CloudQC

CloudQC is a network-aware framework for multi-tenant distributed quantum computing that optimizes both quantum network cost and quantum computing performance.

Overview

CloudQC addresses the challenges of running distributed quantum computing (DQC) in a multi-tenant quantum cloud environment. It focuses on two key problems:

1. Efficiently placing multiple quantum circuits onto Quantum Processing Units (QPUs)
2. Scheduling network resources(EPR) pairs

Core Components

• Multi-tenant Circuit Placement: Places quantum circuits onto QPUs while considering the impact on future circuit placements and minimizing cross-QPU communication
• Network Scheduler: Intelligently allocates quantum network resources (EPR pairs) while accounting for the probabilistic nature of quantum communication

Basic Usage

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()

Requirements

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

License

MIT License