Quantum Computing

An applied quantum learning initiative focused on real-world problems, decision-making, and insight. The program aims to strengthen applied quantum innovation by expanding both the community of practitioners and the range of credible application areas.

Applied Quantum Systems Program

By prioritizing problem formulation, structural analysis, and reduction, the program trains participants to evaluate where quantum or quantum-inspired methods are appropriate, rather than focusing on tool usage alone. This approach addresses a key gap in the current quantum ecosystem, where practical advantage is highly application-dependent.

The capstone project anchors this vision by requiring participants to apply the methodology to real-world problems. This enables structured exploration of applications across domains such as agriculture, cybersecurity, optimization, materials modeling, and data-driven engineering. Overall, the program functions as a bridge between foundational quantum knowledge and domain-specific application, supporting rigorous, responsible, and application-driven adoption of quantum methods across industries. Primary focus on the use, applicability, and justification of quantum methods. Platform-agnostic training centered on abstraction and system modeling.

Multi-week program enabling depth, iteration, and technical maturity. Emphasis on problem formulation, structure identification, and reduction. Learner actively applies their existing domain expertise to formulate, model, and evaluate new problems. Success measured by quality of reasoning, modeling decisions, and feasibility assessment. Capstone-driven exploration of real-world applications (e.g. agriculture, cybersecurity, optimization).

Focus

Applied quantum methods for real-world systems

Duration

8-10 week training program

Approach

Problem formulation, structural analysis, computational reduction

Participants

Technical students, engineers, R&D professionals

Outcome

Evaluating and optimizing quantum & quantum-inspired solutions.

Course Content

Unit 1
Classical vs quantum systems 1 topic
1 Qubit
Unit 2
What makes a system “quantum” vs “complex”? 0 topics
Unit 3
When classical intuition fails 0 topics
Unit 4
Systems vs particles: a modeling shift 0 topics
Unit 5
Case studies: where quantum thinking appears implicitly 0 topics
Unit 6
Course roadmap and expectations 0 topics
Unit 7
Describing Systems: Deterministic vs probabilistic 0 topics
Unit 8
Signals, noise, and information limits 0 topics
Unit 9
Classical uncertainty vs quantum uncertainty 0 topics
Unit 10
When classical models break down 0 topics
Unit 11
Lesson Number 30 0 topics
Unit 12
Motivation for quantum representations 0 topics
Unit 13
State as an information container 0 topics
Unit 14
Encoding information beyond bits 0 topics
Unit 15
Transformations as system operations 0 topics
Unit 16
Geometry of state spaces 0 topics
Unit 17
Interpreting amplitudes without formalism 0 topics
  • Meet the Developer

ArithQ

ArithQ, incorporated in January 2026, was founded by Mandana Bidarvand, a postdoctoral researcher in quantum and mathematical modeling. The company’s mission is to turn advanced computational ideas—classical, quantum, and hybrid—into practical tools that simplify complex scientific and industrial problems. ArithQ focuses on reducing the computational burden in large-scale models by identifying structure, compressing complexity, and producing cleaner, more usable formulations.

Beyond its technical work, ArithQ aims to serve as a bridge between academia and industry—helping researchers understand where their expertise can solve real, high-value challenges. By revealing meaningful problems and providing the mathematical pathways to approach them, ArithQ partners with Nanan Academy to inspire more scientists to translate their knowledge into practical impact.