Machine Learning techniques for state recognition and auto-tuning in quantum dotsQuantum PhysicsRecent progress in building large-scale quantum devices for exploring quantum computing and simulation paradigms has relied upon effective tools for achieving and maintaining good experimental parameters, i.e. ...
Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers...
3.3 Quantum machine learning The vast amounts of data anticipated to be processed in 5G and 6G use cases will require computational power and computation time capabilities that are challenging to achieve in current systems. But traditional machine learning techniques take a long time as data volumes...
Machine learning techniques use mathematical algorithms and tools to search for patterns in data. These techniques have become powerful tools for many different applications, which can range from biomedical uses such as in cancer reconnaissance, in genetics and genomics, in autism monitoring and diagnosi...
AUTOMATED DETECTION OF PARKINSON'S DISEASE BASED ON HYBRID CNN AND QUANTUM MACHINE LEARNING TECHNIQUES IN MRI IMAGES Parkinson's disease (PD) is a long-term neurological condition that causes severe neuronal degeneration in the motor cortex. Because of the constraints of ... RK Ahalya,GF Nkondo,...
More recently, quantum machine learning techniques such as annealing have shownbusiness promiseby optimising the yields of financial assets or the calculation of credit ratings. Quantum techniques in machine learning are also likely to become important inmedical technologyor drug design as the principles...
Truncation/compression techniques can help us to reduce the dimension of Hilbert space, thereby reduce the calculation time, e.g. truncate a size of exponential (2^N) to the polynomial (N). E.g. For two spin-1 particles, there are 9 energy levels. However, we can only focus on the ...
"This is an exciting time to combine machine learning withquantum computing," Kais said. "Impressive progress has been made recently in building quantum computers, andquantummachine learningtechniques will become powerful tools for finding new patterns in big data." ...
Synergizing quantum techniques with machine learning for advancing drug discovery challenge Article Open access 28 December 2024 QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules Article Open access 02 February 2021 Exploring chemical...
This section gives an overview over our techniques. First, we outline the proof strategy that leads to the different generalization bounds stated above. Second, we present more details about our numerical investigations. Analytical methods An established approach to generalization bounds in classical stat...