Universal Inductive Inference of Quantum States
Abstract
Solomonoff's universal inductive inference [Inf. Control. 1964] is one of the most general frameworks for learning from sequential data and predicting future observations. It provides prediction guarantees for any computable stochastic source. We ask whether an analogous universal theory of induction can be developed for quantum systems. To this end, we introduce universal quantum inductive inference, a framework for learning and predicting quantum sources that may exhibit arbitrary correlations and entanglement across time. Given the outcomes of past measurements and the corresponding post-measurement quantum systems, the learner produces a joint state that predicts the next measurement outcome and post-measurement system while preserving correlations with the past systems. We model a quantum source as a multipartite quantum state whose description is generated by an unknown s-bit program within a given time bound. The learner is required to produce a state that is ε-close in trace distance to the target with probability at least 1−δ. We establish an information-theoretic inference algorithm with round complexity O(sε−2δ−1). We further prove a matching lower bound in the relevant parameter regime, even for classical sources. As a second result, we construct a non-i.i.d. state tomography algorithm that outputs a classical description of the conditional state of the next system given past measurement outcomes. Our information-theoretic tomography algorithm achieves round complexity O(smin{2s,2n}ε−2δ−1), where n is the number of qubits received in each round. Finally, we investigate the computational hardness of universal quantum inductive inference under quantum cryptographic assumptions.
Related
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BibTeX
@misc{hiroka2026universal,
title = {{Universal Inductive Inference of Quantum States}},
author = {Taiga Hiroka and Min-Hsiu Hsieh and Yuki Shirakawa},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.36912},
eprint = {2609.36912},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.36912},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Universal Inductive Inference of Quantum States
AU - Taiga Hiroka
AU - Min-Hsiu Hsieh
AU - Yuki Shirakawa
PY - 2026
DA - 2026/09/29
PB - arXiv
DO - 10.48550/arxiv.2609.36912
UR - https://arxiv.org/abs/2609.36912
AB - Solomonoff's universal inductive inference [Inf. Control. 1964] is one of the most general frameworks for learning from sequential data and predicting future observations. It provides prediction guarantees for any computable stochastic source. We ask whether an analogous universal theory of induction can be developed for quantum systems. To this end, we introduce universal quantum inductive inference, a framework for learning and predicting quantum sources that may exhibit arbitrary correlations and entanglement across time. Given the outcomes of past measurements and the corresponding post-measurement quantum systems, the learner produces a joint state that predicts the next measurement outcome and post-measurement system while preserving correlations with the past systems. We model a quantum source as a multipartite quantum state whose description is generated by an unknown s-bit program within a given time bound. The learner is required to produce a state that is ε-close in trace distance to the target with probability at least 1−δ. We establish an information-theoretic inference algorithm with round complexity O(sε^−2δ^−1). We further prove a matching lower bound in the relevant parameter regime, even for classical sources. As a second result, we construct a non-i.i.d. state tomography algorithm that outputs a classical description of the conditional state of the next system given past measurement outcomes. Our information-theoretic tomography algorithm achieves round complexity O(smin{2^s,2^n}ε^−2δ^−1), where n is the number of qubits received in each round. Finally, we investigate the computational hardness of universal quantum inductive inference under quantum cryptographic assumptions.
ER -