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Learn about a quantum-enhanced classical algorithm that enables efficient simulation of sub-regions within quantum circuit expectation landscapes through this 16-minute conference presentation. Discover how researchers developed a method to generate classical surrogates for "patches" of parameterized quantum circuit landscapes, allowing classical devices to approximate expectation values after simple quantum measurements. Explore the theoretical foundations including time and sample complexity guarantees for various circuit families, and examine numerical demonstrations on Hamiltonian variational ansatz and long-time dynamics simulations using a 127-qubit heavy-hex topology. Understand the implications for identifying optimal quantum-classical hybrid approaches and determining when quantum computers provide genuine advantages over classical methods. Gain insights into this cutting-edge research that bridges quantum computing and classical simulation techniques, presented by an international team of researchers at the Quantum Techniques in Machine Learning conference.
