mpstab: a cutting-edge quantum circuit simulator

mpstab is an open-source Python library designed for high-performance simulation of quantum circuits using a hybrid Stabilizer-Matrix Product Operator (HSMPO) formalism.

By combining the efficiency of Clifford stabilizers with the expressiveness of Tensor Networks, mpstab allows researchers to simulate circuits that are “mostly Clifford” but contain non-trivial non-stabilizer resources. The hybrid stabilizer-MPO representation was introduced in this work from Mello, Santini and Collura.

Core Features

The HSMPO formalism is implemented here exploiting, as operative engines, the state-of-art stabilizers and tensor network simulators. Among our core features:

• Hybrid Evolution: Seamlessly combine stabilizer and tensor network engines for efficient simulation.

• Qibo Integration: Native custom backend support, allowing you to run Qibo circuits using mpstab algorithms. Quantum circuit and observables definitions from Qibo are supported as well.

• Scalable Clifford Simulation: Simulate pure Clifford circuits with hundreds of qubits.

• Advanced Metrics: Built-in tools for computing entanglement entropies, fidelity bounds, and circuit analysis.

Getting Started

• Installation
  • Prerequisites
  • Quick Install
  • Development Installation
  • Dependency Groups
  • Verification
• Theoretical Background, a snapshot
  • Classical Simulation Approaches
  • Other Hybrid Techniques
  • The HSMPO Ansatz
  • Evolution Rules
  • Computational Complexity and Costs
  • Numerical Truncation and Fidelity
• Quick Start Guide
  • Your First MPSTAB Program
  • Simulate with Bond Dimension Limit
  • Use an Ansatz
  • Measure Complex Observables

Tutorials & Examples

• A quick introduction to HSMPO
  • A first simple circuit
  • Simulating a Clifford circuit
• MPSTAB as a Qibo Backend Provider
  • What is a Backend Provider?
  • Computing Hamiltonian Expectation Values
• Working with Engines
  • Available Engines
  • Engine Configuration
  • Tips for Efficient Simulation
  • Example: Adaptive Bond Dimension
  • Further Reading
• Using Ansätze
  • Available Ansätze
  • Building Custom Ansätze
  • The ansatz partitioning
  • Further Reading
• Fidelity and Approximation
  • Concept: Fidelity Lower Bound
  • Accessing Fidelity Information
  • Interpreting Fidelity Values
  • Benchmarking: Hybrid vs Pure Tensor Network
  • Practical Guidelines
  • Sources of Fidelity Loss
  • Further Reading

API Reference

• Evolutors
  • HSMPO: Hybrid Stabilizer MPO
  • Utilities
  • Stabilizer Evolution Tools
  • Tensor Network Evolution Tools
• Engines
  • Stabilizer Engines
  • Tensor Network Engines
  • Engine Comparison
  • Selecting Engines
  • Engine Interface
  • Performance Tuning
  • See Also
• Models & Methods
  • Ansätze: Quantum Circuit Templates
  • Error Mitigation
  • Entanglement Metrics & Analysis
  • Additional Utilities
  • Quick Reference
  • Related Documentation

Indices and tables

• Index

• Module Index

• Search Page