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Anthony Polloreno, Ph.D.

Research Engineer

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Rigetti Computing

I worked as a full-stack software engineer building a quantum computer at Rigetti Computing. During this time, the company grew from ~10 to ~100. I was a primary maintainer and developer of the control library, and delivered several key engineering goals:

  1. Instrumentation driver development for signal analyzer, VNA, microwave radio, AWG, switching matrix, etc. (Python, C++, instrumentation DSLs).
  2. Database front end and backend development (Python, SQL, SQLAlchemy, Flask, Alembic, HTML).
  3. Code reviewed core functionality for experimental syntax and semantics and managed version releases.
  4. Developed distributed code for running distributed statistical analyses (Python, MPI, MPI4PY).
  5. Developing a matched filters interface for optimal signal readout (Python, C++).
  6. Developed the Clifford library for use on the experimental stack, as well as through pyquil. One product of this was a reduced swap-representation that can be found here(Python, Lisp).
  7. Developed simulation code for efficiently calibration the DRAG parameter in single qubit gates (Julia).
  8. Developed schema for storing calibration data.
  9. Managed translation of experimental suite into internal experimental DSL for pulse based experiments (Python).
This work resulted in these papers:
  1. Parametrically Activated Entangling Gates Using Transmon Qubits,
  2. Demonstration of Universal Parametric Entangling Gates on a Multi-Qubit Lattice,
  3. Demonstration of a Parametrically Activated Entangling Gate Protected from Flux Noise.

Markovian Error Generators

Code for symbolically deriving the lowest-order effect of Markovian error rates on a quantum gate set.

Penning Trap Simulation

Code for simulating the dynamics of a 2D crystal of ions in a Penning trap over a cluster. Can be found here.

Convex Optimization for Optimal Control

A small suite of numerical tools are available from my time at Sandia. I use the convex optimization library MPI4py and simple gradient-based methods together with Gaussian quadrature to produce noise-robust optimal controls for a two level quantum system. Code is available here.