Table of Contents

1. Introduction
2. Why Use Qiskit for QML?
3. Qiskit Machine Learning Overview
4. Installing and Setting Up Qiskit ML
5. Qiskit Data Encoding Techniques
6. Feature Map Circuits for Classification
7. Variational Quantum Classifiers (VQC)
8. Building a Simple Quantum Classifier
9. Training and Evaluation
10. Using Quantum Kernels with SVM
11. Multiclass Classification Strategies
12. Regression with Quantum Circuits
13. Hardware-Aware Simulation in Qiskit Aer
14. Running QML Models on Real IBM Quantum Hardware
15. Integrating Classical Preprocessing with QML
16. Visualizing Quantum Decision Boundaries
17. Parameter Shift Gradients and Optimization
18. Challenges and Best Practices
19. Applications and Case Studies
20. Conclusion

1. Introduction

Qiskit is IBM’s open-source quantum computing SDK. Its qiskit-machine-learning module provides tools to build, train, and evaluate quantum machine learning models using simulators or real quantum hardware.

2. Why Use Qiskit for QML?

• Direct access to IBM QPUs
• Integration with Qiskit Terra and Aer
• Native support for quantum feature maps, VQCs, and kernels
• Strong documentation and community support

3. Qiskit Machine Learning Overview

• Core components include quantum classifiers, regressors, and kernel-based learners
• Seamless integration with NumPy, SciKit-learn, and Qiskit Aer backends

4. Installing and Setting Up Qiskit ML

pip install qiskit qiskit-machine-learning

5. Qiskit Data Encoding Techniques

• Feature maps transform classical data into quantum states
• Common encodings: ZZFeatureMap, PauliFeatureMap, ZFeatureMap

6. Feature Map Circuits for Classification

from qiskit.circuit.library import ZZFeatureMap
feature_map = ZZFeatureMap(feature_dimension=2, reps=2)

7. Variational Quantum Classifiers (VQC)

• Learn trainable quantum parameters to minimize classification loss
• Combine feature map with variational ansatz

8. Building a Simple Quantum Classifier

from qiskit_machine_learning.algorithms import VQC
from qiskit_machine_learning.circuit.library import RawFeatureVector
from qiskit.circuit.library import TwoLocal

ansatz = TwoLocal(2, ['ry', 'rz'], 'cz', reps=3)
vqc = VQC(feature_map=feature_map, ansatz=ansatz, optimizer='SPSA')

9. Training and Evaluation

• Use datasets like Iris, Breast Cancer, or synthetic XOR
• Split data and train using .fit() and .score() methods

10. Using Quantum Kernels with SVM

from qiskit_machine_learning.kernels import QuantumKernel
from sklearn.svm import SVC

qkernel = QuantumKernel(feature_map=feature_map)
kernel_matrix = qkernel.evaluate(x_train, x_train)
svc = SVC(kernel='precomputed').fit(kernel_matrix, y_train)

11. Multiclass Classification Strategies

• One-vs-Rest or One-vs-One approaches with VQC or Quantum SVM
• Wrap QML model inside sklearn.multiclass.OneVsRestClassifier

12. Regression with Quantum Circuits

• Use qiskit_machine_learning.algorithms.VQR for variational quantum regression

13. Hardware-Aware Simulation in Qiskit Aer

from qiskit_aer import AerSimulator
sim = AerSimulator(noise_model=noise, method='statevector')

14. Running QML Models on Real IBM Quantum Hardware

• Log in to IBMQ:

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

15. Integrating Classical Preprocessing with QML

• Standardize or normalize input features
• Combine with PCA or feature selection before encoding

16. Visualizing Quantum Decision Boundaries

• Plot fidelity heatmaps or measurement probability landscapes
• Use matplotlib and Qiskit circuit sampling

17. Parameter Shift Gradients and Optimization

• Support for analytic gradients using parameter-shift rule
• Optimizers: SPSA, COBYLA, L-BFGS-B

18. Challenges and Best Practices

• Qubit count and circuit depth affect accuracy
• Use shallow ansatz for NISQ devices
• Avoid overfitting via regularization or shot averaging

19. Applications and Case Studies

• Quantum-enhanced finance and healthcare prediction
• Quantum kernel for molecule property classification

20. Conclusion

Qiskit offers a versatile and accessible platform for experimenting with quantum machine learning. From simulators to real hardware, its tools support rapid prototyping and evaluation of quantum-enhanced models across a wide range of domains.

