Releases: followthesapper/ATLAS-Q
ATLAS-Q v0.6.3 - Coherence-Aware VQE with VRA Integration
Coherence-aware VQE framework with real-time quality validation:
- GO/NO-GO classification using e^-2 boundary threshold (R̄ ≈ 0.135)
- VRA integration achieving 5× measurement compression
- Hardware-validated on IBM Brisbane: H2 (R̄=0.891), LiH (R̄=0.980), H2O (R̄=0.988)
Multi-platform installation:
- PyPI: pip install atlas-quantum
- UV: uv pip install atlas-quantum
- Debian/Ubuntu: .deb package included
- Conda-forge: recipe ready
Technical improvements:
- Professional documentation (removed emojis, consolidated research docs)
- Fixed import sorting for CI compliance
- Updated all dependencies and version numbers
ATLAS-Q v0.6.2
ATLAS-Q v0.6.2
Release Description
This release adds comprehensive Sphinx documentation and Grover's quantum search algorithm implementation to ATLAS-Q.
Key Features:
- Complete Documentation System - 70+ pages of professional Sphinx-based documentation including tutorials, how-to guides,
explanations, and comprehensive API reference - Grover's Algorithm - Production-ready quantum search implementation with MPS backend integration, supporting both
function-based and bitmap oracles - Enhanced VQE/QAOA - UCCSD ansatz for molecular chemistry simulations with improved gradient computation
- Custom Documentation Theme - Professional documentation site with custom branding and responsive design
Documentation Highlights:
- 6 tutorials covering beginner to advanced topics
- 9 practical how-to guides for common tasks
- 8 in-depth explanation articles on algorithms and design
- 19 comprehensive API reference pages with examples
- GitHub Pages deployment with automatic builds
New Modules:
- atlas_q.grover - Grover's quantum search algorithm
- atlas_q.ansatz_uccsd - UCCSD ansatz for molecular VQE
- atlas_q.unitary_backends - Unitary evolution backends
Improvements:
- Enhanced MPO operations with additional contraction methods
- Improved test coverage across all modules
- Better error handling and numerical stability
- CI/CD improvements with comprehensive linting
Documentation Site: https://followthesapper.github.io/ATLAS-Q/
Installation:
pip install atlas-quantum==0.6.2
ATLAS-Q v0.5.0
ATLAS-Q v0.5.0
GPU-accelerated quantum tensor network simulator with adaptive MPS, custom Triton kernels, and verified benchmarks.
Installation
pip install atlas-quantum[gpu]
Or try the https://colab.research.google.com/github/followthesapper/ATLAS-Q/blob/ATLAS-Q/ATLAS_Q_Demo.ipynb in Google Colab.
Key Features
- Adaptive MPS: 626,000× memory compression vs full statevector (30 qubits)
- Custom Triton Kernels: 1.5-3× speedup on gate operations
- Stabilizer Backend: 20× speedup for Clifford circuits
- Period-Finding: Shor's algorithm with O(1) memory for periodic states
- NISQ Simulation: Noise models with Kraus operators
- Variational Algorithms: VQE, QAOA with GPU optimization
- Time Evolution: TDVP for Hamiltonian dynamics
- 2D Circuits: Automatic SWAP insertion for grid topologies
Performance
- 77,304 ops/sec gate throughput (GPU)
- 30 qubits in 0.03 MB (vs 16 GB statevector)
- All 7/7 benchmark suites passing
What's Included
- Python package for PyPI (atlas-quantum)
- Docker images (GPU + CPU)
- Interactive Jupyter notebook demo
- Complete documentation with verified examples
- GitHub Actions CI/CD workflows
Documentation
- https://github.com/followthesapper/ATLAS-Q/blob/ATLAS-Q/docs/COMPLETE_GUIDE.md
- https://github.com/followthesapper/ATLAS-Q/blob/ATLAS-Q/docs/FEATURE_STATUS.md
- https://github.com/followthesapper/ATLAS-Q/blob/ATLAS-Q/ATLAS_Q_Demo.ipynb
- https://followthesapper.github.io/ATLAS-Q/
Quick Example
from atlas_q import get_quantum_sim
QCH, _, _, _ = get_quantum_sim()
