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npj Quantum Information

Experimental causal inference from measurements alone in the presence of spatial and temporal quantum correlations

Hongfeng Liu, Xiangjing Liu, Qian Chen, Yixian Qiu, Vlatko Vedral, Xinfang Nie, Oscar Dahlsten, Dawei Lu

Published Aug 10, 2026• 0 citations
Publisher

Abstract

The causal structure of a set of events specifies which events influence which others. Determining this structure, known as causal inference, is a fundamental task in science. Reset-style intervention is commonly used to determine causal structure in classical scenarios. However, in the more fundamental quantum theory, there is evidence that projective measurements alone can suffice in some cases. We here extend those cases to two systems at two times, more general channels, and from fine-grained to less invasive coarse-grained projective measurements. The measurements are implemented by scattering circuits in a nuclear magnetic resonance platform. The measurement data are analysed via the pseudo-density matrix (PDM) formalism for measurements on systems at several times, in order to determine compatibility with given causal structures. We thereby successfully demonstrate causal inference from coarse-grained measurements alone, in a scenario with several qubits and two times.

DOI
10.1038/s41534-026-01337-5

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The causal structure of a set of events specifies which events influence which others. Determining this structure, known as causal inference, is a fundamental task in science. Reset-style intervention is commonly used to determine causal structure in classical scenarios. However, in the more fundamental quantum theory, there is evidence that projective measurements alone can suffice in some cases. We here extend those cases to two systems at two times, more general channels, and from fine-grained to less invasive coarse-grained projective measurements. The measurements are implemented by scattering circuits in a nuclear magnetic resonance platform. The measurement data are analysed via the pseudo-density matrix (PDM) formalism for measurements on systems at several times, in order to determine compatibility with given causal structures. We thereby successfully demonstrate causal inference from coarse-grained measurements alone, in a scenario with several qubits and two times.
We probe the foundations of causal structure inference experimentally. The causal structure concerns which events influence other events. We probe whether causal structure can be determined without intervention in quantum systems. Intervention is commonly used to determine causal structure in classical scenarios, but in the more fundamental quantum theory, there is evidence that measurements alone, even coarse-grained measurements, can suffice. We demonstrate the experimental discrimination between several possible causal structures for a bipartite quantum system at two times, solely via coarse-grained projective measurements. The measurements are implemented by an approach known as scattering circuits in a nuclear magnetic resonance platform. Using recent analytical methods the data thus gathered is sufficient to determine the causal structure. Coarse-grained projective measurements disturb the quantum state less than fine-grained projective measurements and much less than interventions that reset the system to a fixed state.
We report NMR scattering circuit experiments that reveal causal structure. The scattering circuit involves interacting a probe qubit with the system of interest and finally measuring the probe qubit. The scattering circuit thereby implements a coarse-grained projective measurement. Causal structure refers to which events influence others and in the quantum case corresponds to different quantum circuit structures. In classical scenarios, intervention is commonly used to infer causal structure. In this quantum scenario of a bipartite system at two times, we demonstrate via scattering circuit experiments that coarse-grained measurements alone suffice for determining the causal structure. The experiment is undertaken by manipulating the nuclear spins of four Carbon-13 atoms in crotonic acid. The data analysis determines the compatibility of the data with given causal structures via representing the data as a pseudo density matrix (PDM) and analysing properties of the PDM. We demonstrate the successful identification of the causal structure for partial swap channels and fully decohering channels.
We give a causal inference scheme using quantum observations alone for a case with both temporal and spatial correlations: a bipartite quantum system with measurements at two times. The protocol determines compatibility with five causal structures distinguished by the direction of causal influence and whether there are initial correlations. We derive and exploit a closed-form expression for the spacetime pseudo-density matrix (PDM) for many times and qubits. This PDM can be determined by light-touch coarse-grained measurements alone. We prove that if there is no signalling between two subsystems, the reduced state of the PDM cannot have negativity, regardless of initial spatial correlations. In addition, the protocol exploits the time asymmetry of the PDM to determine the temporal order. The protocol succeeds for a state with coherence undergoing a fully decohering channel. Thus coherence in the channel is not necessary for the quantum advantage of causal inference from observations alone.
In ordinary, non-relativistic, quantum physics, time enters only as a parameter and not as an observable: a state of a physical system is specified at a given time and then evolved according to the prescribed dynamics. While the state can, and usually does, extend across all space, it is only defined at one instant of time. Here we ask what would happen if we defined the notion of the quantum density matrix for multiple spatial and temporal measurements. We introduce the concept of a pseudo-density matrix (PDM) which treats space and time indiscriminately. This matrix in general fails to be positive for measurement events which do not occur simultaneously, motivating us to define a measure of causality that discriminates between spatial and temporal correlations. Important properties of this measure, such as monotonicity under local operations, are proved. Two qubit NMR experiments are presented that illustrate how a temporal pseudo-density matrix approaches a genuinely allowed density matrix as the amount of decoherence is increased between two consecutive measurements.
Identifying causal order from restricted projective data is generally nontrivial. When two quantum players interact only through an unobserved environment, the available local measurement statistics are typically not tomographically complete, so the underlying process cannot in general be reconstructed exactly from the observed distribution. As a result, causal direction can be statistically identifiable in some cases but fundamentally indistinguishable in others. In this work, we determine necessary and sufficient conditions for deciding when an observed distribution is compatible with a memoryless sequential quantum process in a fixed direction. We show that directional conditional-independence structure and the positivity criterion based on the pseudo-density matrix, as developed in recent work by We show that directional conditional-independence structure and PDM-based positivity criteria are not sufficient by themselves. We then specialize to the two-qubit Pauli setting, where the problem remains non-tomographic but becomes explicitly tractable. In this regime, we characterize when the two sequential directions are statistically indistinguishable, and we show by example that positivity alone does not exclude more general memoryful strategies, whereas the additional algebraic consistency requirement does.
Though the topic of causal inference is typically considered in the context of classical statistical models, recent years have seen great interest in extending causal inference techniques to quantum and generalized theories. Causal identification is a type of causal inference problem concerned with recovering from observational data and qualitative assumptions the causal mechanisms generating the data, and hence the effects of hypothetical interventions. A major obstacle to a theory of causal identification in the quantum setting is the question of what should play the role of "observational data," as any means of extracting data at a certain locus will almost certainly disturb the system. Hence, one might think a priori that quantum measurements are already too much like interventions, so that the problem of causal identification trivializes. This is not the case. Fixing a limited class of quantum instruments (namely the class of all projective measurements) to play the role of "observations," we note that as in the classical setting, there exist scenarios for which causal identification is not possible. We then present sufficient conditions for quantum causal identification, starting with a quantum analogue of the well-known "front-door criterion" and finishing with a broader class of scenarios for which the effect of a single intervention is identifiable. These results emerge from generalizing the process-theoretic account of classical causal inference due to Jacobs, Kissinger, and Zanasi beyond the setting of Markov categories, and thereby treating the classical and quantum problems uniformly.