Quantum-Enhanced Inference of Conditional Future Probabilities with Reduced Memory Cost
Abstract
Many potential future events have devastating consequences, from the onset of earthquakes to financial black swan events. Accurate predictions of their probability of occurrence are vital. Yet as the stochastic systems underlying such events become increasingly complex, these probabilities can depend on ever-growing amounts of past data, which require ever more memory to track. The accuracy of our estimates is ultimately limited by both computational and memory constraints; the former to reduce sampling error, the latter to avoid sampling bias. Traditional quantum algorithms -- such as amplitude estimation -- address only the former, and become untenable especially in near-future quantum devices where quantum memory is a premium. Here, we offer quantum-algorithmic means to reduce systematic bias while maintaining a quadratic speed-up. Our results thus advance the timeline for the practical deployment of quantum technologies, and illustrate their additional benefits when simulating processes of growing complexity.
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BibTeX
@misc{chen2026quantumenhanced,
title = {{Quantum-Enhanced Inference of Conditional Future Probabilities with Reduced Memory Cost}},
author = {Jianjun Chen and Akshat Gupta and Chengran Yang and Ariel Neufeld and Jayne Thompson and Thomas J. Elliott and Mile Gu},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.10756},
eprint = {2610.10756},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.10756},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Quantum-Enhanced Inference of Conditional Future Probabilities with Reduced Memory Cost
AU - Jianjun Chen
AU - Akshat Gupta
AU - Chengran Yang
AU - Ariel Neufeld
AU - Jayne Thompson
AU - Thomas J. Elliott
AU - Mile Gu
PY - 2026
DA - 2026/10/07
PB - arXiv
DO - 10.48550/arxiv.2610.10756
UR - https://arxiv.org/abs/2610.10756
AB - Many potential future events have devastating consequences, from the onset of earthquakes to financial black swan events. Accurate predictions of their probability of occurrence are vital. Yet as the stochastic systems underlying such events become increasingly complex, these probabilities can depend on ever-growing amounts of past data, which require ever more memory to track. The accuracy of our estimates is ultimately limited by both computational and memory constraints; the former to reduce sampling error, the latter to avoid sampling bias. Traditional quantum algorithms -- such as amplitude estimation -- address only the former, and become untenable especially in near-future quantum devices where quantum memory is a premium. Here, we offer quantum-algorithmic means to reduce systematic bias while maintaining a quadratic speed-up. Our results thus advance the timeline for the practical deployment of quantum technologies, and illustrate their additional benefits when simulating processes of growing complexity.
ER -