Jaimie A. Greasley | Searcharxiv

arXiv2026

Intro2QC: An Approachable Introduction to Quantum Computing for STEM Education in High Schools

In recent years, quantum computing has gained strong traction on the global stage. Pioneering developments in quantum engineering, algorithms and software reinforce the view that quantum computation will eventually catalyze transformative technological advancements across diverse sectors. In anticipation of the future impact of quantum computing, many governments, universities and tech organizations have invested heavily into advancing quantum science, boosting quantum readiness, and building a diverse, talented workforce through quantum education initiatives. Specialized university programs, public awareness campaigns and STEM outreach activities all serve a critical function in fostering a deeper understanding of quantum physics and quantum technologies among non-experts. At the level of high school, a major vision is for these initiatives to reshape the teaching of 20th century physics in STEM classrooms, and inspire the future generation of quantum scientists and professionals. In the current paper, we are presenting the materials we have created for an outreach workshop introducing quantum computing to high school students in an approachable and engaging way. Intro2QC is an exploration of quantum computing's relevance to solving real-world challenges by relating how the unintuitive behaviours of quantum systems can be useful in providing computational advantage. The workshop materials include an online interactive quantum programming tutorial hosted on the Intro2QC website, along with the full lesson plan for STEM instructors and the presentation slides available on the Github repository. This single-session 90-minute workshop has been delivered to approximately 160 students in multiple school groups from Grades 9-12 in British Columbia Canada in the highlight of the International Year of Quantum Science and Technology.

physics.ed-ph↗

arXiv2025

Quantum algorithm for the gradient of a logarithm-determinant

The logarithm-determinant is an widely-present operation in many areas of physics and computer science. Derivatives of the logarithm-determinant compute physically relevant quantities in statistical physics models, quantum field theories, as well as the inverses of matrices. A multi-variable version of the quantum gradient algorithm is developed here to evaluate the derivative of the logarithm-determinant. From this, the inverse of a sparse-rank input operator may be determined efficiently. Measuring an expectation value of the quantum state--instead of all $N^2$ elements of the input operator--can be accomplished in $O(k/\varepsilon^2)$ time in the idealized case for $k$ relevant eigenvectors of the input matrix with precision $\varepsilon$. A practical implementation of the required operator will likely need $\log_2N$ overhead, giving an overall complexity of $O((k\log_2 N)/\varepsilon^2)$. The method applies widely and converges super-linearly in $k$ when the condition number is high. The best classical method we are aware of scales as $N$. Given the same resource assumptions as other algorithms, such that an equal superposition of eigenvectors is available efficiently, the algorithm is evaluated in the practical case as $O(\log_2 N/\varepsilon^2)$. The output is given in $O(1)$ queries of oracle, which is given explicitly here and only relies on time-evolution operators that can be implemented with arbitrarily small error. The algorithm is envisioned for fully error-corrected quantum computers but may be implementable on near-term machines. We discuss how this algorithm can be used for kernel-based quantum machine-learning.

quant-ph↗