Condensed Matter Theory

Abstract:

Half a century ago, two theoretical papers were published that together sparked major new directions — conceptual, mathematical and practically applicable — in several previously disparate fields of science. In this Comment, the authors of one of those papers expose key aspects of the thinking behind them, their implementations and implications, along with sketches of several subsequent and consequential developments.

Abstract:

We consider the universal aspects of two problems: (i) the singular value structure of a product $M_t=m_t{m}_{t-1}\dots m_1$ of many large independent random matrices and (ii) the slow purification of a large number of qubits by repeated quantum measurements. The time-evolution operator in the latter case is again a product of matrices $m_i$ , representing time steps in the evolution, but the $m_i$ are now nontrivially correlated as a result of Born’s rule. Both processes are associated with the decay of natural measures of entropy as a function of time or of the number of matrices in the product. We argue that, for a broad class of models, each process is described by universal scaling forms for purification and that (i) and (ii) represent distinct “universality classes” with distinct scaling functions. Using the replica trick, these universality classes correspond to effective one-dimensional statistical mechanics models for a gas of “kinks,” representing domain walls between elements of the permutation group. This is an instructive low-dimensional limit of the effective statistical mechanics models for random circuits and tensor networks. These results apply to longtime purification in spatially local monitored circuit models on the entangled side of the measurement phase transition.

Abstract:

By linking genetic sequences to phenotypic traits, genotype-phenotype maps represent a key layer in biological organization. Their structure modulates the effects of genetic mutations which can contribute to shaping evolutionary outcomes. Recent work based on algorithmic information theory introduced an upper bound on the likelihood of a random genetic mutation causing a transition between two phenotypes, using only the conditional complexity between them. Here we evaluate how well this bound works for a range of genotype-phenotype maps, including a differential equation model for circadian rhythm, a matrix-multiplication model of gene regulatory networks, a developmental model of tooth morphologies for ringed seals, a polyomino-tile shape model of biological self-assembly, and the hydrophobic/polar (HP) lattice protein model. By assessing three levels of predictive performance, we find that the bound provides meaningful estimates of phenotype transition probabilities across these complex systems. These results suggest that transition probabilities can be predicted to some degree directly from the phenotypes themselves, without needing detailed knowledge of the underlying genotype-phenotype map.

Abstract:

Graphene moiré systems are ideal environments for investigating complex phase diagrams and gaining fundamental insights into the mechanisms that underlie them, as they permit controlled manipulation of electronic properties. Magic-angle twisted trilayer graphene has emerged as a key platform for exploring moiré superconductivity due to the robustness of its superconducting order and the ability to tune its energy bands with an electric field. Here we report the direct observation of two domes of superconductivity in the phase diagram of magic-angle twisted trilayer graphene. The dependence of the superconductivity of doped holes on the temperature, magnetic field and bias current shows that it is suppressed near a specific filling of the moiré flat band, leading to a double dome in the phase diagram within a finite range of the displacement field. The transport properties are also indicative of a phase transition and the potentially distinct nature of superconductivity in the two domes. Hartree–Fock calculations incorporating mild strain yield an incommensurate Kekulé spiral state whose effective spin polarization peaks in the regime where superconductivity is suppressed in the experiments.

Abstract:

Multifarious assembly models consider multiple structures assembled from a shared set of components, reflecting the efficient usage of components in biological self-assembly. These models are subject to a high-dimensional parameter space, with only a finite region of parameter space giving reliable self-assembly. Here, we use a continuous-time Gillespie simulation method to study multifarious self-assembly and find that the region of parameter space in which reliable self-assembly can be achieved is smaller than what was obtained previously using a discrete-time Monte Carlo simulation method. We explain this discrepancy through a detailed analysis of the stability of assembled structures against chimera formation. We find that our continuous-time simulations of multifarious self-assembly can expose this instability in large systems even at moderate simulation times. In contrast, discrete-time simulations are slow to show this instability, particularly for large system sizes. For the remaining state space, we find good agreement between the predictions of continuous- and discrete-time simulations. We present physical arguments that can help us predict the state boundaries in the parameter space and gain a deeper understanding of multifarious self-assembly.