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Paper 1
Mind the gaps: The fraught road to quantum advantage
Jens Eisert, John Preskill
- Year
- 2025
- Journal
- arXiv preprint
- DOI
- arXiv:2510.19928
- arXiv
- 2510.19928
Quantum computing is advancing rapidly, yet substantial gaps separate today's noisy intermediate-scale quantum (NISQ) devices from tomorrow's fault-tolerant application-scale quantum (FASQ) machines. We identify four related hurdles along the road ahead: (i) from error mitigation to active error detection and correction, (ii) from rudimentary error correction to scalable fault tolerance, (iii) from early heuristics to mature, verifiable algorithms, and (iv) from exploratory simulators to credible advantage in quantum simulation. Targeting these transitions will accelerate progress toward broadly useful quantum computing.
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Quantum generative model on bicycle-sharing system and an application
Fumio Nemoto, Nobuyuki Koike, Daichi Sato, Yuuta Kawaai, Masayuki Ohzeki
- Year
- 2025
- Journal
- arXiv preprint
- DOI
- arXiv:2510.04512
- arXiv
- 2510.04512
Recently, bicycle-sharing systems have been implemented in numerous cities, becoming integral to daily life. However, a prevalent issue arises when intensive commuting demand leads to bicycle shortages in specific areas and at particular times. To address this challenge, we employ a novel quantum machine learning model that analyzes time series data by fitting quantum time evolution to observed sequences. This model enables us to capture actual trends in bicycle counts at individual ports and identify correlations between different ports. Utilizing the trained model, we simulate the impact of proactively adding bicycles to high-demand ports on the overall rental number across the system. Given that the core of this method lies in a Monte Carlo simulation, it is anticipated to have a wide range of industrial applications.
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