A classical polynomial-time algorithm for optimized sampling of lottery tickets in neural networks removes the exponential dependence on data dimension from prior classical approaches.
Quantum Recommendation Systems
4 Pith papers cite this work. Polarity classification is still indexing.
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Adding loop composition to branching quantum walk models produces a variable-time quantum search algorithm whose complexity matches the best known results.
Encodes M by N matrix into quantum state using Θ(log(MN)) qubits in O(log²(MN)) time via segment tree embedded in bucket brigade QRAM with constant ancillas and O(MN) memory cells.
Precomputes rotation angles classically and adds a magnitude-then-phase procedure to enable complex-valued state preparation on BBQRAM at unchanged O(log²(MN)) query cost with no reversible arithmetic on the QPU.
citing papers explorer
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Winning Lottery Tickets in Neural Networks via a Quantum-Inspired Classical Algorithm
A classical polynomial-time algorithm for optimized sampling of lottery tickets in neural networks removes the exponential dependence on data dimension from prior classical approaches.
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Loop Composition in Quantum Algorithms
Adding loop composition to branching quantum walk models produces a variable-time quantum search algorithm whose complexity matches the best known results.
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Efficient Quantum State Preparation with Bucket Brigade QRAM
Encodes M by N matrix into quantum state using Θ(log(MN)) qubits in O(log²(MN)) time via segment tree embedded in bucket brigade QRAM with constant ancillas and O(MN) memory cells.
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Efficient Complex-Valued State Preparation on Bucket Brigade QRAM
Precomputes rotation angles classically and adds a magnitude-then-phase procedure to enable complex-valued state preparation on BBQRAM at unchanged O(log²(MN)) query cost with no reversible arithmetic on the QPU.