Davide Venturelli

Entanglement Across Separate Silicon Dies in a Modular Superconducting Qubit Device

  1. Alysson Gold,
  2. JP Paquette,
  3. Anna Stockklauser,
  4. Matthew J. Reagor,
  5. M. Sohaib Alam,
  6. Andrew Bestwick,
  7. Nicolas Didier,
  8. Ani Nersisyan,
  9. Feyza Oruc,
  10. Armin Razavi,
  11. Ben Scharmann,
  12. Eyob A. Sete,
  13. Biswajit Sur,
  14. Davide Venturelli,
  15. Cody James Winkleblack,
  16. Filip Wudarski,
  17. Mike Harburn,
  18. and Chad Rigetti
Assembling future large-scale quantum computers out of smaller, specialized modules promises to simplify a number of formidable science and engineering challenges. One of the primary
challenges in developing a modular architecture is in engineering high fidelity, low-latency quantum interconnects between modules. Here we demonstrate a modular solid state architecture with deterministic inter-module coupling between four physically separate, interchangeable superconducting qubit integrated circuits, achieving two-qubit gate fidelities as high as 99.1±0.5\% and 98.3±0.3\% for iSWAP and CZ entangling gates, respectively. The quality of the inter-module entanglement is further confirmed by a demonstration of Bell-inequality violation for disjoint pairs of entangled qubits across the four separate silicon dies. Having proven out the fundamental building blocks, this work provides the technological foundations for a modular quantum processor: technology which will accelerate near-term experimental efforts and open up new paths to the fault-tolerant era for solid state qubit architectures.
arxiv pdf

A case study in programming a quantum annealer for hard operational planning problems

  1. Eleanor G. Rieffel,
  2. Davide Venturelli,
  3. Bryan O'Gorman,
  4. Minh B. Do,
  5. Elicia Prystay,
  6. and Vadim N. Smelyanskiy
We report on a case study in programming an early quantum annealer to attack optimization problems related to operational planning. While a number of studies have looked at the performance
of quantum annealers on problems native to their architecture, and others have examined performance of select problems stemming from an application area, ours is one of the first studies of a quantum annealer’s performance on parametrized families of hard problems from a practical domain. We explore two different general mappings of planning problems to quadratic unconstrained binary optimization (QUBO) problems, and apply them to two parametrized families of planning problems, navigation-type and scheduling-type. We also examine two more compact, but problem-type specific, mappings to QUBO, one for the navigation-type planning problems and one for the scheduling-type planning problems. We study embedding properties and parameter setting, and examine their effect on the efficiency with which the quantum annealer solves these problems. From these results we derive insights useful for the programming and design of future quantum annealers: problem choice, the mapping used, the properties of the embedding, and the annealing profile all matter, each significantly affecting the performance.
arxiv pdf