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Variational Quantum Algorithms
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Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers due to the extremely high computational cost. Quantum computers promise a solution, although fault-tolerant quantum computers will likely not be available in the near future. Current quantum devices have serious constraints, including limited numbers of qubits and noise processes that limit circuit depth. Variational Quantum Algorithms (VQAs), which use a classical optimizer to train a parametrized quantum circuit, have emerged as a leading strategy to address these constraints. VQAs have now been proposed for essentially all applications that researchers have envisioned for quantum computers, and they appear to the best hope for obtaining quantum advantage. Nevertheless, challenges remain including the trainability, accuracy, and efficiency of VQAs. Here we overview the field of VQAs, discuss strategies to overcome their challenges, and highlight the exciting prospects for using them to obtain quantum advantage.
Forward citations
Cited by 4 Pith papers
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Exploiting biased noise in variational quantum models
Twirling amplitude-damping noise into uniform Pauli/depolarising channels reduces expressivity and gradient magnitudes, while preserving the noise bias yields better VQA optimisation in the studied models.
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Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise
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