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S. Kais

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Preprint Oct 2026

Quantum dynamics of spin systems using quantum-like resources

Recent publications have introduced the concept of quantum-like (QL) bits, along with their associated QL-states and QL-gate operations, emerging from the dynamics of complex synchronized networks. The present work builds on this framework to present a simulation of quantum dynamics in spin systems. We focus on the tra...

Debadrita Saha, Ethan Dickey, William Horvat et al. · 0 citations
Preprint Sep 2026

Predictive Structure Behind Rare Outcomes in Random Quantum Circuits

Rare outputs of random quantum circuits are usually treated as terminal statistics. We ask whether they reveal intermediate organization that remains useful beyond the selecting future. Comparing unconditioned random-normal (RN) and peak-selected random-peaked (RP) circuits from the same local generator, we find strong...

Myeongsu Kim, Travis S. Humble, S. Kais · 0 citations
Preprint Sep 2026

Accelerating Quantum Simulations of Materials Through Parameter and Ansatz Transfer Strategies

Quantum computing offers a promising route for electronic-structure calculations, but practical condensed-matter applications often require solving large families of related Hamiltonians arising from $\mathbf{k}$-point sampling, compositional variation, defects, and changes in system size. Here, we develop strategies f...

Saurabh Shivpuje, Vinit Singh, Manas Sajjan et al. · 0 citations
Preprint Sep 2026

Dynamical Readout of Measurement Statistics and Emergent Entanglement-Like States in Classical Networks

Can a classical network not only encode a quantum-like state, but also read out its measurement statistics through its own collective dynamics? Here we introduce a network-native readout scheme for a structured classical network whose community modes form an effective two-qubit state space. Network connectivity selects...

Song-Bo Xie, Ethan Dickey, Syed Ahmed Taimoor et al. · 1 citation
Preprint Aug 2026

Symmetry Constraints Regularize Neural Quantum State Learning

The results indicate that symmetry compilation concentrates the expressive power of NQS on states relevant to the target problem, thereby reducing model size and training cost without sacrificing accuracy.

Turbasu Chatterjee, Manas Sajjan, Songbo Xie et al. · 0 citations

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