Network Introduction

The Distributed Quantum Challenge

As quantum computing matures, organizations need to orchestrate workloads across multiple quantum backends. Each backend has different characteristics, availability, and strengths. Managing this complexity manually is error-prone and inefficient.

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Key Challenges

Resource Fragmentation

Quantum resources are scattered across different providers, each with different APIs, capabilities, and pricing.

Backend Variability

• IonQ: Trapped ions, all-to-all connectivity, high fidelity
• IBM: Superconducting qubits, limited connectivity, fast gates
• Rigetti: Superconducting, hybrid classical-quantum
• AWS Braket: Multiple technologies, cloud access

Execution Uncertainty

Hardware failures, calibration cycles, and maintenance windows make execution unpredictable.

Result Quality

Different backends produce different quality results. Aggregating and reconciling these requires sophisticated techniques.

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The Network Solution

QCOS Network provides a unified abstraction over multiple quantum backends:

┌─────────────────────────────────────────────────────┐
│                   Your Application                   │
├─────────────────────────────────────────────────────┤
│                   QCOS Network                       │
│  ┌─────────────────────────────────────────────────┐│
│  │           Unified Quantum API                   ││
│  ├─────────────────────────────────────────────────┤│
│  │  Orchestration │ Load Balancing │ Failover     ││
│  ├─────────────────────────────────────────────────┤│
│  │            Backend Adapters                     ││
│  └─────────────────────────────────────────────────┘│
├─────────────────────────────────────────────────────┤
│   IonQ    │    IBM    │  Rigetti  │    AWS    │ ... │
└─────────────────────────────────────────────────────┘

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Core Capabilities

1. Multi-Backend Execution

Execute circuits on multiple backends with a single API call:

result = network.execute(
    circuit=circuit,
    backends=["ionq_simulator", "ibm_brisbane", "rigetti_aspen"],
    strategy="all",
    shots=1024
)

for backend_result in result.results:
    print(f"{backend_result.backend}: {backend_result.counts}")

2. Intelligent Load Balancing

Four load balancing strategies:

Strategy	Behavior
fastest	Use the backend with lowest queue time
best_fidelity	Use the backend with highest current fidelity
round_robin	Distribute evenly across backends
cost_optimized	Minimize execution cost

3. Automatic Failover

If a backend fails, jobs are automatically rerouted:

result = network.execute(
    circuit=circuit,
    backends=["primary_backend", "backup_backend"],
    failover=True,
    failover_timeout_s=30
)

4. Result Aggregation

Combine results from multiple backends for improved accuracy:

result = network.execute(
    circuit=circuit,
    backends=["ionq", "ibm", "rigetti"],
    strategy="all",
    aggregate=True,
    aggregation_method="weighted_average"
)

print(f"Aggregated counts: {result.aggregated_counts}")
print(f"Confidence: {result.aggregation_confidence}")

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Architecture Components

Orchestrator

The central coordinator that:

• Receives execution requests
• Selects appropriate backends
• Monitors execution status
• Handles failures and retries

Backend Adapters

Plugins that translate between QCOS Network and specific backends:

• IonQ Adapter
• IBM Adapter
• Rigetti Adapter
• AWS Braket Adapter
• Local Simulator Adapter

Load Balancer

Real-time decision engine considering:

• Queue depths
• Current calibration quality
• Historical performance
• Cost constraints
• Geographic proximity

Result Aggregator

Sophisticated result combination:

• Weighted by fidelity
• Outlier detection
• Confidence scoring
• Error mitigation

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Supported Backends

Backend	Type	Qubits	Status
IonQ Simulator	Simulator	29	✅ Available
IonQ Aria	Hardware	25	✅ Available
IonQ Forte	Hardware	32	✅ Available
IBM Brisbane	Hardware	127	✅ Available
IBM Sherbrooke	Hardware	127	✅ Available
Rigetti Aspen-M	Hardware	80	✅ Available
AWS SV1	Simulator	34	✅ Available
Local Simulator	Simulator	32	✅ Always

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Network Topology

Cloud Network

Default topology connecting to all cloud backends via QCOS infrastructure.

Hybrid Network

Combine cloud backends with on-premises resources:

network = QuantumNetwork(
    cloud_backends=["ionq", "ibm"],
    local_backends=["local_gpu_simulator"],
    topology="hybrid"
)

Private Network

Enterprise-only air-gapped network for sensitive workloads.

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Use Cases

High Availability

Ensure workloads complete even if individual backends fail.

Performance Optimization

Route to the fastest available backend for time-sensitive work.

Cost Optimization

Balance quality and cost across different pricing tiers.

Research

Execute on multiple backends for comparative studies.

Validation

Cross-validate results by running on different technologies.

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Getting Started

Ready to leverage distributed quantum computing?

1. Quick Start Guide - Your first distributed job
2. API Reference - Explore the Network API
3. Load Balancing Guide - Optimize backend selection

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