Efficient Gradient Estimation for Parameterized Quantum Systems with Lie Algebraic Symmetries
arXiv:2404.05108v3 Announce Type: replace-cross Abstract: Gradient estimation is a central challenge in training parameterized quantum circuits (PQCs) for hybrid quantum-classical optimization and learning problems. This difficulty arises from several factors, including the exponential dimensionality of the Hilbert spaces and the information loss in quantum measurements. Existing estimators, such as finite difference and the parameter shift rule, often fail to adequately address these challenges for certain classes of PQCs. In this work, we propose a novel gradient estimation framework that le
The continuous drive to improve the efficiency and scalability of quantum computing algorithms necessitates breakthroughs in fundamental techniques like gradient estimation for parameterized quantum circuits.
Efficient gradient estimation is critical for advancing quantum machine learning and optimization, which are core to developing more powerful and practical quantum applications.
This novel framework could significantly reduce the computational overhead and improve the accuracy of training parameterized quantum systems, enabling more complex quantum algorithms.
- · Quantum computing researchers
- · Quantum hardware developers
- · Companies investing in quantum AI
- · High-performance computing sector
- · Classical optimization methods (in specific quantum contexts)
Improved training of quantum machine learning models and quantum optimization algorithms.
Accelerated development and commercialization of quantum-enhanced applications across various industries.
Potential for quantum computers to achieve practical advantages over classical supercomputers in specific, complex problem domains sooner.
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Read at arXiv cs.LG