sim = QCH()
factors = sim.factor_number(221)
print(f"221 = {factors[0]} × {factors[1]}") # 221 = 13 × 17
Requirements
- Python 3.9+
- PyTorch 2.0+
- CUDA 11.8+ (for GPU acceleration)
- Triton 2.0+ (optional, for custom kernels)
License
MIT LicenseATLAS-Q v0.6.1
ATLAS-Q v0.6.1 - Critical PyPI Package Fixes
Bug Fix Release - Resolves import errors affecting PyPI users
What's Fixed
🐛 Critical Import Errors:
- Fixed NameError: name 'Tuple' is not defined in mpo_ops.py
- Fixed NameError: name 'np' is not defined in mpo_ops.py
- Added missing typing and numpy imports to core modules
📦 Triton Kernels Now Included:
- Moved triton_kernels/ into package structure (src/triton_kernels/)
- PyPI users now get custom Triton GPU kernels (1.5-3× speedup)
- Simplified import paths - no more complex sys.path manipulation
- Graceful fallback to PyTorch if Triton not installed
🔧 Additional Fixes:
- Fixed all Ruff I001 import sorting violations
- Updated Docker images to v0.6.1
- Corrected PEP 8 import ordering across all files
Who Should Upgrade
CRITICAL for PyPI users: If you installed via pip install atlas-quantum, this fixes breaking import errors. Upgrade immediately:
pip install --upgrade atlas-quantum
Docker users: Automatically resolved in new image builds
Verification
This now works without errors:
from atlas_q import get_mpo_ops, get_vqe_qaoa, get_adaptive_mps
mpo_modules = get_mpo_ops()
MPOBuilder = mpo_modules['MPOBuilder']
✅ No more NameError!
Full Changelog: See https://github.com/followthesapper/ATLAS-Q/blob/main/CHANGELOG.md
ATLAS-Q v0.6.0
ATLAS-Q v0.6.0 - Molecular Chemistry & Advanced Tensor Networks
Release Date: October 2025
We're excited to announce ATLAS-Q v0.6.0, featuring quantum chemistry integration, graph optimization, and advanced tensor network capabilities. This release adds
38 new tests, bringing total integration test coverage to 46/46 passing ✅.
🎯 What's New
Priority 1: Quantum Chemistry & Graph Optimization
Molecular Hamiltonians - Full quantum chemistry support
- PySCF integration for electronic structure calculations
- Jordan-Wigner transformation for fermion-to-qubit mapping
- Support for H₂, LiH, H₂O, and custom molecular geometries
- Seamless VQE integration for ground state energy calculations
- 4/4 tests passing
MaxCut QAOA - Graph optimization Hamiltonians
- Automatic MaxCut Hamiltonian builder for QAOA
- Weighted and unweighted graph support
- Automatic edge normalization for undirected graphs
- Perfect for combinatorial optimization problems
- 4/4 tests passing
Priority 2: Advanced Tensor Network Features
Circuit Cutting - Partition large circuits
- Min-cut graph partitioning algorithm
- Entanglement analysis and visualization
- Automatic sampling overhead calculation
- Run large circuits on small quantum computers
- 7/7 tests passing
PEPS 2D Tensor Networks - True 2D quantum simulation
- Projected Entangled Pair States for 2D grid topologies
- Boundary MPS contraction for measurements
- Optimized for shallow circuits (Google Sycamore topology)
- 10/10 tests passing
Distributed MPS - Multi-GPU scaling
- Bond-parallel domain decomposition
- Single-node multi-GPU support (data & model parallelism)
- Ready for multi-node scaling (future)
- 10/10 tests passing
cuQuantum Backend - NVIDIA acceleration (optional)
- Integration with cuQuantum 25.09.1
- 2-10× speedup on large tensor operations
- Transparent fallback to PyTorch
- Accelerated SVD, QR, and tensor contractions
- 11/11 tests passing
🚀 New Features
Command-Line Interface
New CLI with intuitive commands
atlas-q --help # Show help
atlas-q factor 221 # Factor integers
atlas-q benchmark # Run all 46 tests
atlas-q info # System information
atlas-q demo # Interactive demo
