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Found 7 papers, 2 papers with code
Ab-initio quantum chemistry with neural-network wavefunctions
Machine learning and specifically deep-learning methods have outperformed human capabilities in many pattern recognition and data processing problems, in game playing, and now also play an increasingly important role in scientific discovery.
Analytic theory for the dynamics of wide quantum neural networks
We define wide quantum neural networks as parameterized quantum circuits in the limit of a large number of qubits and variational parameters.
Representation Learning via Quantum Neural Tangent Kernels
We analytically solve the dynamics in the frozen limit, or lazy training regime, where variational angles change slowly and a linear perturbation is good enough.
Precise measurement of quantum observables with neural-network estimators
The measurement precision of modern quantum simulators is intrinsically constrained by the limited set of measurements that can be efficiently implemented on hardware.
Quantum Physics Disordered Systems and Neural Networks Strongly Correlated Electrons
Fermionic neural-network states for ab-initio electronic structure
Neural-network quantum states have been successfully used to study a variety of lattice and continuous-space problems.
Computational Physics Disordered Systems and Neural Networks Strongly Correlated Electrons Quantum Physics
Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
Quantum computers can be used to address molecular structure, materials science and condensed matter physics problems, which currently stretch the limits of existing high-performance computing resources.
Quantum Physics Superconductivity
Tapering off qubits to simulate fermionic Hamiltonians
Such encodings eliminate redundant degrees of freedom in a way that preserves a simple structure of the system Hamiltonian enabling quantum simulations with fewer qubits.
Quantum Physics
