Data structure of synthetic quantum matter - in and out of equilibrium

Abstract

Analog quantum simulators natively return large collections of single-shot projective measurements, the wave-function snapshots. These are high-dimensional configurations drawn from the Born distribution of the state they prepare. These collections carry far more information than is retained when they are simply compressed into low-order observables fixed in advance. Moreover, this procedure requires knowing beforehand what one is looking for. This thesis chooses to take a different route, asking what structure a dataset of snapshots possesses as a set of points in configuration space, and what kind of physics that structure reflects. The tools used here are drawn from un supervised learning and network theory, namely principal component analysis (PCA), intrinsic-dimension estimators (TWO-NN and BID), and the wave-function networks obtained by promoting snapshots to nodes and activating links according to the met ric structure of configuration space. They are chosen under two constraints, namely interpretability at every step, and the smallest possible amount of physical input: no order parameter, no symmetry, and no knowledge of the Hamiltonian. At equilibrium, wave-function networks are used to build a classification of one dimensional quantum phases based on snapshots alone. The scheme first selects, in the spirit of Occam’s razor, the measurement basis carrying the minimal information content, and then analyses the networks constructed in that basis. Paramagnetic and symmetry-broken phases display low complexity and “probability” networks, symmetry protected topological phases high complexity and Erdős–Rényi networks, and critical points high complexity and scale-free networks. Interestingly, gapped and gapless mat ter are treated on the same footing, and real-space decimation enters the scheme to resolve massive phases rather than flowing towards criticality. Out of equilibrium, the dynamical exponent governing transport of a conserved quan tity is shown to be imprinted on the singular-value spectrum of the snapshot matrix. In particular, it is shown that the PCA entropy, and in fact the leading eigenvalue alone, grows with the same exponent as the underlying spin or energy transport, at modest system sizes, evolution times and sample numbers. Ergodicity breaking is then ad dressed through the binary intrinsic dimension (BID) of the snapshots, in the quantum sun model: the BID grows and saturates at the Thouless time, reaching its maximum in the ergodic phase, remaining system-size independent in the non-ergodic one, and growing sub-maximally at criticality, where the data occupy an extensive but lower dimensional manifold. The associated networks reproduce the equilibrium dichotomy, being random in the ergodic phase and scale-free at the transition, whose location can be identified by the BID within a few percent of spectral estimates. Preliminary experimental observations, not reported here, show analogous features. Taken together, these results indicate that datasets orders of magnitude smaller than the Hilbert-space dimension suffice to identify collective properties of quantum matter through the study of the structure of the data points in configuration space. Moreover, the same structural classes recur across ground states and quenched dynamics, with no order parameter supplied and under realistic experimental imperfections. The final chapter elaborates on another aspect of quantum simulator capabilities, that is, that to realize gauge theory dynamics. There, one-dimensional U(1) lattice gauge theories with a topological θ term are mapped exactly onto kinetically con strained fermions in an emergent Stark potential. The mapping accounts for the slow relaxation reported in recent Rydberg experiments: prethermalization near an inte grable point at θ = 0, and Stark localization with Hilbert-space fragmentation at θ ̸ =0.