Real-Time Quantum Experiments with Qiskit Runtime: Accelerating Hybrid Workflows on IBM QPUs

Table of Contents

1. Introduction
2. What Is Qiskit Runtime?
3. Benefits of Qiskit Runtime for Real-Time Experiments
4. Architectural Overview
5. Supported Quantum Algorithms and Use Cases
6. Runtime Programs: Prebuilt and Custom
7. Setting Up Access to Qiskit Runtime
8. Qiskit Runtime Primitives: Estimator and Sampler
9. Creating a Real-Time Quantum Experiment Workflow
10. Example: Variational Circuit Using Estimator
11. Running Experiments on Dedicated IBM QPUs
12. Managing Sessions and Runtime Jobs
13. Monitoring Job Progress and Logs
14. Result Retrieval and Error Handling
15. Integrating Classical Feedback in Runtime
16. Runtime Performance and Cost Considerations
17. Writing Custom Qiskit Runtime Programs
18. Debugging and Testing Runtime Programs
19. Reproducibility and Metadata Logging
20. Conclusion

1. Introduction

Qiskit Runtime enables real-time quantum-classical hybrid experiments by reducing latency between quantum executions and classical computations. It empowers researchers to run more efficient and scalable algorithms on IBM quantum devices.

2. What Is Qiskit Runtime?

A container-based execution environment hosted on IBM Cloud that accelerates quantum workloads by running them close to the QPU and within a single session.

3. Benefits of Qiskit Runtime for Real-Time Experiments

• Up to 100x speedup vs traditional workflows
• Lower roundtrip latency
• State persistence across iterations
• Cost-efficient execution for large-scale variational algorithms

4. Architectural Overview

• Client: Python script using qiskit-ibm-runtime
• Runtime container: runs prebuilt or custom programs
• Backend: IBM QPU or simulator

5. Supported Quantum Algorithms and Use Cases

• VQE, QAOA, QNNs
• Ground state estimation
• Kernel estimation
• Variational optimization

6. Runtime Programs: Prebuilt and Custom

• Prebuilt: sampler, estimator, vqe, qaoa
• Custom: define your own loop logic and operations

7. Setting Up Access to Qiskit Runtime

from qiskit_ibm_runtime import QiskitRuntimeService
service = QiskitRuntimeService(channel="ibm_quantum", token="YOUR_TOKEN")

8. Qiskit Runtime Primitives: Estimator and Sampler

from qiskit_ibm_runtime import Estimator, Session
with Session(service=service, backend="ibmq_qasm2") as session:
    estimator = Estimator(session=session)
    job = estimator.run(circuit, observables)

9. Creating a Real-Time Quantum Experiment Workflow

• Define circuit and observable
• Set runtime options (shots, optimization level)
• Loop within session for parameter updates

10. Example: Variational Circuit Using Estimator

from qiskit.circuit import Parameter
theta = Parameter("θ")
circuit = QuantumCircuit(1)
circuit.rx(theta, 0)

11. Running Experiments on Dedicated IBM QPUs

Use Premium Plan for lower queue times and reserved access.

12. Managing Sessions and Runtime Jobs

• Sessions can contain multiple job executions
• Monitor with session.jobs and job.status()

13. Monitoring Job Progress and Logs

job = estimator.run(...)
job.result()
job.status()
job.logs()

14. Result Retrieval and Error Handling

• Retry failed jobs
• Handle network exceptions
• Use callback functions for streaming output

15. Integrating Classical Feedback in Runtime

Within a session, use:

• Intermediate classical calculations
• Optimizer feedback for parameter tuning

16. Runtime Performance and Cost Considerations

• Runtime execution incurs per-second billing
• Sessions reduce roundtrip API calls

17. Writing Custom Qiskit Runtime Programs

• Create main() entry with ProgramContext
• Package and deploy via IBM Quantum Dashboard

18. Debugging and Testing Runtime Programs

• Use local simulator for testing
• Log intermediate data
• Validate output consistency

19. Reproducibility and Metadata Logging

• Record session ID, backend, transpiled circuit hash
• Log optimizer state and iteration data

20. Conclusion

Qiskit Runtime revolutionizes real-time quantum experimentation by offering faster, more integrated, and scalable workflows. Whether you’re running variational algorithms or real-time kernel estimation, this environment makes quantum computing more practical and powerful.