Powerful New Developer Tools Increase the Versatility of the QCOS Platform

The field of quantum computing has advanced beyond the level of error-prone physical qubits to an era of reliable quantum computation with logical qubits. To continue making progress toward fault-tolerant quantum machines, advances in both quantum hardware and software are required. SoftQuantus is committed to providing the tools that developers and scientists need to enable applications on the quantum machines that exist today, as well as the fault-tolerant machines of the future.

New Tools That Simplify Quantum Development

The QCOS Development Kit is an open-source developer toolkit for building quantum applications. It provides everything needed—from simulators to a modern programming experience—for developers to build and execute quantum code, both locally and on quantum hardware.

QCOS Platform Architecture

Domain libraries and workflows available with QCOS include those for quantum chemistry, error correction, optimization, and machine learning, with other applications on the horizon.

Diagram of the QCOS Platform showing Core Framework, Platform Modules, Applications, and Hardware Abstraction Layer

AI-Enabled Quantum Programming

QCOS is fully integrated with VS Code and GitHub Copilot. After installing the QCOS extension from the VS Code Marketplace, GitHub Copilot simplifies the use of QCOS features including Python and Jupyter integration, circuit rendering, IntelliSense, breakpoint debugging, local simulators, visualizations, histograms, hardware submission, and resource estimation.

from softqcos import QCOS, AutopilotConfig

# Initialize QCOS client
qcos = QCOS(api_key="your-api-key")

# Configure Autopilot for VQE optimization
config = AutopilotConfig(
    algorithm="VQE",
    ansatz="EfficientSU2",
    optimizer="COBYLA",
    shots=1000,
    hardware="ibm_heron"
)

# Submit and run with automatic optimization
result = await qcos.run_autopilot(
    hamiltonian=molecular_hamiltonian,
    config=config
)

# Access optimized results
print(f"Ground state energy: {result.energy}")
print(f"Evaluations used: {result.iterations}")  # ~78% fewer than VQE
print(f"Fidelity achieved: {result.fidelity}")

Example: Implementing VQE optimization with QCOS Autopilot using the Python SDK

With GitHub Copilot and QCOS, programming tasks such as code generation, unit tests, and job submissions are faster and easier than ever before.

Developer Tools & SDK

QCOS for Scientific Applications

For today's quantum computers to help solve complex scientific problems, those problems must first be simplified and optimized to fit within hardware constraints. QCOS provides the advanced tools that developers and quantum researchers need to transform complex challenges into problems that today's quantum computers can tackle efficiently.

Built-in Error Mitigation

SoftQuantus has a longstanding heritage of building tools to further quantum error correction research and development. QCOS includes built-in error mitigation strategies that are automatically applied based on hardware characteristics.

• ✓Zero-Noise Extrapolation (ZNE) — Automatically scales noise and extrapolates to ideal results
• ✓Probabilistic Error Cancellation (PEC) — Hardware-calibrated noise inversion
• ✓Readout Error Mitigation — Corrects measurement biases with calibration matrices
• ✓Dynamical Decoupling — Pulse sequences that suppress decoherence during idle periods

QCOS is Part of Our Highly Adaptable Platform

The QCOS platform brings together quantum hardware, software, AI, and high-performance computing to create an end-to-end solution. By implementing advanced error mitigation on high-quality physical qubits, our platform enables reliable computation needed to realize groundbreaking use cases of quantum computing.

The QCOS platform is designed to be highly versatile and integrable with multiple types of quantum hardware. Our hardware abstraction layer (HAL) enables seamless workload migration across providers, ensuring you're never locked into a single vendor.