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Found 20 papers, 1 papers with code
Guardians of the Quantum GAN
We obtain watermark extraction accuracy of 100% and ~90% for training the qGAN on individual and multiple quantum hardware setups (and inferencing on different hardware), respectively.
Application of Quantum Tensor Networks for Protein Classification
We show that protein sequences can be thought of as sentences in natural language processing and can be parsed using the existing Quantum Natural Language framework into parameterized quantum circuits of reasonable qubits, which can be trained to solve various protein-related machine-learning problems.
AltGraph: Redesigning Quantum Circuits Using Generative Graph Models for Efficient Optimization
Rule-based approaches are used in quantum compilers such as Qiskit, tket, and Quilc.
Evaluating Efficacy of Model Stealing Attacks and Defenses on Quantum Neural Networks
In this study, we assess the efficacy of such attacks in the realm of quantum computing.
DyPP: Dynamic Parameter Prediction to Accelerate Convergence of Variational Quantum Algorithms
The exponential run time of quantum simulators on classical machines and long queue times and high costs of real quantum devices present significant challenges in the efficient optimization of Variational Quantum Algorithms (VQAs) like Variational Quantum Eigensolver (VQE), Quantum Approximate Optimization Algorithm (QAOA) and Quantum Neural Networks (QNNs).
Random Relabeling for Efficient Machine Unlearning
Learning algorithms and data are the driving forces for machine learning to bring about tremendous transformation of industrial intelligence.
Predicting Side Effect of Drug Molecules using Recurrent Neural Networks
Identification and verification of molecular properties such as side effects is one of the most important and time-consuming steps in the process of molecule synthesis.
Shot Optimization in Quantum Machine Learning Architectures to Accelerate Training
We use a (a) linear function where the number of shots reduce linearly with epochs, and (b) step function where the number of shots reduce in step with epochs.
Energy-based Generative Models for Target-specific Drug Discovery
Drug targets are the main focus of drug discovery due to their key role in disease pathogenesis.
Quantum Machine Learning for Material Synthesis and Hardware Security
Using quantum computing, this paper addresses two scientifically pressing and day-to-day relevant problems, namely, chemical retrosynthesis which is an important step in drug/material discovery and security of the semiconductor supply chain.
Analysis of Power-Oriented Fault Injection Attacks on Spiking Neural Networks
We investigate the impact of the attacks on digit classification tasks and find that in the worst-case scenario, classification accuracy is reduced by 85. 65%.
Security Aspects of Quantum Machine Learning: Opportunities, Threats and Defenses
In the last few years, quantum computing has experienced a growth spurt.
DeepQMLP: A Scalable Quantum-Classical Hybrid DeepNeural Network Architecture for Classification
In DeepQMLP, stacked shallow Quantum Neural Network (QNN) models mimic the hidden layers of a classical feed-forward multi-layer perceptron network.
Scalable Variational Quantum Circuits for Autoencoder-based Drug Discovery
The architectural strategies in hybrid quantum classical networks such as, adjustable quantum layer depth, heterogeneous learning rates, and patched quantum circuits are proposed to learn high dimensional dataset such as, ligand-targeted drugs.
Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)
We review two proposals on quantum-classical hybrid ML models for image classification namely, Quanvolutional Neural Network and dimension reduction using a classical algorithm followed by QNN.
Drug Discovery Approaches using Quantum Machine Learning
Traditional drug discovery pipeline takes several years and cost billions of dollars.
Quantum Generative Models for Small Molecule Drug Discovery
We propose a qubit-efficient quantum GAN with a hybrid generator (QGAN-HG) to learn richer representation of molecules via searching exponentially large chemical space with few qubits more efficiently than classical GAN.
Quantum-soft QUBO Suppression for Accurate Object Detection
Non-maximum suppression (NMS) has been adopted by default for removing redundant object detections for decades.
Accelerating Quantum Approximate Optimization Algorithm using Machine Learning
As a result, the optimization loop converges in a fewer number of iterations.
Analysis of Quantum Approximate Optimization Algorithm under Realistic Noise in Superconducting Qubits
In QAOA, the objective is a function of the quantum state, which itself is a function of the gate parameters of a multi-level parameterized quantum circuit (PQC).
Quantum Physics Emerging Technologies
