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From the 1 of 7 linked papers with an AI index.

most citedQuantum Reservoir Computing for Realized Volatility Forecasting

2 citations · 3 across the 3 of their papers we have counts for

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8 papers

quant-ph20261 cited

Variational simulation of higher-spin systems on qubit-based quantum simulators

Chufan Lyu, Zuoheng Zou, Xusheng Xu +2

The paper proposes a variational framework for simulating higher‑spin (d‑level) models on qubit‑based quantum simulators, using penalty terms to suppress unphysical states and comp…

quant-ph20262 cited

Quantum Reservoir Computing for Realized Volatility Forecasting

Qingyu Li, Chiranjib Mukhopadhyay, Abolfazl Bayat +1

Recent advances in quantum computing have demonstrated its potential to significantly enhance the analysis and forecasting of complex classical data. Among these, quantum reservoir…

quant-ph2026

Quantum reservoir computing for predicting and characterizing chaotic maps

Qingyu Li, Chiranjib Mukhopadhyay, Ludovico Minati +1

Quantum reservoir computing has emerged as a promising paradigm for harnessing quantum systems to process temporal data efficiently by bypassing the costly training of gradient-bas…

cond-mat.quant-gas2026

Enhanced multi-parameter metrology in dissipative Rydberg atom time crystals

Bang Liu, Jun-Rong Chen, Yu Ma +10

The pursuit of unprecedented sensitivity in quantum enhanced metrology has spurred interest in non-equilibrium quantum phases of matter and their symmetry breaking. In particular,…

physics.optics2026

Mechanisms and Opportunities for Tunable High-Purity Single Photon Emitters: A Review of Hybrid Perovskites and Prospects for Bright Squeezed Vacuum

Galy Yang, Eric Ashallay, Zhiming Wang +2

Single-photon emitters (SPEs) are central to quantum communication, computing, and metrology, yet their development remains constrained by trade-offs in purity, indistinguishabilit…

quant-ph2025

Enhancing the reachability of variational quantum algorithms via input-state design

Shaojun Wu, Shan Jin, Abolfazl Bayat +1

Variational quantum algorithms (VQAs) face an inherent trade-off between expressivity and trainability: deeper circuits can represent richer states but suffer from noise accumulati…