Becoming a physicist was not Maria Schuld's life goal. As an undergrad, she started out studying political science, taking physics in parallel. Her plan was to work for a nonprofit organization in a capacity that had a very clear benefit to society. But then, she says, "life happened"--jobs fell through and other opportunities opened up--and she found herself with a career in quantum machine learning. Today Schuld, who works for the Canadian quantum computing company Xanadu from her home in South Africa, says that she has matured in what she thinks it means for a person to benefit society.
This week's episode focuses on the interface between physics and computing, with deep dives into how artificial intelligence (AI) is contributing to medical physics and how silicon could form the basis of a future quantum computer. First, we hear from Tami Freeman, Physics World's resident expert on medical physics, about a new positron emission tomography (PET) scanner that can image a patient's whole body much more quickly (or at higher resolutions) than is possible with current commercial scanners. We then stick with the medical theme to discuss three recent examples of how AI is being used in medicine: firstly to diagnose skin conditions (but, disturbingly, only if the patient's skin is white); secondly to help radiologists detect lung tumours in X-rays; and thirdly to develop better radiotherapy treatment plans. There are several ways of constructing the qubits, or quantum bits, that make up a quantum computer, and this week we hear from a trio of researchers – Fernando Gonzalez-Zalba, Alessandro Rossi and Tsung-Yeh Yang – who have been developing silicon-based qubits. Their work is part of a Europe-wide collaboration between universities, government laboratories and companies called MOS-Quito, and you can read more about it in their article for the Physics World Focus on Computing.
This book is a whirlwind survey of more speculative topics– getting well beyond "normal" low-energy quantum physics to talk about black holes and that sort of thing– wrapped up in a personal narrative. This gets into some heavy ideas, but Gefter's voice and enthusiasm for the topic are charming enough to carry it off.
Predicting the future is easy, if you are a physicist. Break a glass, and you can boldly assert that it will fall into a number of shards, assuming you know the initial conditions. Knowing the past is more difficult – you need to store much more information to piece a pile of broken glass back together. This "causal asymmetry" makes it easier to determine cause and effect and thus place events in order. But it doesn't exist in the quantum world, say Mile Gu at Nanyang …