I. I. Soloviev

Picosecond operations on superconducting quantum register based on Ramsey patterns

  1. M. V. Bastrakova,
  2. N. V. Klenov,
  3. V. I. Ruzhickiy,
  4. I. I. Soloviev,
  5. and A. M. Satanin
An ultrafast qubit control concept is proposed to reduce the duration of operations with a single and multiple superconducting qubits. It is based on the generation of Ramsey fringes
due to unipolar picosecond control pulses. The key role in the concept is played by the interference of waves of qubit states population propagating forward and backward in time. The influence of the shape and duration of control pulses on the contrast of the interference pattern is revealed in the frame of Ramsey’s paradigm. Protocols for observation of Ramsey oscillations and implementation of various gate operations are developed. We also suggest a notional engineering solution for creating the required picosecond control pulses with desired shape and amplitude. It is demonstrated that this makes it possible to control the quantum states of the system with the fidelity of more than 99%.
arxiv pdf

Adiabatic Superconducting Artificial Neural Network: Basic Cells

  1. I. I. Soloviev,
  2. A. E. Schegolev,
  3. N. V. Klenov,
  4. S. V. Bakurskiy,
  5. M. Yu. Kupriyanov,
  6. M. V. Tereshonok,
  7. A. V. Shadrin,
  8. V. S. Stolyarov,
  9. and A. A. Golubov
We consider adiabatic superconducting cells operating as an artificial neuron and synapse of a multilayer perceptron (MLP). Their compact circuits contain just one and two Josephson
junctions, respectively. While the signal is represented as magnetic flux, the proposed cells are inherently nonlinear and close-to-linear magnetic flux transformers. The neuron is capable of providing a one-shot calculation of sigmoid and hyperbolic tangent activation functions most commonly used in MLP. The synapse features by both positive and negative signal transfer coefficients in the range ~ (-0.5,0.5). We briefly discuss implementation issues and further steps toward multilayer adiabatic superconducting artificial neural network which promises to be a compact and the most energy-efficient implementation of MLP.
arxiv pdf