Mid-circuit stabilizer verification in six-qubit GSE-encoded Clifford Trotter steps reduces logical error rates by up to 54% on Barium ion hardware, with the gain vanishing if checks are deferred to circuit end.
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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.
The authors present Pilot-Quantum, a middleware for adaptive resource management in hybrid quantum-HPC systems, along with execution motifs and a performance modeling toolkit called Q-Dreamer.
A transpilation framework maps OpenQASM 3.0 dynamic circuits with conditionals and loops to CUDA-Q kernels, reducing depth and improving execution efficiency via direct control-flow translation.
Review of quantum neural networks on gate-based quantum computers for molecular property prediction and generation in drug discovery.
citing papers explorer
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Mid-Circuit Measurements for Clifford Noise Reduction in Hamiltonian Simulations
Mid-circuit stabilizer verification in six-qubit GSE-encoded Clifford Trotter steps reduces logical error rates by up to 54% on Barium ion hardware, with the gain vanishing if checks are deferred to circuit end.
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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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Hybrid Quantum-HPC Middleware Systems for Adaptive Resource, Workload and Task Management
The authors present Pilot-Quantum, a middleware for adaptive resource management in hybrid quantum-HPC systems, along with execution motifs and a performance modeling toolkit called Q-Dreamer.
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Efficient Transpilation of OpenQASM 3.0 Dynamic Circuits to CUDA-Q: Performance and Expressiveness Advantages
A transpilation framework maps OpenQASM 3.0 dynamic circuits with conditionals and loops to CUDA-Q kernels, reducing depth and improving execution efficiency via direct control-flow translation.
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Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries
Review of quantum neural networks on gate-based quantum computers for molecular property prediction and generation in drug discovery.