CSCI 5263: Quantum Computer Architecture and Systems

Instructor: Ramin Ayanzadeh

Email: ayanzadeh@colorado.edu

This course examine the architecture and system-level design of quantum computers. Learn programming, compilation, error correction, and noise mitigation through integrated lectures and labs using leading quantum SDKs and how to apply these concepts in a research-based project to improve reliability and enhance the security of emerging quantum systems. 

Topics Covered

  • Quantum computing basics: foundational principles including qubits, superposition, entanglement, and quantum gates.
  • Operational models of different types of quantum computers: circuit-based, adiabatic, and emerging paradigms such as measurement-based models and quantum annealing accelerators.
  • Quantum software stack: layers of quantum programming, compilation, and runtime execution.
  • Hybrid quantum–classical systems: design principles and system trade-offs in executing variational algorithms (e.g., QAOA, VQE, QML), focusing on training, latency, resource balance, and runtime coordination.
  • Qubit technologies: superconducting, trapped-ion, neutral-atom, and photonic platforms.
  • Hardware limitations (noise and reliability): impact of noise on quantum hardware; key limitations such as decoherence, gate fidelity, and limited connectivity.
  • Software - hardware co-design and microarchitectural control: pulse-level scheduling, calibration, and cross-layer optimization for fidelity and throughput.
  • Evaluation and benchmarking: metrics and tools for assessing performance, reliability, and scalability across quantum hardware and software platforms.
  • Quantum error correction and fault-tolerant architectures: surface and LDPC codes, real-time decoding, resource overhead, latency–fidelity trade-offs, and mitigation of magic-state factory costs.
  • Resource estimation and architectural simulation: performance modeling, logical-to-physical qubit mapping, and scalability analysis.
  • Distributed architectures: concepts, synchronization, and communication challenges in modular and networked quantum systems.
  • Security and privacy: system-level concerns and techniques for secure, privacy-preserving quantum computing in cloud-accessed environments.

Course Readings

This is an emerging and rapidly evolving area of research, where new results appear continuously. Lectures will be supported by selected readings from top-tier computer architecture and systems conferences such as ISCA, ASPLOS, HPCA, MICRO, and SC, as well as leading quantum venues including IEEE QCE and Nature journals. The reading list will evolve each year to reflect the latest advances and maintain relevance. Readings are intended to complement lectures, reinforcing key concepts and exposing students to current research directions.

Semester Grades

  • Labs and Assignments (30%): Hands-on exercises to apply quantum computing and system concepts using industry SDKs.
  • Paper Presentations (15%): Student-led presentations and discussions of research papers from top-tier architecture and quantum venues.
  • Reading Reviews (10%): Short written reflections or peer reviews demonstrating understanding of assigned readings.
  • Final Research Project Presentation (20%): Oral presentation of the final research project results.
  • Final Project Deliverables (25%): Written proposal, progress update, final report, and accompanying code submission.

Learning Outcome

  • Understand the operational models and system organization of different types of quantum computers.
  • Explain the structure and interactions of the quantum software and hardware stack.
  • Identify key architectural and system-level challenges in reliability, scalability, and performance.
  • Apply software–hardware co-design principles to improve fidelity and efficiency in quantum computing.
  • Evaluate design trade-offs across qubit technologies, compilers, and execution models.
  • Interpret and critique research papers in quantum computer architecture and systems.