November 4, 2026
As quantum technologies continue to develop, researchers are exploring how quantum computers can do more than process information: they want to understand how these systems can learn from data, make inferences, and draw conclusions about the world around us.
For Junyu Liu, an assistant professor at the University of Pittsburgh’s School of Computing and Information (SCI), addressing these questions means examining the systems that make quantum machine learning possible. His recent research funding includes a Swiss National Science Foundation (SNSF)-supported collaboration with Yiwen Chu at ETH Zurich and a new National Science Foundation (NSF) Quantum Information Science grant, “Towards Fourth Generation Quantum Sensors” (NSF Award #2610010), with Kaushik Seshadreesan, also an assistant professor at SCI.
Although the projects approach quantum machine learning from different directions, both investigate how quantum technologies can learn more efficiently and reliably under real-world constraints.
Connecting Classical Data to Quantum Systems
The SNSF-supported project, “An acoustic quantum random-access memory,” addresses a fundamental challenge in quantum computing: how to efficiently transfer classical data into a quantum system.
Much of the information used in machine learning exists in classical form. For quantum computers to learn from that information, they need efficient ways to access it. Without those interfaces, the time required to retrieve data could diminish the performance advantages offered by quantum algorithms.
Liu’s collaboration with Chu at ETH Zurich explores how acoustic devices could help address this challenge. While Chu’s group leads the experimental work, Liu’s group investigates the architectures and protocols needed to make these interfaces efficient and reliable as systems grow.
The collaboration brings theoretical computer science and experimental hardware research together, allowing researchers to develop approaches informed by both what quantum systems may achieve and what can be built in practice.
“I want the work to help researchers make concrete decisions about which devices to build and which applications are worth pursuing,” Liu said.
Exploring the Future of Quantum Sensing
Liu’s second project, “Towards Fourth Generation Quantum Sensors,” approaches quantum machine learning from the perspective of sensing. Supported by a new NSF Quantum Information Science grant and conducted in collaboration with Seshadreesan, the project investigates how quantum devices can learn useful information directly from the physical world.
Quantum sensors can measure physical quantities, but researchers are still working to understand how combining these sensors with intelligent quantum processors could improve the efficiency and precision of those measurements.
The project aims to establish theoretical foundations for determining when these combined systems could offer meaningful advantages. For example, researchers want to understand whether a quantum sensor working with a quantum processor could reach a reliable conclusion using fewer measurements or estimate a physical quantity more accurately.
“I enjoy that back-and-forth because a practical detail about a device can change how we think about an algorithm or an architecture, and sometimes a theoretical observation suggests a new experiment,” Liu said.
These questions also account for practical challenges, including noise and the limited data available in real experiments. By investigating the relationship between sensing and learning, the researchers hope to identify when these technologies could offer advantages over existing approaches.
Bringing Theory and Experiment Together
For Liu, both projects reflect a broader interest in connecting computer science expertise with the development of useful quantum technologies.
“When I chose this subject as a graduate student, I was watching quantum computing grow from a field that still felt like it was in its infancy into one that was beginning to attract national attention,” Liu said. “It was exciting to think that the ideas we were studying could eventually become technologies people would use.”
That interest continues to shape how he approaches research, particularly when theoretical ideas can be connected to systems that researchers can build and test.
Collaboration plays an important role in that process. Working with Chu allows Liu’s group to develop computer science approaches alongside experimental hardware, creating opportunities for theoretical findings to inform experiments and for practical constraints to shape new research questions. His collaboration with Seshadreesan similarly brings together interests in sensing, learning, and quantum information, while giving students opportunities to approach these problems from different perspectives.
As both projects progress, Liu hopes to see their theoretical foundations translate into concrete results. For the acoustic memory project, that includes testing small memory architectures and investigating what would be required to scale them. For the quantum sensing project, the focus is on identifying when combining sensors with quantum processors can improve learning efficiency or measurement precision, and what experiments would be needed to demonstrate those improvements.
Together, the projects address complementary challenges in quantum machine learning: enabling quantum systems to access information efficiently and helping them extract useful information from the physical world. By studying these foundations alongside the development of quantum hardware, Liu and his collaborators aim to help researchers determine which systems to build and which applications are worth pursuing.
“As the projects move forward, I’m most excited about seeing these connections become concrete through working prototypes and results that can guide experiments,” Liu said.
Liu also extends his gratitude to the Pitt SCI staff who supported the financial administration of these grants, particularly Kevin Loya and Don Bonidie, for their significant contributions to pre-award financial management. He emphasizes that their dedication and support have been instrumental in helping his research group reach this milestone.