1. Introduction
As quantum computing matures, cloud-based quantum platforms like IBM Quantum, Amazon Braket, Microsoft Azure Quantum, and Xanadu are making quantum resources accessible to researchers and developers. While running quantum jobs on these platforms, monitoring and logging play a critical role in managing performance, diagnosing issues, and optimizing execution.
Just like in classical cloud systems, observability in quantum systems helps ensure smooth operation, performance tuning, and debugging. However, in the quantum domain, these tools must handle hybrid workflows, probabilistic outputs, and hardware-level variability.
2. Why Monitoring and Logging Matter in Quantum Computing
Quantum applications often run:
- On shared, queue-based access to quantum hardware
- With probabilistic results that vary by execution
- Under hardware noise, decoherence, and gate error conditions
This makes it essential to monitor and log:
- Job submissions and queuing
- Backend selection and health
- Quantum circuit depth, width, and runtime
- Execution results and statistical fidelity
- Device-specific metrics (gate errors, readout errors, uptime)
3. Key Monitoring Aspects in Quantum Platforms
a. Job Lifecycle Monitoring
Track job states from submission to completion:
- Queued – Waiting for available hardware
- Running – Executing on backend
- Completed – Finished with results ready
- Failed/Cancelled – Unsuccessful runs due to errors
Most platforms allow programmatic access to job states via APIs or SDKs.
b. Execution Time Metrics
Includes:
- Total runtime
- Time spent in queue
- Gate execution time
- Readout time
This helps optimize circuit design for shorter jobs and cost-efficiency.
c. Quantum Circuit Properties
Monitor:
- Qubit count
- Circuit depth
- Number of gates
- Connectivity constraints
These are useful for understanding hardware fit and scalability.
d. Backend Health and Status
Quantum devices undergo regular calibration. Metrics you can monitor include:
- Gate error rates
- T1 (relaxation) and T2 (decoherence) times
- Readout fidelity
- Qubit connectivity graph
- Uptime/downtime logs
4. Key Logging Aspects in Quantum Platforms
Logs are essential for debugging, analysis, and auditing. They typically include:
a. Submission Logs
- Timestamp of job creation
- Selected backend and configuration
- Circuit structure and metadata
- Classical-quantum parameter values
b. Execution Logs
- Job ID and run token
- Backend state at runtime
- Simulator vs hardware flag
- Errors or warnings encountered during run
c. Result Logs
- Bitstring output distribution
- Number of shots (executions)
- Measurement probabilities
- Variational parameter convergence (for hybrid models)
- Comparison with expected results
d. System/Platform Logs
For platform-wide transparency:
- SDK version info
- API call logs
- Latency logs
- Error stack traces
5. Monitoring & Logging by Platform
IBM Quantum
- IBM Quantum Dashboard: Visual backend status, job queue, and calibration data.
- Qiskit SDK:
job.status(),job.result(), and logging functions provide full traceability. - Quantum Runtime Services: Monitor resource usage in managed runtimes.
Amazon Braket
- CloudWatch Integration: Automatically logs job states, resource usage, and results.
- Braket SDK: Allows fetching logs and monitoring metrics.
- Hybrid Jobs: Logs Python stdout/stderr, parameter sweeps, and model convergence.
Azure Quantum
- Azure Monitor: Integration for tracking runs and infrastructure-level metrics.
- Job Management API: Check status, download logs, and get telemetry.
Xanadu PennyLane / Strawberry Fields
- Xanadu Cloud: Provides logging for remote device access and simulator runs.
- PennyLane Logger: Logs hybrid model training, optimizer states, and loss metrics.
6. Custom Monitoring Techniques
Advanced teams often integrate additional tools:
- Grafana + Prometheus: For visual dashboards
- OpenTelemetry: For standardized observability
- ELK Stack (Elasticsearch, Logstash, Kibana): For deep log analysis
- Slack or email alerts: For job failure or queue timeout notifications
7. Best Practices
- Log job IDs and metadata for reproducibility.
- Monitor backend calibration changes that may affect results.
- Use simulators to baseline expected outputs before deploying on hardware.
- Implement retry logic in code for job failures or queue delays.
- Analyze trends in circuit metrics to reduce depth or gate count.
- Centralize logs using cloud-native tools for auditing and optimization.
8. Future Directions
- Real-time backend telemetry during quantum job execution.
- Anomaly detection systems for unusual measurement results.
- Visualization of circuit performance over multiple runs.
- AI-driven log analytics for quantum model debugging.
- Cross-platform monitoring frameworks that unify IBM, AWS, Azure, etc.