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Compressing neural network by tensor network with exponentially fewer variational parameters
In this work, we propose a general compression scheme that significantly reduces the variational parameters of NN by encoding them to deep automatically-differentiable tensor network (ADTN) that contains exponentially-fewer free parameters.
Quantum compiling with a variational instruction set for accurate and fast quantum computing
The quantum instruction set (QIS) is defined as the quantum gates that are physically realizable by controlling the qubits in quantum hardware.
Predicting Quantum Potentials by Deep Neural Network and Metropolis Sampling
The hybridizations of machine learning and quantum physics have caused essential impacts to the methodology in both fields.
Preparation of Many-body Ground States by Time Evolution with Variational Microscopic Magnetic Fields and Incomplete Interactions
State preparation is of fundamental importance in quantum physics, which can be realized by constructing the quantum circuit as a unitary that transforms the initial state to the target, or implementing a quantum control protocol to evolve to the target state with a designed Hamiltonian.
Automatically Differentiable Quantum Circuit for Many-qubit State Preparation
Taking the ground states of quantum lattice models and random matrix product states as examples, with the number of qubits where processing the full coefficients is unlikely, ADQC obtains high fidelities with small numbers of layers $N_L \sim O(1)$.
