QuanLLM-v2.0-qm is QuanLLM's current domain model, starting with quantum mechanics, running at 14B dense and upgradable to 27B dense, with on-premise deployment so data stays on campus. It supports concept understanding, equation-based derivation, and continuous course-aware Q&A, turning abstract ideas into professional answers learners can understand, follow up on, and use. The same architecture can later be extended and customized to more STEM and subject domains; system reliability and scientific verification are provided together with QuanLLM Harness.
Why does tunneling probability fall rapidly as the barrier becomes wider?
A wider barrier makes the wavefunction decay over a longer forbidden region, leaving less amplitude on the far side and rapidly reducing transmission probability.
Extensible Domain Intelligence
QuanLLM-v2.0-qm focuses on domain understanding, equation relationships, and teaching-oriented explanation. It is not merely a chat entry point or the entire QuanLLM system; it is the core domain model that provides subject intelligence to the learning product. It currently starts with quantum mechanics and can later be extended and customized to more domains.
QuanLLM-v2.0-qm can support concept explanation, derivation, and continuous course-aware Q&A, presenting professional content in a form that fits the learning task.
Why can a particle cross a barrier that would be forbidden in classical mechanics?
The wavefunction can extend into the classically forbidden region and decay inside the barrier. For a finite barrier, non-zero amplitude can remain on the far side, giving a non-zero transmission probability.
Starting with quantum mechanics, QuanLLM-v2.0-qm focuses on three high-frequency learning tasks: understanding abstract concepts, following derivations, and continuing questions within course context. The same model framework can later be extended and customized to more STEM and subject domains.
Explain abstract ideas such as states, measurement, wavefunctions, and probability by connecting mathematical expressions to physical meaning.
Organize boundary conditions, equation relationships, and intermediate steps so learners can see the logic connecting what is given to the final result.
Continue with “why,” “what is the next step,” and “how does this connect to earlier material,” while keeping the answer grounded in the same learning context.
These layers are complementary rather than redundant: QuanLLM-v2.0-qm provides understanding, derivation, and explanation for domain-specific content, while QuanLLM Harness adds independent checking, scientific-computing support, and outcome communication. The same reliability framework can be reused as the model extends to more domains.
Turn course-domain questions into professional, learnable domain responses.
Add reliability and scientific support before model output enters a product.
QMStudy then turns both capabilities into courses, AI tutoring, self-testing, and continuous learning experiences.
The first batch is limited to 50 spots; approved applicants receive the API Key and usage instructions by email. Send the fixed email template.
First-batch API Key applications use the fixed template below. Approved applicants receive the API Key and usage instructions by email.