Laura Lewis

a man and a woman wearing sunglasses with stars on them are standing next to each other on a pink background . ALT: a man and a woman wearing sunglasses with stars on them are standing next to each other on a pink background .

Our paper Agnostic Process Tomography (APT) has been published in PRX Quantum.

Check it out here: doi.org/10.1103/q2nb...

Thanks @laura-lewis.bsky.social, Elham and Mina for the fun collaboration!

Nobel Prize in Physics 2025 The Nobel Prize in Physics 2025 was awarded jointly to John Clarke, Michel H. Devoret and John M. Martinis "for the discovery of macroscopic quantum mechanical tunnelling and energy quantisation in an...

The pioneering work of Clarke, Devoret, and Martinis showed that very cold electrical circuits behave in ways that exhibit fundamental principles of quantum physics.
www.nobelprize.org/prizes/physi...

Laura Lewis Born in China • Studied mathematics and computer science at California Institute of Technology (Caltech) in USA • Master’s in mathematics from University of Cambridge in UK • Lives in USA • Quantum in...

Very excited to share @laura-lewis.bsky.social #math journey on @hermathsstory.bsky.social! Check out her story, where she highlights how her love of #math has led to her career path in #QuantumInformation
hermathsstory.eu/laura-lewis/
#TheoreticalComputerScience, #WomenInQuantum

The picture shows the Her Maths Story logo on top, below is a portrait foto of Laura Lewis. She’s wearing a black shirt, white jacket and glasses. She has long dark hair wearing it to one side. In the background you can see green hills and trees. Below the picture it says her name and Quantum information student.

“(…) I started to notice gender imbalance in math (…). I hope that by continuing to pursue a research career, I can inspire other young women to (…) dive into mathematics with confidence.” – Laura Lewis

➡️ hermathsstory.eu/laura-lewis

#WomenInMaths #WomenInQuantum @laura-lewis.bsky.social

Classically, this is proven to be exponentially hard for any classical algorithm using gradient info, which are the workhorse algos for ML 💪

There are also hardness results in other regimes for SQ algorithms and even general classical algos learning under small noise.

Thus, we prove an exponential quantum advantage over classical gradient methods for this problem.

Many challenges arise from discretization (which can destroy the structure of our functions) and non-uniformity, so check out the paper!

We address this gap by designing an efficient quantum algorithm for learning periodic neurons (composition of periodic and linear function) over a broad class of non-uniform distributions.

This is also the first result for in quantum learning of classical real-valued functions.

Previous works proved exponential sample complexity advantages for other function classes when given uniformly distributed data. 🙂

In contrast, for adversarial distributions, there is no advantage in general. 😕

What about for non-uniform distributions 📊?

With Dar Gilboa and Jarrod McClean, we prove a new quantum advantage for learning periodic neurons in the quantum statistical query model!
arxiv.org/pdf/2503.20879

The hardness of this problem was studied long before this work by classical learning theorists. 🔎

At PRX Quantum, we are very excited to launch a collection of papers celebrating the International Year of Quantum Science and Technology!
Have a look… and for those at the APS Global Physics Summit 2025 please come to our session on Tuesday to hear more! prxquantumcollection.org