Table of Contents

1. Introduction
2. Why Software Frameworks Matter in QML
3. Overview of QML Framework Categories
4. PennyLane: A Hybrid Quantum-Classical Framework
5. Core Features of PennyLane
6. Supported Interfaces and Backends
7. Example Workflow in PennyLane
8. TensorFlow Quantum (TFQ): Deep Learning Meets Quantum
9. Core Features of TFQ
10. TFQ Integration with TensorFlow
11. Example Workflow in TFQ
12. Qiskit Machine Learning
13. Cirq and Quantum Programming with Google Tools
14. Amazon Braket SDK
15. Microsoft Q# and QDK
16. ProjectQ and Other Lightweight Frameworks
17. Comparative Table: PennyLane vs TFQ vs Others
18. Choosing the Right Framework for Your Use Case
19. Community and Ecosystem Support
20. Conclusion

1. Introduction

Quantum machine learning (QML) frameworks provide essential tools for building, training, and simulating quantum-enhanced models. They bridge quantum hardware with machine learning libraries, making QML accessible to researchers and developers.

2. Why Software Frameworks Matter in QML

• Abstract away quantum hardware complexities
• Enable hybrid classical-quantum programming
• Provide tools for optimization, visualization, and deployment

3. Overview of QML Framework Categories

• Hybrid frameworks: support classical-quantum integration (PennyLane, TFQ)
• Quantum-native frameworks: focus purely on quantum simulation and programming (Qiskit, Cirq, Q#)
• Backend-agnostic tools: allow switching between simulators and real quantum hardware

4. PennyLane: A Hybrid Quantum-Classical Framework

Developed by Xanadu, PennyLane enables automatic differentiation of quantum circuits and integrates smoothly with classical ML libraries.

5. Core Features of PennyLane

• Hybrid quantum-classical optimization
• Interfaces with PyTorch, TensorFlow, JAX
• Supports gradient-based training with parameter-shift rules
• Plug-and-play with hardware (via Qiskit, Amazon Braket, etc.)

6. Supported Interfaces and Backends

• Classical: PyTorch, TensorFlow, JAX
• Quantum: Strawberry Fields, Qiskit, Braket, Cirq, Rigetti

7. Example Workflow in PennyLane

import pennylane as qml
from pennylane import numpy as np

dev = qml.device("default.qubit", wires=2)

@qml.qnode(dev)
def circuit(params):
    qml.RX(params[0], wires=0)
    qml.CNOT(wires=[0, 1])
    return qml.expval(qml.PauliZ(1))

params = np.array([0.54], requires_grad=True)
result = circuit(params)

8. TensorFlow Quantum (TFQ): Deep Learning Meets Quantum

TFQ is a joint project by Google and TensorFlow for building quantum ML models that integrate directly with TensorFlow’s data pipeline and training loop.

9. Core Features of TFQ

• Uses Cirq for circuit construction
• Fully compatible with TensorFlow 2.x
• Includes quantum layers like tfq.layers.PQC
• Supports batching and hybrid quantum-classical models

10. TFQ Integration with TensorFlow

• Seamless integration with tf.keras API
• Use of classical optimizers for training quantum circuits
• Suitable for large-scale deep learning integration

11. Example Workflow in TFQ

import cirq
import tensorflow as tf
import tensorflow_quantum as tfq

qubit = cirq.GridQubit(0, 0)
circuit = cirq.Circuit(cirq.X(qubit)**0.5)

model_input = tf.keras.Input(shape=(), dtype=tf.dtypes.string)
quantum_layer = tfq.layers.PQC(circuit, cirq.Z(qubit))(model_input)

12. Qiskit Machine Learning

• Part of IBM’s Qiskit ecosystem
• Provides variational quantum classifiers, regressors, and quantum kernels
• Compatible with Aer simulator and IBM Quantum hardware

13. Cirq and Quantum Programming with Google Tools

• Low-level circuit definition and execution
• Basis for TFQ and Sycamore hardware programs
• Good for research-level quantum circuit manipulation

14. Amazon Braket SDK

• Provides access to simulators and real QPUs (IonQ, Rigetti, OQC)
• Python SDK for defining circuits and managing jobs
• Supports hybrid workflows via PennyLane and PyTorch

15. Microsoft Q# and QDK

• Domain-specific language for quantum programming
• Rich libraries for quantum chemistry and simulation
• Less ML-focused but useful for custom quantum algorithm design

16. ProjectQ and Other Lightweight Frameworks

• Simpler interface for fast prototyping
• Good for educational use and circuit visualization

17. Comparative Table: PennyLane vs TFQ vs Others

18. Choosing the Right Framework for Your Use Case

• For hybrid ML research: PennyLane
• For TensorFlow-based ML: TFQ
• For IBM QPU access: Qiskit ML
• For Google Sycamore: Cirq/TFQ

19. Community and Ecosystem Support

• PennyLane: Active GitHub, forums, QHack community
• TFQ: Backed by TensorFlow and Cirq teams
• Qiskit: IBM-supported open-source initiative

20. Conclusion

QML frameworks are vital for making quantum machine learning accessible and practical. Whether using PennyLane for hybrid deep learning or TFQ for native TensorFlow integration, these tools accelerate the development of quantum-enhanced AI systems.

