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Optimal Quantum Circuit Design via Unitary Neural Networks
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Optimal Quantum Circuit Design via Unitary Neural Networks
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The process of translating a quantum algorithm into a form suitable for implementation on a quantum computing platform is crucial but yet challenging. This entails specifying quantum operations with precision, a typically intricate task. In this paper, we present an alternative approach: an automated method for synthesizing the functionality of a quantum algorithm into a quantum circuit model representation. Our methodology involves training a neural network model using diverse input-output mappings of the quantum algorithm. We demonstrate that this trained model can effectively generate a quantum circuit model equivalent to the original algorithm. Remarkably, our observations indicate that the trained model achieves near-perfect mapping of unseen inputs to their respective outputs.
Forward citations
Cited by 4 Pith papers
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SoK: Critical Evaluation of Quantum Machine Learning for Adversarial Robustness
A systematization of knowledge paper that empirically evaluates five adversarial attacks on quantum multilayer perceptrons across encodings and depths, identifying an accuracy-robustness trade-off and differences from...
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SoK: Critical Evaluation of Quantum Machine Learning for Adversarial Robustness
The paper delivers the first comprehensive systematization of adversarial robustness in QML with new empirical tests showing an accuracy-robustness trade-off, amplitude encoding's vulnerability, and QML's greater susc...
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From Characterization To Construction: Generative Quantum Circuit Synthesis from Gate Set Tomography Data
A generative QMLC framework tokenizes GST data, embeds it via curriculum-trained set-vision transformers into a context-aware latent space, and uses diffusion models to synthesize circuits conditioned on desired measu...
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Quantum Circuit Design using Complex valued Neural Network in Stiefel Manifold
A single-layer complex-valued neural network constrained to the Stiefel manifold via Cayley updates is used to learn and transpile quantum circuit unitaries, with fidelity reaching 1 on reported toy examples.
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