Efficient quantum-enhanced classical simulation for patches of quantum landscapes
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910720851968000 |
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| author | Lerch, Sacha Puig, Ricard Rudolph, Manuel S. Angrisani, Armando Jones, Tyson Cerezo, M. Thanasilp, Supanut Holmes, Zoë |
| author_facet | Lerch, Sacha Puig, Ricard Rudolph, Manuel S. Angrisani, Armando Jones, Tyson Cerezo, M. Thanasilp, Supanut Holmes, Zoë |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_19896 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient quantum-enhanced classical simulation for patches of quantum landscapes Lerch, Sacha Puig, Ricard Rudolph, Manuel S. Angrisani, Armando Jones, Tyson Cerezo, M. Thanasilp, Supanut Holmes, Zoë Quantum Physics Machine Learning 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. |
| title | Efficient quantum-enhanced classical simulation for patches of quantum landscapes |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2411.19896 |