Quantum Computing for First-Time Learners

Lewis Westfall, Avery M. Leider

Samantha Norrie, Anthony Estey

One of the first challenges that new learners of Quantum Computing face is learning how to use and interpret quantum circuits represented in circuit, Dirac, and matrix notation. It is necessary for learners to understand the different notations used in Quantum Computing in order to ensure that they have a robust comprehension of the field and can make use of a variety of resources. Depending on their technical background, some learners may struggle with certain notations more than others. We present QNotation, a visual tool that helps learners explore the aforementioned notations. Circuit, Dirac, and matrix notation. This allows them to be able to identify the differences and similarities between the different notations. QNotation was built to be used at multiple stages throughout the Quantum Computer learning journey. While one may start using QNotation to help them become familiar with the different notations used in Quantum Computing, the tool can also be used as a visual quantum circuit debugging tool. In addition to QNotation being a tool that can be used by learners, it can also be used by instructors to support the teachings of quantum computing concepts and building blocks, including entanglement and Quantum Fourier Transform.

Reevu Maity

We investigate learning models for implementing arbitrary unitary operations and perform quantum machine learning tasks. First, we discuss efficient constructions for building unitary transformations in U(d), thus providing upper bounds on the minimum time required to implement them. Second, we study the hardness of learning unitary operations in U(d) via gradient descent on the time parameters of an alternating operator sequence, and numerically find that, despite the nonconvexity of the loss landscape, gradient descent always converges to the target unitary when the sequence contains d2 or more parameters. Third, we introduce the first quantum boosting algorithm for converting a weak quantum learner into a strong accurate quantum learner for a Boolean concept class C; thereby achieving a quadratic quantum improvement over classical AdaBoost in terms of VC(C).

Juuso Jaakola, Vlad Stirbu, Teiko Heinosaari

Interactive quantum circuit simulators are increasingly used to support introductory quantum computing education for learners from varied disciplinary backgrounds. These environments can also generate detailed response logs of submitted circuits, but such data are rarely transformed into evidence that instructors can use for course redesign and development. We present a learning analytics framework for quantum circuit simulator tasks and report its task-level branch on anonymised logs from an introductory quantum computing course delivered in the TIM learning environment. The task branch characterises exercises through participation, completion, first-attempt accuracy, valid-attempt counts, sessionised active time, and observed answer-space reporting of common correct and incorrect constructions. The dataset covers the 2024-2025 cohort of 492 learners, 47,533 valid submissions, and 21 obligatory quantum circuit exercises. The framework identifies bottleneck tasks, broad solution spaces, and recurring wrong-answer patterns that are not visible from final scores alone. Its most distinctive output is a plain-text representation of the observed answer space, designed to make open-ended circuit submissions directly interpretable to instructors. We show how response logs can reveal actionable task-design issues and support concrete course redesign. A learner-level branch is retained only as an ongoing research extension and is not a central empirical contribution of the present paper.

H. David Sheets, Michael Dupin

Jesse Van Griensven Thé, Roydon Andrew Fraser, José R. Rosas-Bustos