Infrequent posting due to other commitments. Here are a few brief items of note from the past month:
- Beng Yee uploaded his second paper from his PhD research to arXiv: A Unified Framework for Trace-induced Quantum Kernels. This project tackled the problem of how to choose the best quantum kernel for a given learning task using tools from classical multiple kernel learning theory. The bottom line: the optimal problem formulation (e.g. as a kernel model, projected kernel model, or quantum neural network) depends on the relative amount of training and test data, whether one wants to impose constraints to the trained model, and whether one has many qubits with low-fidelity gates or a fewer qubits with high fidelity gates. Read to find out more!
- The December issue of Optics & Photonics News highlights some of the most exciting peer-reviewed research in optics and photonics published over the past year. There is also an accompanying perspective on areas to watch in 2024 and beyond by selected summary authors.
- Two papers recently published in PRL caught my eye: Universal Sampling Lower Bounds for Quantum Error Mitigation suggests the quantum error mitigation being pushed by IBM and others as a means of getting useful applications out of current noisy quantum processors may be foiled by an exponentially growing measurement overhead, and Classifying Topology in Photonic Heterostructures with Gapless Environments shows how a recently-developed real space formulation of topological invariants may be a more useful tool for quantifying the robustness of topological states in photonic systems, particularly those exhibiting radiation losses of optical nonlinearities.
- The 7th International Conference on Optical Angular Momentum will be held 24 - 28 June 2024 in South Africa. The abstract submission deadline is 7 January 2024.
- The next edition of the Quantum Techniques in Machine Learning conference will be held in Melbourne, 25-29 November 2024. The abstract submission deadline is 5 July 2024.
- In the news headlines: Alibaba shuts quantum computing lab. Seems to be part of a wider trend of industry funding shifting from quantum to generative AI - see also Zapata and Normal Computing.