Table of Contents

1. Introduction
2. Importance of Security in ML Pipelines
3. Quantum ML: New Threat Landscape
4. Attack Surfaces in Quantum ML Systems
5. Adversarial Attacks on Quantum Models
6. Parameter Manipulation in Variational Circuits
7. Data Poisoning in Quantum Datasets
8. Model Inversion Attacks on Quantum Outputs
9. Quantum Side-Channel Attacks
10. Secure Data Encoding and Preprocessing
11. Privacy-Preserving Quantum Computation
12. Differential Privacy in QML
13. Federated QML and Secure Aggregation
14. Secure Parameter Transmission
15. Access Control for Quantum Resources
16. Trusted Execution Environments for QML
17. Post-Quantum Cryptography for QML Pipelines
18. Best Practices in Secure Quantum ML Design
19. Research Challenges and Open Questions
20. Conclusion

1. Introduction

As quantum machine learning (QML) systems mature and are integrated into practical applications, securing the end-to-end quantum ML pipeline becomes critical. Threats span from data ingestion to quantum circuit execution and result reporting.

2. Importance of Security in ML Pipelines

• Ensure integrity and confidentiality of data and models
• Prevent manipulation or theft of learned parameters
• Guarantee trusted outcomes in adversarial settings

3. Quantum ML: New Threat Landscape

• Combines vulnerabilities of classical ML with quantum-specific threats
• Novel exploits from entanglement, decoherence, and measurement
• Hardware and algorithmic complexity increase the attack surface

4. Attack Surfaces in Quantum ML Systems

• Data encoding and preprocessing modules
• Quantum circuit generation and compilation
• Parameter aggregation and communication
• Post-processing and decision systems

5. Adversarial Attacks on Quantum Models

• Perturbation of inputs or gate parameters
• Gradient-based attacks on variational circuits
• White-box or black-box adversarial inference

6. Parameter Manipulation in Variational Circuits

• Malicious alterations to VQC weights or entanglement layout
• Could lead to misclassification or system compromise
• Detection requires parameter integrity checks

7. Data Poisoning in Quantum Datasets

• Injected corrupted samples during training
• Target specific classes to bias circuit outputs
• Dangerous in federated or crowd-sourced data settings

8. Model Inversion Attacks on Quantum Outputs

• Reconstruct input features from QML outputs
• Exploit measurement-based leakage or response patterns

9. Quantum Side-Channel Attacks

• Leakage via timing, power, or photonic signatures
• Measurement timing could correlate with sensitive inputs
• Under-explored but feasible with future hardware

10. Secure Data Encoding and Preprocessing

• Verify input normalization and encoding routines
• Use encryption-based methods for sensitive data

11. Privacy-Preserving Quantum Computation

• Blind quantum computation protocols
• Delegate computation without revealing input or model

12. Differential Privacy in QML

• Add noise to gradients or circuit outputs
• Ensure that single training samples do not influence overall model

13. Federated QML and Secure Aggregation

• Clients train locally, share parameters
• Apply secure aggregation protocols
• Prevent inference on shared quantum model updates

14. Secure Parameter Transmission

• Use TLS or quantum key distribution (QKD)
• Encrypt parameter updates between client and server

15. Access Control for Quantum Resources

• Authenticate access to quantum simulators or QPUs
• Limit circuit compilation rights and resource quotas

16. Trusted Execution Environments for QML

• Use secure enclaves or containers for classical-quantum coordination
• Validate and attest to integrity of QML runtime

17. Post-Quantum Cryptography for QML Pipelines

• Use lattice-based or code-based cryptographic primitives
• Defend classical components against future quantum attacks

18. Best Practices in Secure Quantum ML Design

• Zero-trust design principles
• Audit trails and logging
• Redundancy and anomaly detection in circuit outputs

19. Research Challenges and Open Questions

• Formal threat models for QML pipelines
• Benchmarking robustness of quantum models
• Verifiable training and inference protocols

20. Conclusion

Securing quantum ML pipelines requires a multi-layered approach encompassing data protection, model robustness, secure computation, and hardware integrity. As QML systems advance, integrating security from design to deployment will be essential for trustworthy and resilient quantum AI.

