Nicola Pancotti

Component-Level Inverse Design of Transmon Qubits Using Neural Networks

  1. Olivia Seidel,
  2. Firas Abouzahr,
  3. Abhishek Chakraborty,
  4. Sadman Ahmed Shanto,
  5. Saikat Das,
  6. Daniel Baxter,
  7. Jonathan Asaadi,
  8. Nicola Pancotti,
  9. Haoyu Yang,
  10. Brucek Khailany,
  11. Sara Sussman,
  12. Enectali Figueroa-Feliciano,
  13. Eli M. Levenson-Falk,
  14. and Taylor L. Patti
Designing a superconducting qubit to realize specific Hamiltonian parameters typically requires iterating through a time and compute-intensive forward loop in which the designer chooses
a layout geometry, simulates it, extracts circuit parameters such as capacitances, and refines the geometry. We study the inverse version of this task using a neural-network workflow that maps target Hamiltonian parameters directly to component-level layout parameters, which we subsequently demonstrate on a planar transmon layout. During training, we pair the inverse model with a frozen forward surrogate model and evaluate the loss in Hamiltonian space rather than in layout-parameter space. In validation against a conventional EM solver, 97% of generated designs produce usable geometries, and the inverse-plus-surrogate pipeline reaches mean percent errors of 0.73% for qubit frequency and 1.58% for anharmonicity, comparable to or below the fabrication and simulation-to-measurement uncertainty expected for academic-process transmon devices of this type. A single pipeline query takes approximately 56 ms on CPU, versus approximately 2 min for a conventional EM capacitance extraction on the same hardware, a speedup of more than 2,100 times. Batching minimizes the AI model inference overhead, reducing the runtime to 0.24 ms per sample on CPU and 2.5 microseconds per sample on GPU, resulting in speedups of 5.0 x 10^5 and 4.8 x 10^7, respectively, relative to a single conventional CPU EM extraction. Our results indicate that component-level inverse design usefully extends and complements conventional EM simulation, including for small datasets on the order of 1,000 samples.
arxiv pdf

Kinetically constrained quantum dynamics in a circuit-QED transmon wire

  1. Riccardo Javier Valencia Tortora,
  2. Nicola Pancotti,
  3. and Jamir Marino
We study the dynamical properties of the bosonic quantum East model at low temperature. We show that a naive generalization of the corresponding spin-1/2 quantum East model does not
posses analogous slow dynamical properties. In particular, conversely to the spin case, the bosonic ground state turns out to be not localized. We restore localization by introducing a repulsive nearest-neighbour interaction term. The bosonic nature of the model allows us to construct rich families of many-body localized states, including coherent, squeezed and cat states. We formalize this finding by introducing a set of superbosonic creation-annihilation operators which satisfy the bosonic commutation relations and, when acting on the vacuum, create excitations exponentially localized around a certain site of the lattice. Given the constrained nature of the model, these states retain memory of their initial conditions for long times. Even in the presence of dissipation, we show that quantum information remains localized within decoherence times tunable with the system’s parameters. We propose a circuit QED implementation of the bosonic quantum East model based on state-of-the-art transmon physics, which could be used in the near future to explore kinetically constrained models in superconducting quantum computing platfoms.
arxiv pdf