Computing Multiple Energy Levels with a Tensor Network Method

This technology computes more than one of the lowest-lying eigenvalues simultaneously. This has applications to computing properties of superconducting qubits and more. Tensor networks have long been known to efficiently find the extremal eigenvalues at the ends of the full spectrum; in particular, for systems with local interactions obeying the area law. Finding the ground state energy with DMRG is often insufficient to fully characterize a given system. This is a common problem in many-body systems. Finding excited energy levels beyond the ground state is also required to gain a better understanding of quantum systems in general. The invention is an algorithm that finds more than one energy level at the same time. Importantly, this method avoids accuracy issues which are suffered by other DMRG techniques, making this invention highly reliable in comparison. One application is superconducting circuits, which are made of an array or lattice of many superconducting junctions. With this invention, representing the lattice problem into a tensor network allows for the determination of the lowest-lying energy levels of the circuit. Those values can be used to find relevant experimental quantities—such as the coherence times of the qubit—and find a high degree of accuracy with known experimental observations. François Nadeau 873 339-2028

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