Table of Contents

1. Introduction
2. What Are Adversarial Attacks?
3. Motivation for Studying Attacks in QML
4. Classical Adversarial Attacks: A Brief Overview
5. Unique Vulnerabilities in Quantum Models
6. Types of Adversarial Attacks in QML
7. Perturbation of Input States
8. Parameter Perturbation Attacks
9. Attacks on Quantum Feature Maps
10. Fidelity-Based Adversarial Examples
11. White-Box vs Black-Box Attacks in QML
12. Gradient-Based Attacks on VQCs
13. Adversarial Examples for Quantum Classifiers
14. Transferability of Adversarial Examples in QML
15. Robustness of Quantum Kernels
16. Defending Quantum Models Against Attacks
17. Quantum Regularization Techniques
18. Noise as a Double-Edged Sword
19. Open Problems and Research Challenges
20. Conclusion

1. Introduction

Adversarial attacks pose a significant threat to classical machine learning systems. As quantum machine learning (QML) becomes more widespread, understanding its vulnerabilities to similar attacks becomes critical to building robust and trustworthy quantum AI systems.

2. What Are Adversarial Attacks?

• Deliberate perturbations to input data that mislead a model
• Examples include imperceptible noise added to images or signals
• Goal: fool the model without obvious change to the input

3. Motivation for Studying Attacks in QML

• QML systems may be deployed in high-stakes environments
• Quantum systems are inherently noisy and hard to interpret
• Understanding adversarial risks is key to security and trust

4. Classical Adversarial Attacks: A Brief Overview

• FGSM (Fast Gradient Sign Method)
• PGD (Projected Gradient Descent)
• CW (Carlini-Wagner) and decision-based attacks

5. Unique Vulnerabilities in Quantum Models

• Quantum data representations are fragile
• Superposition and entanglement introduce novel dependencies
• Observability limits complicate detection

6. Types of Adversarial Attacks in QML

• Input-level perturbations to quantum states
• Gate-level or parameter-level attacks on circuits
• Attacks on measurement process or shot-noise exploitation

7. Perturbation of Input States

• Modify amplitude or angle encoded states
• Small shifts in encoded features can lead to large output deviations
• Adversarial states may look similar but induce different measurement outcomes

8. Parameter Perturbation Attacks

• Add noise to trained gate parameters in VQCs
• Target high-sensitivity regions in the optimization landscape

9. Attacks on Quantum Feature Maps

• Exploit vulnerabilities in quantum kernels
• Manipulate classical data such that mapped quantum states are indistinguishable

10. Fidelity-Based Adversarial Examples

• Minimize fidelity between original and perturbed quantum state
• Objective: maximize classification error while maintaining quantum closeness

11. White-Box vs Black-Box Attacks in QML

• White-box: attacker has full circuit and parameter access
• Black-box: only circuit outputs are accessible
• Gradient estimation via parameter-shift rule enables black-box gradient attacks

12. Gradient-Based Attacks on VQCs

• Use parameter-shift gradients to compute adversarial directions
• Similar to FGSM, perturb input parameters or circuit angles

13. Adversarial Examples for Quantum Classifiers

• Constructed using hybrid loss maximization
• Simulated using Qiskit or PennyLane
• Demonstrated on quantum-enhanced image or time series classifiers

14. Transferability of Adversarial Examples in QML

• Do examples crafted for one quantum model fool another?
• Transfer effects studied across kernel-based and variational circuits

15. Robustness of Quantum Kernels

• Some quantum kernels are more robust to small data perturbations
• Analyze based on the sensitivity of kernel matrix eigenvalues

16. Defending Quantum Models Against Attacks

• Adversarial training (inject noisy or adversarial samples)
• Gradient masking (limit differentiability)
• Circuit randomization and dropout

17. Quantum Regularization Techniques

• Add penalty terms to loss function for sensitivity control
• Train using noise-injected circuits for generalization

18. Noise as a Double-Edged Sword

• May obscure gradients, making attacks harder
• Also destabilizes learning and increases variance

19. Open Problems and Research Challenges

• Formal adversarial bounds for QML models
• Scalable attack algorithms for large QPU systems
• Security standards for quantum AI applications

20. Conclusion

Adversarial attacks represent an emerging frontier in the security of quantum machine learning. As quantum AI systems mature, building robust, interpretable, and attack-resistant models will be vital to ensuring the reliability of quantum-enhanced decision-making.