2 research outputs found
Physics-Informed Neural Networks for an optimal counterdiabatic quantum computation
We introduce a novel methodology that leverages the strength of
Physics-Informed Neural Networks (PINNs) to address the counterdiabatic (CD)
protocol in the optimization of quantum circuits comprised of systems with
qubits. The primary objective is to utilize physics-inspired deep
learning techniques to accurately solve the time evolution of the different
physical observables within the quantum system. To accomplish this objective,
we embed the necessary physical information into an underlying neural network
to effectively tackle the problem. In particular, we impose the hermiticity
condition on all physical observables and make use of the principle of least
action, guaranteeing the acquisition of the most appropriate counterdiabatic
terms based on the underlying physics. The proposed approach offers a
dependable alternative to address the CD driving problem, free from the
constraints typically encountered in previous methodologies relying on
classical numerical approximations. Our method provides a general framework to
obtain optimal results from the physical observables relevant to the problem,
including the external parameterization in time known as scheduling function,
the gauge potential or operator involving the non-adiabatic terms, as well as
the temporal evolution of the energy levels of the system, among others. The
main applications of this methodology have been the and
molecules, represented by a 2-qubit and 4-qubit systems
employing the STO-3G basis. The presented results demonstrate the successful
derivation of a desirable decomposition for the non-adiabatic terms, achieved
through a linear combination utilizing Pauli operators. This attribute confers
significant advantages to its practical implementation within quantum computing
algorithms.Comment: 28 pages, 10 figures, 1 algorithm, 1 tabl