MCMit proposes a constant-latency multi-control branch instruction, transformer and CNN discriminators, plus static MCM elimination and stochastic branching, evaluated on Qubic with QPU traces to cut latency by 70% and logical error rates by up to 9.4x.
The future of quantum computing with superconducting qubits
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An algorithm is presented for estimating distribution complexity of electronic structure Hamiltonians, with O(N^3) entanglement estimation per fragment and quadratic/exponential reductions in distribution cost for quantum and classical interconnects.
QADR decomposes n-qubit VQCs into local sub-circuits to reduce memory from O(2^n) to O(n * 2^{2d+1}) and mitigate barren plateaus, scaling to 2000 features on MNIST and wind turbine diagnostics while matching classical models.
A review summarizing superconducting qubit types, DiVincenzo criteria implementations, coherence limits from defects, and large-scale integration strategies for quantum computing.
citing papers explorer
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MCMit: Mid-Circuit Measurement Error Mitigation
MCMit proposes a constant-latency multi-control branch instruction, transformer and CNN discriminators, plus static MCM elimination and stochastic branching, evaluated on Qubic with QPU traces to cut latency by 70% and logical error rates by up to 9.4x.
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Distribution Complexity of Electronic Structure Simulations on Quantum Supercomputers
An algorithm is presented for estimating distribution complexity of electronic structure Hamiltonians, with O(N^3) entanglement estimation per fragment and quadratic/exponential reductions in distribution cost for quantum and classical interconnects.
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Quantum Algorithm for Distributed Reduction of Entanglements (QADR): A Trainable and Simulation-Efficient QML Framework
QADR decomposes n-qubit VQCs into local sub-circuits to reduce memory from O(2^n) to O(n * 2^{2d+1}) and mitigate barren plateaus, scaling to 2000 features on MNIST and wind turbine diagnostics while matching classical models.