We consider how to forecast progress in the domain of quantum computing. For this purpose we collect a dataset of quantum computer systems to date, scored on their physical qubits and gate error rate, and we define an index combining both metrics, the generalized logical qubit. We study the relationship between physical qubits and gate error rate, and tentatively conclude that they are positively correlated (albeit with some room for doubt), indicating a frontier of development that trades-off between them. We also apply a log-linear regression on the metrics to provide a tentative upper bound on how much progress can be expected over time. Within the (generally optimistic) assumptions of our model, including the key assumption that exponential progress in qubit count and gate fidelity will continue, we estimate that that proof-of-concept fault-tolerant computation based on superconductor technology is unlikely (<5% confidence) to be exhibited before 2026, and that quantum devices capable of factoring RSA-2048 are unlikely (<5% confidence) to exist before 2039. It is of course possible that these milestones will in fact be reached earlier, but that this would require faster progress than has yet been seen.
A key problem in financial mathematics is the forecasting of financial crashes: if we perturb asset prices, will financial institutions fail on a massive scale? This was recently shown to be a computationally intractable (NP-hard) problem. Financial crashes are inherently difficult to predict, even for a regulator which has complete information about the financial system. In this paper we show how this problem can be handled by quantum annealers. More specifically, we map the equilibrium condition of a toy-model financial network to the ground-state problem of a spin-1/2 quantum Hamiltonian wi…
Quantum computing is expected to have a profound impact on society. In this work we discuss the potential consequences on existential risk for humanity. Even with the timeline for large-scale fault-tolerant quantum computing still unclear, it is highly likely that quantum computers will eventually realize an exponential speedup for certain practical applications. We identify quantum simulation as the most relevant application in this regard and we qualitatively outline different risk trajectories. Both amplifying and mitigating effects of quantum computing for existential risk are anticipated.…
We present an algorithm which efficiently estimates the intrinsic long-term value of a portfolio of assets on a quantum computer. The method relies on quantum amplitude estimation to estimate the mean of a novel implementation of the Gordon-Shapiro formula. The choice of loading and readout algorithms makes it possible to price a five-asset portfolio on present day quantum computers, a feat which has not been realised using quantum computing to date. We compare results from two available trapped ion quantum computers. Our results are consistent with classical benchmarks, but result in smaller …
Optimal training of finitely-sampled quantum reservoir computers for forecasting of chaotic dynamics
Imperial College London（伦敦帝国理工学院）；City St George’s, University of London（伦敦大学城市圣乔治学院）；The Alan Turing Institute（艾伦·图灵研究所）；Politecnico di Torino（都灵理工大学）
A Quantum Reservoir for Neurodynamical Forecasting
Uni. de Montréal（蒙特利尔大学）；Centre Hospitalier Univ. de Sainte-Justine（圣朱斯蒂纳大学医院中心）；Oak Ridge National Laboratory（橡树岭国家实验室）；IBM Quantum（IBM量子）；Mila – Quebec AI Institute（米拉-魁北克人工智能研究所）
Quantum extreme learning for forecasting experimental chaotic electronic circuits
Institute of Fundamental and Frontier Sciences, University of Electronic Sciences and Technology of China（电子科技大学基础与前沿研究院）；Key Laboratory of Quantum Physics and Photonic Quantum Information, Ministry of Education, University of Electronic Science and Technology of China（教育部量子物理与光子量子信息重点实验室）；Shimmer Center, Tianfu Jiangxi Laboratory（天府江西实验室 shimmer 中心）
