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Efficient quantum-enhanced classical simulation for patches of quantum landscapes
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Understanding the capabilities of classical simulation methods is key to identifying where quantum computers are advantageous. Not only does this ensure that quantum computers are used only where necessary, but also one can potentially identify subroutines that can be offloaded onto a classical device. In this work, we show that it is always possible to generate a classical surrogate of a sub-region (dubbed a "patch") of an expectation landscape produced by a parameterized quantum circuit. That is, we provide a quantum-enhanced classical algorithm which, after simple measurements on a quantum device, allows one to classically simulate approximate expectation values of a subregion of a landscape. We provide time and sample complexity guarantees for a range of families of circuits of interest, and further numerically demonstrate our simulation algorithms on an exactly verifiable simulation of a Hamiltonian variational ansatz and long-time dynamics simulation on a 127-qubit heavy-hex topology.
Forward citations
Cited by 5 Pith papers
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Quantum Complexity and Chaos in Many-Qudit Doped Clifford Circuits
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Demonstration of Efficient Predictive Surrogates for Large-scale Quantum Processors
Classical surrogates using truncated trigonometric expansions emulate noisy quantum processors and cut measurement overhead in VQE pre-training and Floquet phase identification.
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Pauli Propagation: A Computational Framework for Simulating Quantum Systems
Pauli propagation, a classical method that evolves Pauli operators through quantum circuits, is presented as a unified algorithmic framework together with the Julia package PauliPropagation.jl that implements it.
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Hybrid Quantum Neural Networks: Theory, Implementations, and Applications
A balanced review of hybrid quantum neural networks, concluding that quantum layers help on structured, small-scale and quantum-native problems but do not yet beat classical models on generic benchmarks.
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