#quantum-circuits News & Analysis

4 articles tagged with #quantum-circuits. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AIBullisharXiv – CS AI · Mar 117/10

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A Hybrid Quantum-Classical Framework for Financial Volatility Forecasting Based on Quantum Circuit Born Machines

Researchers developed a hybrid quantum-classical framework combining LSTM neural networks with Quantum Circuit Born Machines for financial volatility forecasting. Testing on Shanghai Stock Exchange data showed significant improvements over classical methods in key metrics like MSE and RMSE, demonstrating quantum computing's potential in financial modeling.

AINeutralarXiv – CS AI · Mar 37/104

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Implementing Pearl's $\mathcal{DO}$-Calculus on Quantum Circuits: A Simpson-Type Case Study on NISQ Hardware

Researchers have developed a method to implement Pearl's causal inference framework (DO-calculus) on quantum circuits, mapping causal networks to quantum hardware through 'circuit surgery.' The approach was successfully demonstrated on IonQ's quantum processor using a healthcare model, showing agreement with classical baselines.

AIBullisharXiv – CS AI · 6d ago6/10

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QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits

Researchers introduce QShield, a hybrid quantum-classical neural network architecture that combines traditional CNNs with quantum processing modules to defend deep learning models against adversarial attacks. Testing on MNIST, OrganAMNIST, and CIFAR-10 datasets shows the hybrid approach maintains accuracy while substantially reducing attack success rates and increasing computational costs for adversaries.

AINeutralarXiv – CS AI · Mar 45/104

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QFlowNet: Fast, Diverse, and Efficient Unitary Synthesis with Generative Flow Networks

Researchers introduce QFlowNet, a novel framework combining Generative Flow Networks with Transformers to solve quantum circuit compilation challenges. The approach achieves 99.7% success rate on 3-qubit benchmarks while generating diverse, efficient quantum gate sequences, addressing key limitations of traditional reinforcement learning methods in quantum computing.