, Osman El‐Atwani, Tannor T. J. Munroe, Saryu Fensin, Boris Maiorov   +4 more
wiley   +1 more source

Toward structure-preserving quantum encodings

Physical Review Research
Harnessing the potential computational advantage of quantum computers for machine learning tasks relies on the uploading of classical data onto quantum computers through what are commonly referred to as quantum encodings. The choice of such encodings may
Arthur J. Parzygnat, Tai-Danae Bradley, Andrew Vlasic, Anh Pham   +3 more
doaj   +1 more source

Flexible Sensors for Robotics Tactile Perception: A Review

Advanced Robotics Research, EarlyView.
Flexible tactile sensing for robotics is reviewed through four interconnected dimensions. Physical mechanisms include piezoresistive, capacitive, piezoelectric, triboelectric, iontronic, and optical sensing. Structural design includes bioinspired, defect‐based, and MEMS‐based tactile systems.
Yu Song, Ying Chen, Yihao Chen, Xue Feng
wiley   +1 more source

Reinforcement learning-based architecture search for quantum machine learning

Quantum machine learning (QML) models use encoding circuits to map data into a quantum Hilbert space. While it is well known that the architecture of these circuits significantly influences core properties of the resulting model, they are often chosen ...
Rapp, Frederic, Huber, Marco, Roth, Marco, Kreplin, David, Kreplin, David A, Huber, Marco F   +5 more
core   +1 more source

Solid Harmonic Wavelet Bispectrum for Image Analysis

Advanced Science, EarlyView.
The Solid Harmonic Wavelet Bispectrum (SHWB), a rotation‐ and translation‐invariant descriptor that captures higher‐order (phase) correlations in signals, is introduced. Combining wavelet scattering, bispectral analysis, and group theory, SHWB achieves interpretable, data‐efficient representations and demonstrates competitive performance across texture,
Alex Brown, Mathilda Avirett‐Mackenzie, Carolin Villforth, Georgios Exarchakis   +3 more
wiley   +1 more source

Improving 2–5 Qubit Quantum Phase Estimation Circuits Using Machine Learning [PDF]

Quantum computing has the potential to solve problems that are currently intractable to classical computers with algorithms like Quantum Phase Estimation (QPE); however, noise significantly hinders the performance of today’s quantum computers.
Woodrum, Charles, Weeks, David E., Charles Woodrum, Wagner, Torrey J., Torrey Wagner, David Weeks   +5 more
core   +1 more source