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Guardians of the Quantum GAN
We obtain watermark extraction accuracy of 100% and ~90% for training the qGAN on individual and multiple quantum hardware setups (and inferencing on different hardware), respectively.
Application of Quantum Tensor Networks for Protein Classification
We show that protein sequences can be thought of as sentences in natural language processing and can be parsed using the existing Quantum Natural Language framework into parameterized quantum circuits of reasonable qubits, which can be trained to solve various protein-related machine-learning problems.
Evaluating Efficacy of Model Stealing Attacks and Defenses on Quantum Neural Networks
In this study, we assess the efficacy of such attacks in the realm of quantum computing.
DyPP: Dynamic Parameter Prediction to Accelerate Convergence of Variational Quantum Algorithms
The exponential run time of quantum simulators on classical machines and long queue times and high costs of real quantum devices present significant challenges in the efficient optimization of Variational Quantum Algorithms (VQAs) like Variational Quantum Eigensolver (VQE), Quantum Approximate Optimization Algorithm (QAOA) and Quantum Neural Networks (QNNs).
