Centrala begrepp
Quantum machine learning, which involves running machine learning algorithms on quantum devices, has significant potential but faces challenges in the current Noisy Intermediate-Scale Quantum (NISQ) era. This review provides a comprehensive overview of the various concepts and techniques that have emerged in the field, including Variational Quantum Algorithms (VQA), Quantum Neural Tangent Kernel (QNTK), and the issue of barren plateaus. It also explores the potential of Fault-Tolerant Quantum Computation (FTQC) algorithms and their applications in quantum machine learning.
Sammanfattning
This comprehensive review covers the current state of quantum machine learning, focusing on developments in both the Noisy Intermediate-Scale Quantum (NISQ) era and the future Fault-Tolerant Quantum Computation (FTQC) era.
In the NISQ era, the review delves into Variational Quantum Algorithms (VQA), which are a central framework. VQA comprises four key elements: the objective function, parameterized quantum circuits (PQC), measurement strategies, and classical optimization techniques. The review also discusses the Quantum Neural Tangent Kernel (QNTK), which provides a theoretical foundation for quantum neural networks and an understanding of stochastic gradient descent dynamics. Additionally, the issue of barren plateaus, where the loss landscape becomes exponentially flat, is explored through the lens of quantum landscape theory.
Moving to the FTQC era, the review introduces several quantum algorithms with the potential for exponential speedup, such as Quantum Phase Estimation (QPE), Quantum Principal Component Analysis (QPCA), and the Harrow-Hassidim-Lloyd (HHL) algorithm. It also discusses the potential of these algorithms in the context of large-scale machine learning models.
The review also covers topics that amalgamate quantum principles with statistical learning theory, including shadow tomography, the classical shadow formalism, and the application of Quantum Machine Learning (QML) in the study of quantum data and quantum simulators.
Overall, this review provides a comprehensive and unbiased overview of the current state of quantum machine learning, highlighting both the challenges and the promising future directions in this rapidly evolving field.
Statistik
Quantum computers are susceptible to background noise, which imposes limitations on our ability to construct quantum computers with sufficient depth for executing tasks demanding fast and precise computations.
Current quantum computers can only handle on the order of around 100 qubits, and they all exhibit noise, making it challenging to derive tangible benefits for our daily lives.
Quantum error correction (QEC) codes are a solution to this predicament, providing a protective buffer zone against information loss in noisy environments.
Classical machine learning doesn't inherently reject noise, and the widely recognized stochastic gradient descent algorithm can even benefit from noise, suggesting that running certain machine learning algorithms on current (NISQ) quantum devices could have some significance.
Citat
"Quantum machine learning represents a highly promising realm in contemporary physics and computer science research, with far-reaching implications spanning quantum chemistry [108], artificial intelligence [89], and even high-energy physics [7]."
"Quantum computers are susceptible to background noise, which imposes limitations on our ability to construct quantum computers with sufficient depth for executing tasks demanding fast and precise computations."
"Quantum error correction (QEC) codes provide a protective buffer zone against information loss in noisy environments."
"Classical machine learning doesn't inherently reject noise, and the widely recognized stochastic gradient descent algorithm can even benefit from noise, suggesting that running certain machine learning algorithms on current (NISQ) quantum devices could have some significance."