Distributed Qaoa Simulator

---
name: distributed-qaoa-simulator
description: "Distributed Quantum Approximate Optimization Algorithm (DQAOA) simulator for QUBO problems across multiple QPUs. Supports monolithic and distributed QAOA execution modes with configurable QPU capacities, cross-QPU coupling handling, and runtime optimizations. Activation: distributed QAOA, DQAOA simulator, QUBO optimization, multi-QPU quantum, quantum unit commitment"
metadata:
  arxiv_id: "2606.26297"
  published: "2026-06-24"
  authors: "Ali Rajabi, Milad Hasanzadeh, Amin Kargarian"
  tags: [quantum, optimization, distributed-computing, QAOA, QUBO, economics]
---

# Distributed QAOA (DQAOA) Simulator

## Description
Open-source Qiskit-compatible DQAOA simulator for QUBO problems arising in engineering design and economic decision applications. Supports monolithic QAOA on single QPU and distributed QAOA across multiple QPUs with configurable capacities.

## Activation Keywords
- distributed QAOA
- DQAOA simulator
- QUBO multi-QPU
- quantum unit commitment
- distributed quantum optimization
- 分布式量子近似优化
- QUBO分布式求解

## Core Architecture

### Workflow Pipeline
1. **QUBO Canonicalization**: Standardize QUBO model formulation
2. **Cost Hamiltonian Mapping**: Map QUBO to quantum cost Hamiltonian
3. **Variable Allocation**: Distribute variables across QPUs by capacity
4. **Coupling Identification**: Separate local vs cross-QPU couplings
5. **Circuit Construction**: Build corresponding circuits per QPU
6. **Execution & Aggregation**: Run modes and aggregate results

### Runtime Optimizations
- Parameterized circuit reuse (avoid recompilation)
- Objective reuse at fixed depth
- Batched evaluations
- Parallel multi-start execution

### Execution Modes
- **Monolithic**: Single QPU, standard QAOA
- **Distributed**: Multiple QPUs with cross-QPU coupling via remote operations
- **Hybrid**: Classical preprocessing + quantum optimization

## Methodology

### Step 1: QUBO Formulation
Express problem as QUBO: minimize x^T Q x for binary x.

### Step 2: Distributed Allocation
Partition variables across N QPUs based on capacity constraints. Identify:
- Local terms (within single QPU)
- Cross-QPU terms (require remote operations)

### Step 3: Circuit Construction
For each QPU:
- Local cost Hamiltonian → RZZ gates
- Mixer Hamiltonian → RX gates
- Cross-QPU couplings → remote entangling operations

### Step 4: Parameter Optimization
Optimize QAOA angles (γ, β) using classical optimizer.

### Step 5: Solution Recovery
Measure bitstrings, recover optimal solution, compare across modes.

## Usage Patterns

### Pattern 1: Engineering Design Optimization
Apply DQAOA to QUBO-formulated engineering design problems (power generation unit commitment, resource allocation).

### Pattern 2: Economic Decision Optimization
Use for portfolio optimization, scheduling, and other economic problems formulatable as QUBO.

### Pattern 3: QPU Capacity Planning
Simulate different QPU configurations to determine minimum hardware requirements for target problem sizes.

## Pitfalls

### Cross-QPU Communication Overhead
Distributed QAOA is more demanding than monolithic because cross-QPU couplings require remote operations. Evaluate whether distribution actually reduces wall-clock time vs. waiting for larger single QPU.

### Enforcement Cap Limitation
Similar to SCUC paper (2606.26345), distributed QAOA shows coverage bottleneck when enforcement cap no longer spans complete commitment period.

### Qiskit Compatibility
Simulator is Qiskit-compatible — requires Qiskit installation. Not all quantum backends support the required gate set.

### Problem Size Scaling
Distributed approach shines for problems too large for single QPU but requires careful variable partitioning. Poor partitioning → excessive cross-QPU communication → slower than monolithic.

## References
- arXiv: 2606.26297 - "A Distributed Quantum Approximate Optimization Algorithm Simulator for Engineering Design Optimization"
- Related: `qaoa-manifold-optimization` (QAOA parameter optimization)
- Related: `quantum-rl-scuc-qsample` (quantum RL for unit commitment)