distributed-iqft-communication

---
name: distributed-iqft-communication
description: "Communication-efficient distributed Inverse Quantum Fourier Transform (iQFT) using communication horizon pruning to reduce inter-node quantum communication from O(P^2) to O(P). Use when: distributed quantum computing, iQFT across quantum networks, quantum communication optimization, distributed Shor algorithm, scalable QFT implementation, or quantum network resource allocation."
---

# Communication-Efficient Distributed Inverse Quantum Fourier Transform

Distributed iQFT implementation over quantum networks with communication horizon pruning to reduce inter-node communication complexity from O(P^2) to O(P) (arXiv: 2605.10710).

## Core Problem

Standard iQFT requires O(n^2) gates for n qubits. When qubits are distributed across P nodes (Q qubits each, n = P*Q), non-local controlled-phase rotations between distant nodes incur expensive inter-node quantum communication (e.g., teleportation). This scales as O(P^2) entanglement resources.

## Communication Horizon Strategy

Controlled-phase rotation angles in iQFT decrease exponentially with qubit distance: CR_k has angle 2pi/2^k. Remote controlled-phase gates with small angles contribute negligibly to the final state and can be safely pruned.

### Mathematical Foundation

The iQFT on n qubits applies:
```
|j> -> 1/sqrt(2^n) sum_{k=0}^{2^n-1} exp(2pi*i*j*k/2^n) |k>
```

Circuit decomposition uses Hadamard + controlled-phase gates:
```
H on qubit i, then CR_k from qubit j to i for all j > i, k = j - i + 1
```

For controlled-phase gate CR_k, the rotation angle is 2pi/2^k. When k is large:
- Phase rotation becomes negligible
- Gate impact on fidelity decays exponentially
- Can be safely omitted with bounded error

### Pruning Threshold

Define communication horizon h:
```
h = ceil(-log2(epsilon / (n * sqrt(2))))
```

For target error epsilon, prune all inter-node CR gates where the phase angle < epsilon/(n*sqrt(2)). This bounds the worst-case error.

## Distributed iQFT Algorithm

### Step 1: Partition Qubits
- n qubits partitioned across P nodes
- Each node hosts Q = n/P qubits (assume divisible for simplicity)
- Logical qubit i maps to node floor(i/Q), local index i mod Q

### Step 2: Local iQFT
- Each node performs local iQFT on its Q qubits
- Only involves intra-node gates (no communication)
- Applies H + local CR gates within each node

### Step 3: Inter-node Controlled-Phase Gates
- For each pair of qubits (i, j) on different nodes:
  - Apply CR_{|i-j|+1} gate
  - Requires quantum communication (teleportation or entanglement swapping)
  - Total: O(P^2 * Q^2) inter-node gates without pruning

### Step 4: Communication Horizon Pruning
- Define horizon h based on acceptable error epsilon
- Prune CR gates where controlled-phase angle < threshold
- Remote qubits beyond horizon h are skipped
- Remaining gates: O(P * min(h, n)) per node
- Global complexity: O(P * min(h, n)) = O(P) when h is constant

## Complexity Analysis

| Metric | Standard | With Horizon |
|--------|----------|--------------|
| Gates | O(n^2) | O(n * min(h, n)) |
| Inter-node | O(P^2 * Q^2) | O(P * min(h*Q, n)) |
| Entanglement/node | O(P*Q) | O(min(h*Q, n)) |
| Global comm | O(P^2) | O(P) (h constant) |

## Error Bounds

Total error from pruning is bounded by:
```
||U - U_pruned|| <= n * sqrt(2) * max_theta
```

Where max_theta is the largest pruned rotation angle. For horizon h:
```
max_theta = 2pi / 2^h
```

Choose h to satisfy desired fidelity threshold.

## Implementation Pattern

```python
def distributed_iqft(n_qubits, p_nodes, epsilon=1e-6):
    q_per_node = n_qubits // p_nodes
    h = compute_horizon(epsilon, n_qubits)
    
    for node in range(p_nodes):
        # Local iQFT
        local_iqft(node, q_per_node)
        
        # Inter-node gates with horizon
        for qubit_i in node_qubits(node):
            for qubit_j in all_qubits():
                if same_node(qubit_i, qubit_j):
                    continue
                distance = abs(qubit_i - qubit_j) + 1
                if distance > h:
                    continue  # Prune: negligible contribution
                apply_controlled_phase(qubit_i, qubit_j, distance)
    
    return circuit
```

## When to Use

- Distributed quantum computing architectures
- Quantum networks with multiple QPUs
- Scalable Shor algorithm implementation
- Resource-constrained quantum communication
- Large-scale quantum Fourier transform
- Cross-node quantum algorithm compilation

## Key Advantages

1. **Linear scaling**: Communication O(P) instead of O(P^2)
2. **Bounded error**: Fidelity guarantee from pruning threshold
3. **Hardware-agnostic**: Works with any distributed quantum architecture
4. **Tunable**: Trade accuracy for communication cost via horizon parameter
5. **Composable**: iQFT is a building block for Shor, HHL, and other algorithms

## Related Algorithms

- **Distributed Shor**: iQFT is the final step; communication savings apply
- **Distributed HHL**: Uses QFT subroutine for phase estimation
- **Quantum phase estimation**: Core application of QFT/iQFT
- **Quantum teleportation**: Mechanism for inter-node gate execution

## References

- arXiv: 2605.10710 - Communication-Efficient Distributed Inverse Quantum Fourier Transform
- Authors: F. Javier Cardama, Jorge Vazquez-Perez, Tomas F. Pena, Andres Gomez