Quantum computing

Quantum computing is a fundamental rethinking of how a computer works. Instead of moving electrons around in a circuit, the spin (or other quantity) can be manipulated to give a superposition of states. Some of the algorithms that are possible with the quantum computer are known to be faster than the best classical algorithms. In this group, we have the tools to work on all different aspects of quantum computing.

Quantum algorithms:

Quantum algorithms use a series of unitary operations to solve some problem.

A proposal to generate a machine learned model of the density functional without excessive measurement is here:

  • T.E. Baker and D. Poulin, "Density functionals and Kohn-Sham potentials with minimal wavefunction preparations on a quantum computer" Phys. Rev. Research 2, 043238 (2020) [online] [arxiv:2008.05592] [pdf] [bibtex]

A proposal to obtain the continued fraction representation of the Green's function is here:

  • T.E. Baker, "Computing Green's functions on a quantum computer via Lanczos recursion" Phys. Rev. A 103, 032404 (2021) [online] [arxiv:2008.05593] [pdf] [bibtex]


Design of a quantum computer:

In order to make the necessary qubit architectures robust to noise, we must take careful consideration of the design of the circuit elements. Primarily, we have worked on superconducting-circuit architectures using cavity quantum electro-dynamics principles with tensor network approaches:

  • A. Di Paolo, T.E. Baker, A. Prémont-Foley, D. Sénéchal, and A. Blais, "Efficient modeling of superconducting quantum circuits with tensor networks" npj Quant. Info. 7, 11 (2021) [online] [arxiv:1912.01018] [pdf] [bibtex]

Quantum error correcting codes:

In order to make self-correcting memories, a topological state can be encoded into a network of qubits. One question then becomes: can a good code be created that outperforms the toric code or any other code? We have investigated the maximal threshold possible on a code with Monte Carlo techniques, since these problems can be mapped onto a statistical physics model:

  • T.E. Baker, "Selecting initial states from Genetic Tempering for efficient Monte Carlo sampling" [online] [arXiv:1801.0937] [pdf] [bibtex]