
Differentiable Learning of Quantum Circuit Born Machine
Quantum circuit Born machines are generative models which represent the ...
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Learning and Inference on Generative Adversarial Quantum Circuits
Quantum mechanics is inherently probabilistic in light of Born's rule. U...
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Quantum Optical Experiments Modeled by Long ShortTerm Memory
We demonstrate how machine learning is able to model experiments in quan...
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Enhancing Generative Models via Quantum Correlations
Generative modeling using samples drawn from the probability distributio...
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DualParameterized Quantum Circuit GAN Model in High Energy Physics
Generative models, and Generative Adversarial Networks (GAN) in particul...
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ACSSq: Algorithmic complexity for short strings via quantum accelerated approach
In this research we present a quantum circuit for estimating algorithmic...
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The Expressive Power of Parameterized Quantum Circuits
Parameterized quantum circuits (PQCs) have been broadly used as a hybrid...
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Learnability and Complexity of Quantum Samples
Given a quantum circuit, a quantum computer can sample the output distribution exponentially faster in the number of bits than classical computers. A similar exponential separation has yet to be established in generative models through quantum sample learning: given samples from an nqubit computation, can we learn the underlying quantum distribution using models with training parameters that scale polynomial in n under a fixed training time? We study four kinds of generative models: Deep Boltzmann machine (DBM), Generative Adversarial Networks (GANs), Long ShortTerm Memory (LSTM) and Autoregressive GAN, on learning quantum data set generated by deep random circuits. We demonstrate the leading performance of LSTM in learning quantum samples, and thus the autoregressive structure present in the underlying quantum distribution from random quantum circuits. Both numerical experiments and a theoretical proof in the case of the DBM show exponentially growing complexity of learningagent parameters required for achieving a fixed accuracy as n increases. Finally, we establish a connection between learnability and the complexity of generative models by benchmarking learnability against different sets of samples drawn from probability distributions of variable degrees of complexities in their quantum and classical representations.
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