For many quasiprobability distributions, the set of positive (“classical”) states is difficult to characterize. However, for a random Kirkwood–Dirac distribution, we completely characterized not only the positive states, but also the positive measurements and unitaries.
Here is a practice version of my talk from last weeks Foundations of Quantum Technologies Conference at Girton College Cambridge. It is a brief overview of my PhD thesis.
https://t.co/L0Ce1OZb5Q
Since finishing my PhD viva on the 6th, with minor corrections, life has been busy. The viva discussion with Alex Thom and Simon Benjamin was insightful, and I'll have a better thesis as a result—thank you! Corrections are now done, but were slowed by an accident on the 17th.
This technique is the same as we used in our recent work on magic state distillation to achieve a 42% improvement in overheads:
https://t.co/PKpdBj6VZR
In our recent arXiv (https://t.co/TACezZ3dDs) we benchmarked QEC and magic state factories for spin qubits. By computing magic state production overheads as a function of initialization, gate, measurement, and decoherence times we estimated the runtimes for typical algorithms.
VQEs have 3 main problems: (i) excessive runtimes, (ii) susceptibility to noise, and (iii) optimizability. In our recent preprint (https://t.co/OnDXWZfUQV), we address the first 2 without losing optimizability. We achieve this by designing 15x faster pulses for 4-qubit gates.
In our recent arXiv (https://t.co/TACezZ3dDs) we benchmarked QEC and magic state factories for spin qubits. By computing magic state production overheads as a function of initialization, gate, measurement, and decoherence times we estimated the runtimes for typical algorithms.
The University of Cambridge is hosting a Foundations of Quantum Technologies conference on September 15–18th to mark the 100-year anniversary of the discovery of modern quantum mechanics.
Abstract submissions for talks and posters close May 4th.
Details:
https://t.co/pUsCqC1pOt