Gabriel Alvarado Barrios

Digital-analog quantum computing of fermion-boson models in superconducting circuits

  1. Shubham Kumar,
  2. Narendra N. Hegade,
  3. Enrique Solano,
  4. Francisco Albarrán-Arriagada,
  5. and Gabriel Alvarado Barrios
We propose a digital-analog quantum algorithm for simulating the Hubbard-Holstein model, describing strongly-correlated fermion-boson interactions, in a suitable architecture with superconducting
circuits. It comprises a linear chain of qubits connected by resonators, emulating electron-electron (e-e) and electron-phonon (e-p) interactions, as well as fermion tunneling. Our approach is adequate for a digital-analog quantum computing (DAQC) of fermion-boson models including those described by the Hubbard-Holstein model. We show the reduction in the circuit depth of the DAQC algorithm, a sequence of digital steps and analog blocks, outperforming the purely digital approach. We exemplify the quantum simulation of a half-filling two-site Hubbard-Holstein model. In such example we obtain fidelities larger than 0.98, showing that our proposal is suitable to study the dynamical behavior of solid-state systems. Our proposal opens the door to computing complex systems for chemistry, materials, and high-energy physics.
arxiv pdf

Entangled Quantum Memristors

  1. Shubham Kumar,
  2. Francisco A. Cárdenas-López,
  3. Narendra N. Hegade,
  4. Xi Chen,
  5. Francisco Albarrán-Arriagada,
  6. Enrique Solano,
  7. and Gabriel Alvarado Barrios
We propose the interaction of two quantum memristors via capacitive and inductive coupling in feasible superconducting circuit architectures. In this composed system the input gets
correlated in time, which changes the dynamic response of each quantum memristor in terms of its pinched hysteresis curve and their nontrivial entanglement. In this sense, the concurrence and memristive dynamics follow an inverse behavior, showing maximal values of entanglement when the hysteresis curve is minimal and vice versa. Moreover, the direction followed in time by the hysteresis curve is reversed whenever the quantum memristor entanglement is maximal. The study of composed quantum memristors paves the way for developing neuromorphic quantum computers and native quantum neural networks, on the path towards quantum advantage with current NISQ technologies.
arxiv pdf