Seminar: Gianluca Stefanucci, University of Rome Tor Vergata, 3pm Wed 2nd March 2022


Online seminar via MS Teams

Title and abstract TBC

Seminar: Liam Scarlett (Curtin University, Australia); 2pm, Wed 23 February 2022


Abstract and title TBC

Seminar: Nicole Yunger Halpern (NIST, QuICS, University of Maryland); Wed 09 February 2022


09 February 2022, 4pm (time TBC)
Location: MS Teams online seminar


My favorite not-quite-probability distribution and its usefulness in metrology”


Quasiprobability distributions resemble probability distributions but can contain negative and imaginary values. Such distributions represent quantum states as probability distributions over phase space represent states in classical statistical mechanics. Many quasiprobabilities exist, the most famous being the Wigner function. Among the least famous ranks the Kirkwood-Dirac distribution, discovered during the early 1900s and then forgotten. But the Kirkwood-Dirac distribution has been enjoying a renaissance recently: Applications range from quantum chaos to tomography, metrology, foundations, and thermodynamics. I will introduce the Kirkwood-Dirac distribution and illustrate its usefulness in metrology: The quasiprobability can be used to prove that operators’ noncommmutation—a nonclassical phenomenon—underlies a protocol’s effectiveness in phase estimation. I aim to convince you that the Kirkwood- Dirac distribution is the best little quasiprobability you’d never heard of.


References
1) Arvidsson-Shukur, NYH, Lepage, Lasek, Barnes, and Lloyd, Nat. Comms. 11, 3775 (2020).
2) Arvidsson-Shukur, Drori-Chevalier, and NYH, J. Phys. A 54, 284001 (2021).
3) Lupu-Gladstein, Yilmaz, Arvidsson-Shukur, Brodutch, Pang, Steinberg, and NYH, arXiv:2111.01194 (2021).
4) NYH, Swingle, and Dressel, Phys. Rev. A 97, 042105 (2018).

Seminar: Nicholas Chancellor, Durham University, 3pm Wed 19 January 2022


Online seminar via MS Teams.


Continuous time quantum computing beyond adiabatic: quantum walks and fast quenches


I will first give a brief review of solving hard optimisation problems through quantum computing in particular in a continious-time as opposed to gate-model setting and then summarize some of our recent results in that area. While the adiabatic theorem provides a useful theoretical handle to understand quantum computing in continuous time, solving hard problems adiabatically would require an exponentially long runtime and therefore unless P=NP will require either an exponentially long coherence time or a mechanism to restore coherence. On the other hand, algorithms which only succeed with an exponentially small probability may still be useful on more realistic devices, for which coherence time either does not scale, or scales only mildly. We find that even the simplest of this algorithm, a quantum walk which consists of evolution with a fixed Hamiltonian can provide better scaling on artificial spin glass problems than unstructured Grover-like search, this implies that the algorithm is using the structure of the problem. When parameters are swept over time rather than held constant, the scaling becomes dramatically better, and competitive with state of the art quantum algorithms. I will discuss the theoretical reasons why these algorithms perform so well, which relate to the relative energy expectation of different terms of the Hamiltonian, and give several examples to demonstrate how the theoretical tools we have developed work, following the results reported in [Callison et. al. PRX Quantum 2, 010338]. I will also touch on the potential relevance of this work to gate-model quantum computing through and algorithm known as the quantum approximate optimisation algorithm.

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