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Qoro Quantum

Qoro Quantum

Qoro Quantum builds the network stack for scalable, distributed quantum computing. Our mission is to enable quantum applications to run seamlessly across multi-vendor, heterogeneous computing environments — spanning CPUs, GPUs, FPGAs, and quantum processing units (QPUs).

We provide a modular software platform that abstracts the complexity of hybrid quantum-classical execution and orchestrates workloads dynamically across classical and quantum compute resources.

System Architecture

Qoro Quantum connects users to quantum resources through a centralized orchestration and control system, abstracting away low-level details like hardware scheduling, data movement, and circuit translation.

Qoro Quantum’s platform is composed of various software components that perform different tasks. It starts from the application layer to develop large scale quantum programs, to the cloud orchestration and scheduling, to the network control. The components are:

  flowchart TD
    classDef smallText font-size:10px;

    A["Divi (Python SDK)"] --> B["Usher (Cloud API)"]
    B --> C["Composer (Scheduler)"]
    C --> D["Conductor (Orchestrator)"]    
    D --> F["VQPU (Emulator)"]
    F --> E["Maestro (Simulator)"]
    D --> G["QPU (Device)"]

1. Divi – End-User Programming Library

  • An open-source Python library for building hybrid quantum-classical workflows.
  • Supports a variety of quantum applications and the ability to define custom applications.
  • Accelerates development of quantum programs, offloading infrastructure and execution management.
  • Handles tasks like result aggregation and error mitigation.

2. Usher – Cloud API

  • The RESTful interface for submitting jobs, querying status, and retrieving results.
  • Authenticates users and manages workload metadata.
  • Enables integration into enterprise pipelines and research platforms via secure APIs.

3. Composer – Network-Aware Scheduler

  • Schedules quantum and classical workloads with awareness of latency, topology, and bandwidth constraints in quantum datacenter networks.
  • Optimizes placement of distributed quantum programs to minimize execution time and maximize coherence.
  • Handles resource-aware scheduling and network- and hardware-layer parallelizations.
  • Uses intelligent decision making to find the best resource for the job.

4. Conductor – Cloud-Native Orchestration Engine

  • Coordinates and schedules hybrid jobs across distributed computing infrastructure.
  • Interfaces with multi-vendor, multi-modal networks to communicate job instructions.
  • Monitors the computing network for state-aware processing.

5. Maestro – Intelligent Quantum Simulator

  • Selects the most appropriate simulator backend based on circuit structure and hardware characteristics.
  • Chooses between matrix product state, state vector, and clifford engines.
  • Enables faster prototyping, benchmarking, and hybrid experimentation without manual simulator tuning.
  • Can estimate runtime for energy consumption estimation tasks.

6. VQPU – Quantum Computing Emulation

  • Mimics the behaviour of a QPU for integration testing.
  • Can behave like any type of quantum computer.
  • Accepts program instructions and mimics the noise and timing characteristics of the device.

Key features

  • Heterogeneous resource awareness
  • Quantum backend abstraction
  • Fine-grained parallelization
  • Multi-tenancy support
  • Scalable cloud-native deployment