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IBM announces three new entries in its quantum advantage tracker, each using different approaches to overcome errors and validate quantum results, demonstrating quantum advantage in ways that can be trusted even when classical verification is infeasible.
Proposes Trans-Ising, a transfer learning method for high-dimensional Ising models that uses a loss-based source screening rule and two-stage estimation to improve estimation accuracy over target-only and naive pooling methods.
This paper presents a scalable backpropagation-based algorithm for training deep convolutional networks to run on thermodynamic Ising hardware, achieving 94.9% on CIFAR-10 and 76.0% on CIFAR-100 while analyzing inference cost-accuracy tradeoffs.