New techniques for error-independent unified path variation, non-degenerate batched sampling, and flexible contraction accelerate tensor network quantum trajectory simulations by more than 10^8 times.
Efficient techniques to gpu accelera- tions of multi-shot quantum computing simulations
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Target-based prompting lets users define fairness distributions for skin tones in generative AI, shifting outputs closer to chosen targets across 36 tested prompts for occupations and contexts.
Qiskit is an open-source SDK that supports quantum circuit design, optimization at multiple abstraction levels, execution on hardware, and dynamic quantum-classical computations.
citing papers explorer
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Accelerating Quantum Tensor Network Simulations with Unified Path Variations and Non-Degenerate Batched Sampling
New techniques for error-independent unified path variation, non-degenerate batched sampling, and flexible contraction accelerate tensor network quantum trajectory simulations by more than 10^8 times.
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Who Defines Fairness? Target-Based Prompting for Demographic Representation in Generative Models
Target-based prompting lets users define fairness distributions for skin tones in generative AI, shifting outputs closer to chosen targets across 36 tested prompts for occupations and contexts.
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Quantum computing with Qiskit
Qiskit is an open-source SDK that supports quantum circuit design, optimization at multiple abstraction levels, execution on hardware, and dynamic quantum-classical computations.