A Python-based Quantum Cloud Simulator for distributed quantum computing environments.

QuCloudSimPy simulates the behavior of quantum jobs running on distributed quantum cloud infrastructures with multiple networked quantum processors (QPUs).

• Simulate quantum clouds with various network topologies
• Generate and schedule quantum circuit jobs
• Different scheduling strategies and placement algorithms
• Analyze performance metrics of distributed quantum computing

• Quantum Cloud Model: Network of QPUs with configurable topologies
• Job Management: Generation and tracking of quantum circuit jobs
• Scheduling System: Various strategies for job scheduling and resource allocation
• Simulation Engine: Discrete Event Simulator (DES) for modeling system behavior

from job import job_generator
from cluster import qCloud
from jobScheduler import job_scheduler
from des import DES
import networkx as nx

# Create a quantum cloud with 4 QPUs in a ring topology
cloud = qCloud(4, nx.cycle_graph, None, 5, 20)

# Generate quantum jobs
job_gen = job_generator()
job_queue = job_gen.generate_job(10)

# Create a discrete event simulator and scheduler
des_simulator = DES(cloud=cloud)
scheduler = job_scheduler(job_queue, des_simulator, cloud)

# Schedule and run jobs
scheduler.schedule_fifo()
des_simulator.run()

• Circuit Types: QFT, QuGAN, Arithmetic, and custom circuits
• Network Topologies: Ring, Star, Mesh, Random, Custom
• Scheduling Strategies: FIFO, Greedy, Simulated Annealing, BFS
• Placement Algorithms: Genetic Algorithm, Simulated Annealing, Partitioning

• Python 3.6+
• numpy, networkx, pytket, matplotlib, pymetis

MIT License