Toward a resource-optimized dynamic quantum algorithm via non-iterative auxiliary subspace corrections

Recent quantum algorithms pertaining to electronic structure theory primarily focus on the threshold-based dynamic construction of ansatz by selectively including important many-body operators. These methods can be made systematically more accurate by tuning the threshold to include a greater number of operators into the ansatz. However, such improvements come at the cost of rapid proliferation of the circuit depth, especially for highly correlated molecular systems. In this work, we address this issue by the development of a novel theoretical framework that relies on the segregation of an ansatz into a dynamically selected core "principal" component, which is, by construction, adiabatically decoupled from the remaining operators. This enables us to perform computations involving the principal component using extremely shallow-depth circuits, whereas the effect of the remaining "auxiliary" component is folded into the energy function via a cost-efficient non-iterative correction, ensuring the requisite accuracy. We propose a formalism that analytically predicts the auxiliary parameters from the principal ones, followed by a suite of non-iterative auxiliary subspace correction techniques with different levels of sophistication. The auxiliary subspace corrections incur no additional quantum resources yet complement an inadequately expressive core of the ansatz to recover a significant amount of electronic correlations. We have numerically validated the resource efficiency and accuracy of our formalism with a number of strongly correlated molecular systems.

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Publication Details

Journal
The Journal of Chemical Physics
Published
2024-10-14
DOI
https://doi.org/10.1063/5.0229137
Citations
6
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
2.08

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article

Toward a resource-optimized dynamic quantum algorithm via non-iterative auxiliary subspace corrections

Chayan Patra, Sonaldeep Halder, Rahul Maitra, Dibyendu Mondal et al.
6 citations
The Journal of Chemical Physics
Quantum Computing Algorithms and Architecture
2.08
article

Toward a resource-optimized dynamic quantum algorithm via non-iterative auxiliary subspace corrections

Chayan Patra, Sonaldeep Halder, Rahul Maitra, Dibyendu Mondal, Debaarjun Mukherjee
article en
6 citations

Abstract

Recent quantum algorithms pertaining to electronic structure theory primarily focus on the threshold-based dynamic construction of ansatz by selectively including important many-body operators. These methods can be made systematically more accurate by tuning the threshold to include a greater number of operators into the ansatz. However, such improvements come at the cost of rapid proliferation of the circuit depth, especially for highly correlated molecular systems. In this work, we address this issue by the development of a novel theoretical framework that relies on the segregation of an ansatz into a dynamically selected core "principal" component, which is, by construction, adiabatically decoupled from the remaining operators. This enables us to perform computations involving the principal component using extremely shallow-depth circuits, whereas the effect of the remaining "auxiliary" component is folded into the energy function via a cost-efficient non-iterative correction, ensuring the requisite accuracy. We propose a formalism that analytically predicts the auxiliary parameters from the principal ones, followed by a suite of non-iterative auxiliary subspace correction techniques with different levels of sophistication. The auxiliary subspace corrections incur no additional quantum resources yet complement an inadequately expressive core of the ansatz to recover a significant amount of electronic correlations. We have numerically validated the resource efficiency and accuracy of our formalism with a number of strongly correlated molecular systems.

The Journal of Chemical PhysicsVol. 161(14)
Indian Institute of Technology Bombay (IN)
Council of Scientific and Industrial Research, India, University Grants Commission, Science and Engineering Research Board, Industrial Research and Consultancy Centre
Decent work and economic growth
Openalex Percentile: Top 11%
Quantum Computing Algorithms and Architecture
2.08
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