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As quantum simulations advance, improving classical methods for modelling quantum systems remains crucial as they provide key benchmarks for quantum simulators. Here the authors present a scalable tensor-network algorithm for simulating open quantum systems, addressing key limitations of existing approaches.

Finding a classical description of a quantum state can require resource-intensive tomography protocols. It has now been shown that, for bosonic systems, tomography is extremely inefficient in general, but can be done efficiently for some useful states.

Previous work on the limits of quantum information processing has often assumed access to unlimited computational resources. Imposing a requirement for computational efficiency on entanglement theory substantially changes what is possible.

Quantum low-density parity-check (QLDPC) codes offer lower overhead than topological quantum error-correcting codes, but decoding remains a key challenge for scalable fault-tolerant quantum computing. This work introduces a highly parallelizable decoding algorithm for QLDPC codes that matches the accuracy of leading decoders while enabling significantly improved scalability.

Landauer’s principle connects entropy and energy dissipation in non-equilibrium processes. An experiment now uses this principle to measure entropy production in a Bose gas to resolve contributions from correlations and dissipation.

Demonstrations of quantum advantage relying on sampling hard-to-compute probability distributions are plagued by difficulties in efficiently confirming the correctness of their output, which is known as the verification problem. Here, the authors use a trapped-ion platform to demonstrate efficient verification of quantum random sampling in measurement-based quantum computing.

Accurately estimating Hamiltonian parameters of a quantum system is crucial in the development of large-scale analog quantum simulators. Here, the authors develop and experimentally demonstrate an algorithm to robustly learn Hamiltonian parameters of bosonic systems undergoing dynamical evolution.

Quantum entanglement comes in a rich variety of types and families if more than two particles are involved. Experiments with photons are opening up fresh ways to systematically study multi-particle entanglement.

It is still unclear whether and how quantum computing might prove useful in solving known large-scale classical machine learning problems. Here, the authors show that variants of known quantum algorithms for solving differential equations can provide an advantage in solving some instances of stochastic gradient descent dynamics.

Error mitigation has helped improve the performance of current quantum computing devices. Now, a mathematical analysis of the technique suggests its benefits may not extend to larger systems.

Photonic quantum technologies rely on the creation and manipulation of continuous variables states whose experimental preparation needs to be verified- a noteworthy impractical task. Here, the authors present a protocol that allows to certify continuous variables states with limited experimental overhead.

Understanding machine learning models’ ability to extrapolate from training data to unseen data - known as generalisation - has recently undergone a paradigm shift, while a similar understanding for their quantum counterparts is still missing. Here, the authors show that uniform generalization bounds pessimistically estimate the performance of quantum machine learning models.

The dynamics of quantum states underlies the emergence of thermodynamics and even recent theories of quantum gravity. Now it has been proven that the quantum complexity of states evolving under random operations grows linearly in time.

The minimal amount of assumptions to justify the use of maximum-entropy ensembles is still debated. Here, the authors show that the transitions that a partially known system environment can undergo are the same allowed for the maximum entropy state which is compatible with the known information.

Rigorously explaining thermalization in isolated quantum systems is a fundamental problem in statistical mechanics that is not yet fully resolved. The authors show that a random transformation of the Hamiltonian that does not affect the Gibbs state or, under some conditions, the short-time dynamics, leads to thermalization of states with low entanglement.

Starting from a strongly correlated state, with highly non-Gaussian correlations, a Gaussian state can emerge dynamically over time. Experiments with ultracold atoms show how the mixing between phase and density fluctuations plays the crucial role.

Quantum technologies require an extremely precise functioning of their components which is ensured by sophisticated tools for device characterization. This Technical Review surveys and assesses the currently available tools according to their overall complexity, information gain, and underlying assumptions.

Quantum many-body systems may not thermalize due to the phenomenon of many-body localisation. Its theoretical underpinning is given by observables, the l-bits, which could not as of now be probed by experiments. The authors define experimentally relevant quantities to retrieve spatially resolved entanglement information, allowing to probe the l-bits.

In scaling to global distances, future quantum networks are expected to make use of satellite-based orbiting quantum memories. In this manuscript, the authors simulate the performance of memory-assisted quantum key distribution (MA-QKD) schemes under a range of operating conditions and network configurations, with encouraging conclusions as to the feasibility of implementing such networks with near-term devices.

The thermalization of many-body localization phases poses a number of open questions related to our understanding of thermalization in quantum systems. Here, the authors aim to demonstrate that a quantum information approach can be used to investigate the mechanisms of thermalization in a quantum many-body system when coupled to an external system.

All human activity generates a carbon footprint and scientific research is no exception; however, given the diversity in research activity, developing a standard approach to monitor environmental impact is complex. In this comment, the authors propose a framework for scientists to self-report emissions of research-related emissions in their papers as a means to gradually build an overview of the potential carbon footprint of scientific activity.