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The Wave-function of the Universe Might Finally Be Calculable

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Physicists think that our universe started out as just a lot of quantum fluctuations. That means, if you’re able to calculate wave-function of those quantum fluctuations, you can learn how the universe ended up the way it is now. In a pre-print, a group of physicists around Nima Arkani-Hamed say they’ve worked out a new powerful method to calculate the wave function of the early universe, and they’re calling it the “cosmohedra.” Let’s take a look.

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👉 Transcript with links to references on Patreon ➜ / sabine.
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🔗 Join this channel to get access to perks ➜
/ @sabinehossenfelder.
🖼️ On instagram ➜ / sciencewtg.

#science #sciencenews #physics #universe

Unlocking graphite’s potential: Sliding layers for advanced material properties

Can copper be turned into gold? For centuries, alchemists pursued this dream, unaware that such a transformation requires a nuclear reaction. In contrast, graphite—the material found in pencil tips—and diamond are both composed entirely of carbon atoms; the key difference lies in how these atoms are arranged. Converting graphite into diamond requires extreme temperatures and pressures to break and reform chemical bonds, making the process impractical.

A more feasible transformation, according to Prof. Moshe Ben Shalom, head of the Quantum Layered Matter Group at Tel Aviv University, involves reconfiguring the atomic layers of graphite by shifting them against relatively weak van der Waals forces. This study, led by Prof. Ben Shalom and Ph.D. students Maayan Vizner Stern and Simon Salleh Atri, all from the Raymond & Beverly Sackler School of Physics & Astronomy at Tel Aviv University, was recently published in the journal Nature Review Physics.

While this method won’t create diamonds, if the switching process is fast and efficient enough, it could serve as a tiny electronic memory unit. In this case, the value of these newly engineered “polytype” materials could surpass that of both diamonds and gold.

Promising new class of high-temperature superconductors achieves stability at room pressure

Researchers have made a significant step in the study of a new class of high-temperature superconductors: creating superconductors that work at room pressure. That advance lays the groundwork for deeper exploration of these materials, bringing us closer to real-world applications such as lossless power grids and advanced quantum technologies.

Superconductivity, the ability of certain materials to conduct electricity with zero resistance, typically occurs at extremely low temperatures, or in some cases, under high pressures. For decades, researchers have focused on a class of materials called cuprates, known for their ability to achieve superconductivity at relatively high temperatures.

About five years ago, a team of researchers at the Department of Energy’s SLAC National Accelerator Laboratory and Stanford University discovered superconductivity in nickelates, materials chemically similar to cuprates—and last summer, another group of researchers reported superconductivity in a new class of nickel oxides at temperatures comparable to cuprates.

Quantum algorithm distributed across multiple processors for the first time—paving the way to quantum supercomputers

In a milestone that brings quantum computing tangibly closer to large-scale practical use, scientists at Oxford University Physics have demonstrated the first instance of distributed quantum computing.

Using a photonic network interface, they successfully linked two separate quantum processors to form a single, fully connected quantum computer, paving the way to tackling computational challenges previously out of reach. The results were published on 5 Feb in Nature.

The breakthrough addresses quantum’s ‘scalability problem’: a quantum computer powerful enough to be industry-disrupting would have to be capable of processing millions of qubits. Packing all these processors in a single device, however, would require a machine of an immense size.

Researchers develop first-of-its-kind quantum gate

A recent study led by quantum researchers at the Department of Energy’s Oak Ridge National Laboratory proved popular among the science community interested in building a more reliable quantum network.

The study, led by ORNL’s Hsuan-Hao Lu, details development of a novel quantum gate that operates between two photonic degrees of freedom—polarization and frequency. (Photonic degrees of freedom describe different properties of a photon that can be controlled and used to store or transmit information.) When combined with hyperentanglement, this new approach could enhance error resilience in , helping to pave the way for future quantum networks.

Their work was published in the journal Optica Quantum.

Researchers uncover 1D-like spin behavior in a triangular molecular lattice, challenging traditional views

Quantum spin liquids (QSLs) are fascinating and mysterious states of matter that have intrigued scientists for decades. First proposed by Nobel laureate Philip Anderson in the 1970s, these materials break the conventional rules of magnetism by never settling into a stable magnetic state, even at temperatures close to absolute zero.

Instead, the spins of the atoms within them remain constantly fluctuating and entangled, creating a kind of magnetic “liquid.” This unusual behavior is driven by a phenomenon called magnetic frustration, where competing forces prevent the system from reaching a single, ordered configuration.

QSLs are notoriously difficult to study. Unlike ordinary magnetic materials, they don’t show the usual signs of magnetic transitions, which makes it hard to detect and understand them using traditional techniques. As a result, their behavior has remained an elusive puzzle for researchers.

Thermalization and criticality on an analogue–digital quantum simulator

The advent of quantum simulators in various platforms8,9,10,11,12,13,14 has opened a powerful experimental avenue towards answering the theoretical question of thermalization5,6, which seeks to reconcile the unitarity of quantum evolution with the emergence of statistical mechanics in constituent subsystems. A particularly interesting setting is that in which a quantum system is swept through a critical point15,16,17,18, as varying the sweep rate can allow for accessing markedly different paths through phase space and correspondingly distinct coarsening behaviour. Such effects have been theoretically predicted to cause deviations19,20,21,22 from the celebrated Kibble–Zurek (KZ) mechanism, which states that the correlation length ξ of the final state follows a universal power-law scaling with the ramp time tr (refs. 3, 23,24,25).

Whereas tremendous technical advancements in quantum simulators have enabled the observation of a wealth of thermalization-related phenomena26,27,28,29,30,31,32,33,34,35, the analogue nature of these systems has also imposed constraints on the experimental versatility. Studying thermalization dynamics necessitates state characterization beyond density–density correlations and preparation of initial states across the entire eigenspectrum, both of which are difficult without universal quantum control36. Although digital quantum processors are in principle suitable for such tasks, implementing Hamiltonian evolution requires a high number of digital gates, making large-scale Hamiltonian simulation infeasible under current gate errors.

In this work, we present a hybrid analogue–digital37,38 quantum simulator comprising 69 superconducting transmon qubits connected by tunable couplers in a two-dimensional (2D) lattice (Fig. 1a). The quantum simulator supports universal entangling gates with pairwise interaction between qubits, and high-fidelity analogue simulation of a U symmetric spin Hamiltonian when all couplers are activated at once. The low analogue evolution error, which was previously difficult to achieve with transmon qubits due to correlated cross-talk effects, is enabled by a new scalable calibration scheme (Fig. 1b). Using cross-entropy benchmarking (XEB)39, we demonstrate analogue performance that exceeds the simulation capacity of known classical algorithms at the full system size.

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