
Differentiable Learning of Quantum Circuit Born Machine
Quantum circuit Born machines are generative models which represent the ...
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Unsupervised Generative Modeling Using Matrix Product States
Generative modeling, which learns joint probability distribution from tr...
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Simulating Quantum Computations with Tutte Polynomials
We establish a classical heuristic algorithm for exactly computing quant...
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Enhancing Generative Models via Quantum Correlations
Generative modeling using samples drawn from the probability distributio...
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Learnability of the output distributions of local quantum circuits
There is currently a large interest in understanding the potential advan...
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The Inductive Bias of Quantum Kernels
It has been hypothesized that quantum computers may lend themselves well...
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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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Probabilistic Modeling with Matrix Product States
Inspired by the possibility that generative models based on quantum circuits can provide a useful inductive bias for sequence modeling tasks, we propose an efficient training algorithm for a subset of classically simulable quantum circuit models. The gradientfree algorithm, presented as a sequence of exactly solvable effective models, is a modification of the density matrix renormalization group procedure adapted for learning a probability distribution. The conclusion that circuitbased models offer a useful inductive bias for classical datasets is supported by experimental results on the parity learning problem.
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