Quantum software and algorithms research

About

The QSAR group works on designing and implementing the software that enables us to write and run algorithms on quantum computers. It was established in 2022 in the Department of Electrical and Computer Engineering at UBC in Vancouver, Canada, and is a part of Quantum BC.

News

(2025-05-05) Congratulations to Ritu, who will be spending the summer with Xanadu as part of their Residency Program's 2025 cohort!
(2025-04-07) The QSAR Lab is excited to begin a new collaboration with Entropica Labs. Mushahid will be working closely with their team on software for quantum error correction as part of a Mitacs Accelerate International internship.
(2025-03-31) Cihan, Gabe, Mushahid, and Ritu represented the Lab with poster presentations at UBC ECE's Research Day. Congrats to Gabe for placing 2nd in the poster competition!
(2024-12-16) The QSAR Lab is now officially part of the Stewart Blusson Quantum Matter Institute at UBC!
(2024-08-23) Congratulations to Abhi on passing his PhD qualifying examination! We are happy to have him continue with us as a PhD student.
(2024-07-30) The QSAR Lab is going to IEEE Quantum Week! Gideon will speak about his new qubit reuse algorithm, Mushahid will speak about his quantum debugging tools, and Ritu will present a poster about her work on readout-aware spin qubit compilation.
(2024-07-17) Congrats to Gabe and Abhi for placing 1st and 2nd in the NSERC CREATE in Quantum Computing's annual 3-minute thesis competition! They delivered full presentations of their work at the Quantum BC Seminar Series on 13 August.
(2024-05-21) The QSAR lab welcomes Nils Quetschlich, from the Chair for Design Automation at TUM, as a visiting PhD student this summer.
(2024-05-07) Thanks to Gabe's hard work, a mixed-state qutrit simulation device was released with PennyLane 0.36! Thank you to the PennyLane team for all their time and work reviewing our contributions.

Past news items can be found in our news archive.

Research

Software and compilation

Quantum compilation is the process of translating a high-level description of a quantum algorithm into a set of instructions that is executed on hardware. It's a process with many moving parts that typically involve solving computationally hard problems.

Areas of investigation include:

Developing automated compilation tools that scale into the 100s of qubits or more
Exploring how machine learning and can improve compilation pipelines
Methods and tools for testing and debugging quantum programs
Exploring how quantum programming languages and compiler considerations will change as we move towards fault-tolerant systems

Ultimately, we are interested in doing the "hard part" (such as the optimization process in the gif below), so that users of quantum software don't need to worry about what happens under the hood, and can focus instead on writing quantum algorithms.

Graphic of the quantum compilation stack.

Gif of optimizing a quantum circuit.

Quantum algorithms and applications

Differentiable quantum transforms being used in an error mitigation pipeline.

Compared to classical computing, there are relatively few known quantum algorithms, and even fewer that will one day achieve the substantial speedups needed to solve life-sized problems.

Our group works on both co-designing and implementing software that facilitates our reasoning about algorithms, and exploring how it can be used to develop new algorithms and applications.

Our focus areas include:

Developing qutrit and qudit simulation tools for the open-source framework PennyLane
Exploring the use of higher-level quantum systems like qutrits and qudits for quantum algorithms
Applying quantum computing to problems in nuclear, particle, and condensed-matter physics
Development and implementation of new differentiable quantum transforms

Research Contributions

ODM, S. Núñez-Corrales, M. Stechły, S. P. Reinhardt, T. Mattson (2024) An Abstraction Hierarchy Toward Productive Quantum Programming. In Proc. of 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), Montreal, PQ. pp. 979-989. (arXiv)

M. Khan, P. Nair, ODM (2024) CircInspect: Integrating Visual Circuit Analysis, Abstraction, and Real-Time Development in Quantum Debugging. In Proc. of 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), Montreal, PQ. pp. 1000-1006.

G. Uchehara, T. Aamodt, ODM (2024) Graph-based identification of qubit network (GidNET) for qubit reuse. In Proc. of 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), Montreal, PQ. pp. 1120-1131. (arXiv)

ODM (2024) On the need for effective tools for debugging quantum programs. In Proc. of the 5th ACM/IEEE International Workshop on Quantum Software Engineering (Q-SE 2024). Association for Computing Machinery, New York, NY, USA, 17–20. (arXiv)

C. Sarma, ODM, A. Abhishek, P. C. Srivastava (2023) Prediction of the neutron drip line in oxygen isotopes using quantum computation. Phys. Rev. C 108 064305. (arXiv)

G. Bottrill, M. Pandey, ODM (2023) Exploring the Potential of Qutrits for Quantum Optimization of Graph Coloring. 2023 IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA, USA, 2023, pp. 177-183. (arXiv)

G. Uchehara, T. Aamodt, ODM (2022) Rotation-inspired circuit cut optimization. In 2022 IEEE/ACM Third International Workshop on Quantum Computing Software (QCS), 50-56. (arXiv)

Software Contributions

The Ionizer, a tool for transpiling and optimizing circuits into trapped ion gates
QSAR student researchers contributed two simulation devices, default.qutrit and default.qutrit.mixed, and a suite of qutrit operators to PennyLane, with the support of the PennyLane team.
QT-CorGI, a library for solving graph 3-colouring problems with qutrit-based QAOA.

