Documentation

Getting Started

Welcome to the k&z documentation hub. Whether you are a quantum researcher submitting your first circuit or an enterprise team integrating hybrid quantum-classical pipelines, these docs will guide you through every step. Our platform is designed to minimize the distance between your algorithm and a running QPU — no cryogenics expertise required.

Quick-Start Checklist

Create your account — Sign up at console.kandz.co and verify your organization email.
Generate API credentials — Navigate to Settings → API Keys to create your first key pair.
Install the SDK — Use the command below to add our Python SDK to your environment.
Submit a test job — Run the included sample circuit to confirm end-to-end connectivity.
Explore advanced features — Hybrid workflows, error mitigation, and multi-QPU scheduling.

SDK Installation

The k&z SDK is available for Python 3.10+ and supports integration with popular quantum frameworks including Qiskit, Cirq, and PennyLane.

# Install the k&z SDK via pip
pip install kandz-sdk

# Or install with optional integrations
pip install kandz-sdk[qiskit,cirq,pennylane]

# Verify your installation
python -c "import kandz; print(kandz.__version__)"

Authentication

All API requests require authentication via either an API key or an OAuth 2.0 bearer token. API keys are ideal for server-to-server communication, while OAuth tokens support interactive and delegated workflows.

import kandz

# Authenticate with an API key
client = kandz.Client(api_key="kz_live_abc123...")

# Or authenticate with OAuth
client = kandz.Client(
    oauth_token="eyJhbGciOi...",
    org_id="org_456def"
)

Store your credentials securely using environment variables (KZ_API_KEY) or a secrets manager. Never commit API keys to version control.

QPU Access

Learn how to discover available quantum processors, check qubit counts and connectivity, query calibration data, and submit circuits to specific QPU clusters. Includes guidance on selecting the right processor topology for your workload.

View QPU API docs →

Hybrid Workflows

Combine classical HPC compute with quantum processors in a single pipeline. Our hybrid runtime handles data serialization, intermediate measurement, and parameter re-binding automatically so you can focus on the algorithm, not the plumbing.

Explore hybrid docs →

Circuit Compilation

Understand how k&z transpiles abstract circuits into hardware-native gate sets. Configure optimization levels, gate decomposition strategies, and qubit mapping heuristics to minimize depth and maximize fidelity on real devices.

Compilation reference →

Error Mitigation

Apply readout error correction, zero-noise extrapolation, and probabilistic error cancellation with a single parameter flag. Our built-in mitigation toolkit integrates seamlessly with job submission — no post-processing scripts required.

Mitigation techniques →

Job Scheduling

Control how your workloads are queued and prioritized. Learn about fair-share scheduling, priority tiers, reserved capacity windows, and batch submission to optimize throughput for large-scale experiments.

Scheduling guide →

Security & Compliance

Understand how k&z protects your data at rest and in transit, manages encryption keys, enforces access controls, and maintains compliance with SOC 2 Type II, ISO 27001, and GDPR requirements.

Security docs →

SDK Reference

The k&z SDK exposes a high-level Pythonic interface over our REST API. Core modules include:

kandz.Client — Session management, authentication, and configuration.
kandz.circuits — Circuit construction helpers and framework adapters (Qiskit, Cirq, PennyLane).
kandz.jobs — Job submission, status polling, cancellation, and result retrieval.
kandz.devices — QPU discovery, calibration data, and availability queries.
kandz.hybrid — Hybrid workflow orchestration and classical-quantum data exchange.
kandz.mitigation — Error mitigation strategies and post-processing utilities.

Sample: Submit a Bell State Circuit

import kandz

client = kandz.Client()

# Build a simple Bell state circuit
circuit = kandz.circuits.QuantumCircuit(2)
circuit.h(0)
circuit.cx(0, 1)
circuit.measure_all()

# Submit to the best available QPU
job = client.jobs.submit(
    circuit=circuit,
    shots=4096,
    device="auto",            # automatic device selection
    mitigation="readout"      # apply readout error correction
)

# Wait for results
result = job.wait()
print(result.counts)
# {'00': 2038, '11': 2058}

Additional Resources

Full API Reference — Complete endpoint documentation with request and response schemas.
Security Overview — How we protect your quantum workloads and data.
Support Center — Get help from our quantum infrastructure team.
Blog & Research Notes — Technical deep-dives and product announcements.