Toward Neural Network Simulation of Variational Quantum Algorithms
- URL: http://arxiv.org/abs/2211.02929v1
- Date: Sat, 5 Nov 2022 15:46:47 GMT
- Title: Toward Neural Network Simulation of Variational Quantum Algorithms
- Authors: Oliver Knitter, James Stokes, Shravan Veerapaneni
- Abstract summary: Variational quantum algorithms (VQAs) utilize a hybrid quantum-classical architecture to recast problems of high-dimensional linear algebra as ones of optimization.
We ask if classical optimization algorithms can be constructed paralleling other VQAs, focusing on the example of the variational quantum linear solver (VQLS)
We find that such a construction can be applied to the VQLS, yielding a paradigm that could theoretically extend to other VQAs of similar form.
- Score: 1.9723551683930771
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Variational quantum algorithms (VQAs) utilize a hybrid quantum-classical
architecture to recast problems of high-dimensional linear algebra as ones of
stochastic optimization. Despite the promise of leveraging near- to
intermediate-term quantum resources to accelerate this task, the computational
advantage of VQAs over wholly classical algorithms has not been firmly
established. For instance, while the variational quantum eigensolver (VQE) has
been developed to approximate low-lying eigenmodes of high-dimensional sparse
linear operators, analogous classical optimization algorithms exist in the
variational Monte Carlo (VMC) literature, utilizing neural networks in place of
quantum circuits to represent quantum states. In this paper we ask if classical
stochastic optimization algorithms can be constructed paralleling other VQAs,
focusing on the example of the variational quantum linear solver (VQLS). We
find that such a construction can be applied to the VQLS, yielding a paradigm
that could theoretically extend to other VQAs of similar form.
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