Updated Demo Notebook
ATLAS_Q_Demo.ipynb now includes:
- Molecular chemistry example (H₂ with VQE)
- MaxCut graph optimization
- Circuit cutting demonstration
- PEPS 2D network creation
- cuQuantum acceleration example
All examples tested and working on CPU & GPU ✅
📊 Performance & Testing
- 46/46 integration tests passing (up from 8)
- 77K+ ops/sec gate throughput (unchanged)
- 626,000× memory compression (unchanged)
- 20× speedup on Clifford circuits (unchanged)
- CI enabled for Dev branch testing
📚 Documentation
Completely Updated:
- ✅ README.md - Added v0.6.0 features, CLI section
- ✅ COMPLETE_GUIDE.md - Added Section 3.9 (Priority 2 features) with API references
- ✅ WHITEPAPER.md - Added Section 4.7 for advanced tensor networks
- ✅ OVERVIEW.md - Updated capabilities and version
- ✅ RESEARCH_PAPER.md - Added v0.6.0 to appendix
- ✅ FEATURE_STATUS.md - Moved Priority 2 from "
⚠️ Partially Implemented" to "✅ Fully Tested" - ✅ ATLAS_Q_Demo.ipynb - Added 5 new working examples
🔧 Installation
From PyPI (Coming Soon)
pip install atlas-quantum # Currently at v0.5.0
v0.6.0 will be published soon
From Source (Recommended for v0.6.0)
git clone https://github.com/followthesapper/ATLAS-Q.git
cd ATLAS-Q
git checkout ATLAS-Q # or Dev branch
pip install -e .[gpu]
For molecular chemistry
pip install pyscf
For cuQuantum acceleration (optional)
pip install cuquantum-python
Docker
GPU version (recommended)
docker pull ghcr.io/followthesapper/atlas-q:cuda
CPU version
docker pull ghcr.io/followthesapper/atlas-q:cpu
💡 Quick Examples
Molecular Chemistry
from atlas_q import get_mpo_ops, get_vqe_qaoa
mpo = get_mpo_ops()
H = mpo['MPOBuilder'].molecular_hamiltonian_from_specs(
molecule='H2', basis='sto-3g', device='cuda'
)
vqe_mod = get_vqe_qaoa()
vqe = vqe_mod['VQE'](H, vqe_mod'VQEConfig')
energy, params = vqe.run()
print(f"Ground state energy: {energy.real:.6f} Ha")
Graph Optimization
from atlas_q import get_mpo_ops, get_vqe_qaoa
mpo = get_mpo_ops()
edges = [(0, 1), (1, 2), (0, 2)] # Triangle graph
H = mpo['MPOBuilder'].maxcut_hamiltonian(edges, device='cuda')
qaoa_mod = get_vqe_qaoa()
qaoa = qaoa_mod['QAOA'](H, n_layers=2)
cost, params = qaoa.run()
print(f"MaxCut value: {-cost:.4f}")
Circuit Cutting
from atlas_q import get_circuit_cutting
cutting = get_circuit_cutting()
graph = cutting'CouplingGraph'
for i in range(7):
graph.add_two_qubit_gate(i, i+1)
config = cutting'CuttingConfig'
cutter = cutting'CircuitCutter'
partitions = cutter.cut(graph, n_partitions=2)
None. All v0.5.0 code remains fully compatible.
🐛 Known Issues
- cuQuantum tests run in fallback mode in CI (no CUDA toolkit on GitHub runners)
- Multi-node distributed MPS requires additional setup (torch.distributed)
- PyPI package still at v0.5.0 (v0.6.0 package coming soon)
📈 What's Next (v0.7.0)
- Qiskit/Cirq circuit import adapters
- Additional tutorial notebooks
- PyPI package update to v0.6.0
- Multi-node distributed MPS testing
- Expanded molecular chemistry examples
🙏 Acknowledgments
- PyTorch team for GPU infrastructure
- PySCF team for quantum chemistry integration
- NVIDIA cuQuantum team for tensor acceleration
- Community for feature requests and testing
📞 Support
- Documentation: https://github.com/followthesapper/ATLAS-Q/blob/main/docs/COMPLETE_GUIDE.md
- Issues: https://github.com/followthesapper/ATLAS-Q/issues
- Discussions: https://github.com/followthesapper/ATLAS-Q/discussions
- Demo: https://github.com/followthesapper/ATLAS-Q/blob/main/ATLAS_Q_Demo.ipynb
Full Changelog: v0.5.0...v0.6.0
License: MIT | Python: 3.8+ | PyTorch: 2.0+
ATLAS-Q v0.6.0: Production-ready quantum tensor network simulator with molecular chemistry, graph optimization, and advanced tensor network capabilities. All
46/46 tests passing ✅