Programmatic Control
Native Quantum-Inspired Optimization for Python Developers
Integrate BQPhy directly into your Python workflows using a clean, production- ready SDK.
Scale from notebooks to enterprise pipelines with cloud-backed, high- performance solvers.
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Step 01
Install SDK
Install the BQPhy Python package using pip
Step 02
Authenticate
Initialize your session with bqphy.login() to securely connect to the BQP cloud backend.
Step 03
Solve
Pass your optimization model to bqphy.solve() and retrieve results natively in Python.
Run Advanced Optimization on Your Real Models
Quantum-inspired solvers for large-scale, non-convex optimization—integrated directly into your Python workflows.
Linux (x86_64)
GLIBC: 2.32 or higher
Supported Distributions:
Ubuntu 22.04+ (GLIBC 2.35+)
CentOS Stream 9+ (GLIBC 2.34+)
RHEL 9+ (GLIBC 2.34+)
Fedora 35+ (GLIBC 2.34+)
Ubuntu 20.04+ (requires GLIBC upgrade to 2.32+)
Debian 11+ (may require GLIBC upgrade)
Windows (64-bit)
Windows 10 / 11
Microsoft Visual C++ Redistributable 2019 or later
Python Dependencies
Python: 3.10+
pip: 21.0+ recommended
venv: Required (included with Python 3.10+)
NumPy: Installed automatically with the wheel package
Hardware Requirements
Multicore x86 architecture
Minimum 8 CPU cores
Memory:
Minimum: 2 GB RAM
Recommended: 4 GB RAM
Disk Space:
200 MB free space for virtual environment and dependencies