Earlier this week, I participated in a UCSD/Harvard panel on quantum machine learning, mostly attended by undergraduate students curius about the topic.

Here are the questions that defined the conversation:

📌 How do I actually get started?

Students want to understand the first steps: which skills matter most, how much physics they really need, and how to build a foundation in linear algebra, algorithms, and quantum concepts without getting overwhelmed by the hype.

📌 What skills should undergraduates prioritize?

Beyond quantum theory, they’re asking about the practical mix of software engineering, math, and domain expertise. Many want to know whether learning by doing — small projects, cloud access, hands-on exercises — is more valuable than advanced coursework.

📌 Do I need a PhD to work in quantum?

This came up repeatedly. The short answer: not necessarily. With the industry growing across hardware, software, applications, and product roles, students are trying to understand which paths benefit most from deep academic specialization versus strong engineering or computational skills.

📌Where are the real career opportunities?

Students are increasingly aware that QML extends beyond academia. They’re asking which industries will see early value — from chemistry and pharma to finance, logistics, and materials — and how companies are building quantum teams today.

📌 What’s overhyped vs. underhyped in quantum computing?

A recurring theme: separating excitement from reality. Students asked which technologies are getting too much attention, which ones matter more than people realize, and how to interpret announcements about performance milestones and “quantum advantage.

📌 How do AI and quantum intersect?

There was real curiosity about two directions:using AI to improve quantum systems (decoding, error correction, optimization)using quantum to expand the frontier of AI models. Students wanted to understand where these intersections are mature — and where expectations still need grounding.

📌 Why neutral atoms — and what makes them different?

Students were curious about the physics and engineering behind neutral-atom platforms. They asked why companies are investing in neutral atoms, how they compare with trapped ions or superconducting qubits, and what advantages matter most for scaling, error correction, and real-world applications.

📌 What should early-career researchers focus on?

Many asked how to gain research experience as undergraduates, where to find meaningful projects, and how to identify groups, labs, and companies that welcome early talent.

The questions were thoughtful, forward-looking, and refreshingly pragmatic. If this is any indication, the next generation is committed to understanding the field on its merits and building skills that matter.