Quantum transport and localization in 1d and 2d tight-binding lattices

  1. Amir H. Karamlou,
  2. Jochen Braumüller,
  3. Yariv Yanay,
  4. Agustin Di Paolo,
  5. Patrick Harrington,
  6. Bharath Kannan,
  7. David Kim,
  8. Morten Kjaergaard,
  9. Alexander Melville,
  10. Sarah Muschinske,
  11. Bethany Niedzielski,
  12. Antti Vepsäläinen,
  13. Roni Winik,
  14. Jonilyn L. Yoder,
  15. Mollie Schwartz,
  16. Charles Tahan,
  17. Terry P. Orlando,
  18. Simon Gustavsson,
  19. and William D. Oliver
Particle transport and localization phenomena in condensed-matter systems can be modeled using a tight-binding lattice Hamiltonian. The ideal experimental emulation of such a model
utilizes simultaneous, high-fidelity control and readout of each lattice site in a highly coherent quantum system. Here, we experimentally study quantum transport in one-dimensional and two-dimensional tight-binding lattices, emulated by a fully controllable 3×3 array of superconducting qubits. We probe the propagation of entanglement throughout the lattice and extract the degree of localization in the Anderson and Wannier-Stark regimes in the presence of site-tunable disorder strengths and gradients. Our results are in quantitative agreement with numerical simulations and match theoretical predictions based on the tight-binding model. The demonstrated level of experimental control and accuracy in extracting the system observables of interest will enable the exploration of larger, interacting lattices where numerical simulations become intractable.

Deep Neural Network Discrimination of Multiplexed Superconducting Qubit States

  1. Benjamin Lienhard,
  2. Antti Vepsäläinen,
  3. Luke C.G. Govia,
  4. Cole R. Hoffer,
  5. Jack Y. Qiu,
  6. Diego Ristè,
  7. Matthew Ware,
  8. David Kim,
  9. Roni Winik,
  10. Alexander Melville,
  11. Bethany Niedzielski,
  12. Jonilyn Yoder,
  13. Guilhem J. Ribeill,
  14. Thomas A. Ohki,
  15. Hari K. Krovi,
  16. Terry P. Orlando,
  17. Simon Gustavsson,
  18. and William D. Oliver
Demonstrating the quantum computational advantage will require high-fidelity control and readout of multi-qubit systems. As system size increases, multiplexed qubit readout becomes
a practical necessity to limit the growth of resource overhead. Many contemporary qubit-state discriminators presume single-qubit operating conditions or require considerable computational effort, limiting their potential extensibility. Here, we present multi-qubit readout using neural networks as state discriminators. We compare our approach to contemporary methods employed on a quantum device with five superconducting qubits and frequency-multiplexed readout. We find that fully-connected feedforward neural networks increase the qubit-state-assignment fidelity for our system. Relative to contemporary discriminators, the assignment error rate is reduced by up to 25 % due to the compensation of system-dependent nonidealities such as readout crosstalk which is reduced by up to one order of magnitude. Our work demonstrates a potentially extensible building block for high-fidelity readout relevant to both near-term devices and future fault-tolerant systems.