Our group's full software suite can be found on our GitHub organization page.

People

Current members

Abhishek Abhishek (PhD student)
Cihan Bosnali (undergraduate student)
Gabe Bottrill (MASc student)
Gideon Uchehara (co-supervised PhD student)
Jackson Fraser (undergraduate student)
Jacky Jiang (MASc student)
Mushahid Khan (co-supervised PhD student)
Nishanth Baskaran (PhD student)
Ritu Thombre (MASc student)

Former members

Tanishq Pradhan (undergraduate student, summer/fall 2024)
Nils Quetschlich (visiting PhD student, summer 2024)

Positions

For prospective graduate students

All positions for the 2025/2026 are now filled. Please check back at a later date.

For UBC undergraduate students

All positions for summer 2025 are filled. If you are a UBC undergraduate interested in taking a research experience course in Fall/Winter 2025/2026, please contact Olivia directly at olivia.dimatteo@ubc.ca.

Group code of conduct

All QSAR group members are expected to uphold the following codes of professional and scientific conduct. The text below is partially based on CoCs of the Tropini, Avasthi, and Willis labs.

The CoC was last reviewed in August 2024. As a group, we will review and update it together on at least a yearly basis (in particular, following an influx of new members) so that everyone has the opportunity to contribute their ideas.

Professional conduct

You work with the group, not for the group. All group members, regardless of academic status, are independent researchers in training. Everyone comes with their own skill set and experience, and contributes their unique perspective to the work that we do. We work together and don't compete with each other.
Be professional with everyone. Treat other group members and your shared space with respect. Ensure everyone has the chance to speak during group meetings, and be mindful of interrupting or talking over others. Our work hours are flexible, so be considerate of the time and schedules of your colleagues. When you are collaborating with others, giving a talk, or at a conference, you are out representing our group. Conduct yourself in the same professional manner as you would within the group, regardless of where you are and who you are with, and follow the CoCs in place at events.
Harassment will not be tolerated. There is zero tolerance for exclusionary comments or jokes, threats, or violent behaviour or language. Offensive behaviour or comments of any kind relating to gender, gender identity and expression, sexual orientation, disability, mental illness, neuro(a)typicality, physical appearance, body size, age, race, ethnicity, religion, lifestyle choices, etc. are not welcome in our group. Please do not take photographs of others, share their work, or post personally-identifying information online without their explicit permission.
Ask for help when needed, and provide it willingly. Everyone is here to learn, so ask lots of questions! When you receive questions, respond to them respectfully without putting the other person down; focus on helping the other person learn how to do something, rather than simply doing it for them. Be mindful of people's time: if your question is something a quick online search will resolve, do so before reaching out.
Provide constructive feedback. Feedback is meant to help someone improve. Identify errors when they occur, and make direct and honest suggestions where appropriate (including to the group lead!). Make feedback about the work, not the person. This includes discussions about the work of others.

Scientific conduct

We share our data and code. We post preprints on arXiv so our work is available to everyone. We publish our source code and data under permissive licenses on GitHub or other code-sharing platforms. We also prioritize the use of open-source libraries and contribute back to them when we can.
We conduct our work with integrity. Never falsify, make omissions, or otherwise manipulate your code or data. Our work should be reproducible by others in the field, and we should provide everything needed to enable that.
We collaborate. This includes with other research groups at UBC and beyond, companies, and online communities. We treat confidential information within collaborations with the utmost care. If you wish to collaborate with an external party, great! Please initiate a discussion with the group lead first.
We provide proper attribution. Always cite sources and images in written work and presentations, even if the source is yourself. When using external software libraries, make sure to check the licenses.
We are mindful about the use of generative AI. It is useful for code completion and boilerplate code once you're comfortable implementing something from scratch. It's also helpful for rephrasing or checking grammar on a first draft written based on your own ideas. However, please don't provide models not trained and hosted by us (or a partner we trust) with large blocks of text or code from unpublished work, or use them for projects containing confidential information or IP.
It's okay to make mistakes. Everyone makes them, this is how we learn. It is important to be open and honest about them, accept responsibility, and we will work together to resolve them.