Publisher Correction: State estimation with quantum extreme learning machines beyond the scrambling time
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State estimation with quantum extreme learning machines beyond the scrambling time
Quantum extreme learning machines (QELMs) leverage untrained quantum dynamics to efficiently process information encoded in input quantum states, avoiding the high computational cost of training more complicated nonlinear models. On the other hand, quantum information scrambling (QIS) quantifies how the spread of quantum information into correlations makes it irretrievable from local measurements. Here, we explore the tight relation between QIS and the predictive power of QELMs. In particular, we show efficient state estimation is possible even beyond the scrambling time, for many different types of dynamics — in fact, we show that in all the cases we studied, the reconstruction efficiency at long interaction times matches the optimal one offered by random global unitary dynamics. These results offer promising venues for robust experimental QELM-based state estimation protocols, as well as providing novel insights into the nature of QIS from a state estimation perspective.
Parallel circuit implementation of variational quantum algorithms
We present a framework to split quantum circuits of variational quantum algorithms (VQAs) to allow for parallel training and execution to solve problems larger than the number of available qubits in a quantum device. We apply this method to combinatorial optimization problems, where inherent structures can be identified, and show how to implement these parallelized quantum circuits. We show how to formulate an objective function for the classical optimizer to guide the optimization towards meaningful solutions. We test our framework by creating a parallelized version of the Quantum Approximate Optimization Algorithm and a variational version of quantum annealing and explain how our framework applies to other quantum optimization algorithms. We provide results obtained both from simulation and experiments on real hardware. Our results show that the information lost by splitting the quantum circuits can be partially recovered by optimizing a global objective function evaluated with the separate circuit samples.
Light-matter coupling via quantum pathways for spontaneous symmetry breaking in van der Waals antiferromagnetic semiconductors
Light-matter interaction simultaneously alters both the original material and incident light. Light not only reveals material details but also activates coupling mechanisms. The coupling has been demonstrated mechanically, for instance, through the patterning of metallic antennas, resulting in the emergence of plasmonic quasiparticles and enabling wavefront engineering of light via the generalized Snell’s law. However, quantum-mechanical light-matter interaction, wherein photons coherently excite distinct quantum pathways, remains poorly understood. Here, we report on quantum interference between light-induced quantum pathways through the orbital quantum levels and spin continuum. The quantum interference immediately breaks the symmetry of the hexagonal antiferromagnetic semiconductor FePS3. Below the Néel temperature, we observe the emergence of birefringence and linear dichroism, namely, quantum anisotropy due to quantum interference, which is further enhanced by the thickness effect. We explain the direct relevance of the quantum anisotropy to a quantum phase transition by spontaneous symmetry breaking in Mexican hat potential. Our findings suggest material modulation via selective quantum pathways through quantum light-matter interaction.
Direct entanglement detection of quantum systems using machine learning
Entanglement plays a crucial role in advancing quantum technologies and exploring quantum many-body simulations. Here, we introduce a protocol aided by neural networks for measuring entanglement in both equilibrium and non-equilibrium states of local Hamiltonians, with a favorable amount of training data. Our numerical simulations across various Hamiltonian models and qubit configurations reveal that this approach can predict comprehensive entanglement metrics, such as Rényi entropy, for up to 100 qubits using only single-qubit and two-qubit Pauli measurements. Excitingly, future entanglement dynamics beyond the measurement window can be predicted based solely on previous single-qubit traces. Experimentally, we utilize a nuclear spin quantum processor and a neural network to measure entanglement in the ground and dynamical states of a one-dimensional spin chain. The results demonstrate the feasibility of our method in practical experiments. Therefore, our approach offers a promising method for experimentally measuring entanglement in systems with dozens to hundreds of qubits.
Understanding learning through uncertainty and bias
Learning allows humans and other animals to make predictions about the environment that facilitate adaptive behavior. Casting learning as predictive inference can shed light on normative cognitive mechanisms that improve predictions under uncertainty. Drawing on normative learning models, we illustrate how learning should be adjusted to different sources of uncertainty, including perceptual uncertainty, risk, and uncertainty due to environmental changes. Such models explain many hallmarks of human learning in terms of specific statistical considerations that come into play when updating predictions under uncertainty. However, humans also display systematic learning biases that deviate from normative models, as studied in computational psychiatry. Some biases can be explained as normative inference conditioned on inaccurate prior assumptions about the environment, while others reflect approximations to Bayesian inference aimed at reducing cognitive demands. These biases offer insights into cognitive mechanisms underlying learning and how they might go awry in psychiatric illness.
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