Quantum distributed computing algorithms based on classical snapshot theory. Extends Chandy-Lamport snapshot to quantum systems for implementing decomposable global quantum operations. Use when designing quantum distributed algorithms, quantum causality analysis, quantum consensus, or quantum snapshot operations. Keywords: quantum distributed systems, QGO algorithm, quantum causality, quantum snapshot, Chandy-Lamport quantum.
Quantum Distributed Snapshot
Overview
Extension of classical distributed computing theory to quantum systems. Implements asynchronous quantum global operations using concepts from the Chandy-Lamport snapshot algorithm.
Source Paper: arXiv:2604.08298 - "Asynchronous Quantum Distributed Computing: Causality, Snapshots, and Global Operations"
Core Concepts
1. Quantum Distributed Systems
Definition: Network of quantum processors that can:
Perform local quantum operations
Send/receive quantum messages (qubits)
Maintain quantum coherence across distributed nodes
Handle asynchronous communication
Challenge: Entanglement breaks classical causality assumptions. A global quantum state may not manifest causality from its standard description.
2. Decomposable Global Quantum Operations
Key Concept: Global quantum operations that can be decomposed into local operations on components.
Example: Quantum snapshot - instantaneously measure the whole system.
Based on Chandy-Lamport's classical snapshot algorithm.
Algorithm Steps:
# Conceptual algorithmdefQGO_algorithm(nodes, channels):
"""
Implement decomposable global quantum operation.
Based on Chandy-Lamport snapshot:
1. Initiator node records local state
2. Sends marker messages along all outgoing channels
3. Upon receiving marker:
- If first marker: record local state, forward markers
- If already recorded: record channel state
4. Collect all local and channel states
5. Combine to form global operation result
"""# Initiator
initiator = select_node()
# Record local quantum state
local_states = {}
local_states[initiator] = measure_local(initiator)
# Send quantum markersfor channel in outgoing_channels(initiator):
send_marker(channel)
# Process incoming markers (recursive)whilenot all_states_recorded():
process_markers()
# Combine local operations to form global operation
global_result = combine_local_ops(local_states, channel_states)
return global_result
