Implementing Quantum Machine Learning on Real Hardware: From Simulation to Execution

Table of Contents

1. Introduction
2. Why Run QML on Real Quantum Hardware?
3. Understanding NISQ Hardware Constraints
4. Hardware Providers and Access Models
5. QML-Friendly Devices: IBM, IonQ, Rigetti, OQC
6. Circuit Depth, Qubit Count, and Connectivity
7. Choosing a Suitable QML Model
8. Preprocessing for Hardware Execution
9. Shot Management and Execution Time
10. Noise-Aware Model Design
11. Noise Models and Mitigation Techniques
12. IBM Qiskit Hardware Integration
13. PennyLane with Amazon Braket and IBM QPU
14. QML Example on IBM Quantum: Variational Classifier
15. Circuit Optimization and Transpilation
16. Benchmarking Results from Real Hardware
17. Practical Considerations: Queue, Calibration, Costs
18. Error Mitigation Techniques
19. Lessons Learned and Best Practices
20. Conclusion

1. Introduction

Quantum machine learning (QML) can now be deployed on real quantum hardware thanks to advances in cloud-based quantum computing platforms. This article explains how to implement QML models on physical devices and discusses practical challenges and solutions.

2. Why Run QML on Real Quantum Hardware?

• Validate simulation results
• Understand real-world noise and performance
• Explore near-term quantum advantage possibilities

3. Understanding NISQ Hardware Constraints

• Limited qubit count
• Gate fidelity issues
• Short coherence times
• Circuit depth and connectivity limitations

4. Hardware Providers and Access Models

• IBM Quantum: Free and premium access
• Amazon Braket: Pay-per-use for IonQ, Rigetti, OQC
• Azure Quantum: Offers Q# and third-party access

5. QML-Friendly Devices: IBM, IonQ, Rigetti, OQC

• IBM: superconducting qubits, well-integrated with Qiskit
• IonQ: trapped ions, high fidelity, all-to-all connectivity
• Rigetti: superconducting, QPU via Braket
• OQC: photonic platform, accessible via Braket

6. Circuit Depth, Qubit Count, and Connectivity

• Use fewer qubits and shallower circuits
• Design circuits respecting connectivity topology
• Optimize gate layout during compilation

7. Choosing a Suitable QML Model

• Variational Quantum Classifier (VQC)
• Quantum kernel methods
• Quantum autoencoders (experimental)

8. Preprocessing for Hardware Execution

• Normalize data
• Use low-dimensional inputs
• Encode data with angle or basis encoding

9. Shot Management and Execution Time

• Use 1024–8192 shots for stability
• More shots → better average, longer wait time
• Batching jobs can save queue time

10. Noise-Aware Model Design

• Minimize entanglement and gate count
• Choose error-resilient ansatz (e.g., shallow circuits)
• Avoid long idle times between operations

11. Noise Models and Mitigation Techniques

• Readout error mitigation
• Zero-noise extrapolation
• Measurement error calibration

12. IBM Qiskit Hardware Integration

from qiskit_ibm_provider import IBMProvider
provider = IBMProvider()
backend = provider.get_backend("ibmq_quito")

13. PennyLane with Amazon Braket and IBM QPU

import pennylane as qml
dev = qml.device("braket.aws.qubit", device_arn="arn:aws:...")

14. QML Example on IBM Quantum: Variational Classifier

• Use ZZFeatureMap + TwoLocal ansatz
• Optimizer: SPSA or COBYLA
• Evaluate using Aer simulator then run on backend

15. Circuit Optimization and Transpilation

from qiskit.transpiler import PassManager
from qiskit.transpiler.passes import Optimize1qGates
pass_manager = PassManager([Optimize1qGates()])

16. Benchmarking Results from Real Hardware

• Measure accuracy on test set
• Compare to simulation performance
• Use fidelity or KL divergence as metrics

17. Practical Considerations: Queue, Calibration, Costs

• Use lowest-load backend
• Check calibration dashboard
• Estimate cost if using Braket (e.g., per-task rate)

18. Error Mitigation Techniques

• Use qiskit.ignis for measurement error mitigation
• Use multiple runs with different transpilation seeds

19. Lessons Learned and Best Practices

• Simulate thoroughly before submitting to real QPU
• Batch jobs and optimize circuits to save time and cost
• Always compare results with noisy simulator baseline

20. Conclusion

Running QML models on real quantum hardware is both feasible and insightful. With thoughtful design, noise mitigation, and platform integration, researchers can move beyond simulation and explore how quantum models behave in real-world scenarios.