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Category: quantum physics – Page 178

New DESI results strengthen hints that dark energy may evolve

The fate of the universe hinges on the balance between matter and dark energy: the fundamental ingredient that drives its accelerating expansion. New results from the Dark Energy Spectroscopic Instrument (DESI) collaboration use the largest 3D map of our universe ever made to track dark energy’s influence over the past 11 billion years. Researchers see hints that dark energy, widely thought to be a “cosmological constant,” might be evolving over time in unexpected ways.

DESI is an international experiment with more than 900 researchers from more than 70 institutions around the world and is managed by the U.S. Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab). The collaboration shared their findings today in multiple papers that will be posted on the online repository arXiv and in a presentation at the American Physical Society’s Global Physics Summit in Anaheim, California.

“What we are seeing is deeply intriguing,” said Alexie Leauthaud-Harnett, co-spokesperson for DESI and a professor at UC Santa Cruz. “It is exciting to think that we may be on the cusp of a major discovery about dark energy and the fundamental nature of our .”

A simple way to control superconductivity: Twisting atomically thin layers fine-tunes properties

Scientists from the RIKEN Center for Emergent Matter Science (CEMS) and collaborators have discovered a new way to control superconductivity—an essential phenomenon for developing more energy-efficient technologies and quantum computing—by simply twisting atomically thin layers within a layered device.

By adjusting the twist angle, they were able to finely tune the “superconducting gap,” which plays a key role in the behavior of these materials. The research is published in Nature Physics.

The superconducting gap is the energy threshold required to break apart Cooper pairs—bound electron pairs that enable superconductivity at low temperatures. Having a larger gap allows superconductivity to persist at higher, more accessible temperatures, and tuning the gap is also important for optimizing Cooper pair behavior at the nanoscale, contributing to the high functionality of quantum devices.

Quantum ‘Tornadoes’ Spotted in Semimetal May Redefine Electronics

Physicists in Germany have led experiments that show the inertia of electrons can form ‘tornadoes’ inside a quantum semimetal.

It’s almost impossible for electrons to sit still, and their motions can take on some bizarre forms. Case in point: an analysis of electron behavior in a quantum material called tantalum arsenide reveals vortices.

But the story gets weirder. These electrons aren’t spiraling in a physical place – they’re doing so in a quantum blur of possibility called momentum space. Rather than drawing a map of particles potential locations, or position space, momentum space describes their motion through their energy and direction.

Quantum circuit optimization with AlphaTensor

AlphaTensor–Quantum addresses three main challenges that go beyond the capabilities of AlphaTensor25 when applied to this problem. First, it optimizes the symmetric (rather than the standard) tensor rank; this is achieved by modifying the RL environment and actions to provide symmetric (Waring) decompositions of the tensor, which has the beneficial side effect of reducing the action search space. Second, AlphaTensor–Quantum scales up to large tensor sizes, which is a requirement as the size of the tensor corresponds directly to the number of qubits in the circuit to be optimized; this is achieved by a neural network architecture featuring symmetrization layers. Third, AlphaTensor–Quantum leverages domain knowledge that falls outside of the tensor decomposition framework; this is achieved by incorporating gadgets (constructions that can save T gates by using auxiliary ancilla qubits) through an efficient procedure embedded in the RL environment.

We demonstrate that AlphaTensor–Quantum is a powerful method for finding efficient quantum circuits. On a benchmark of arithmetic primitives, it outperforms all existing methods for T-count optimization, especially when allowed to leverage domain knowledge. For multiplication in finite fields, an operation with application in cryptography34, AlphaTensor–Quantum finds an efficient quantum algorithm with the same complexity as the classical Karatsuba method35. This is the most efficient quantum algorithm for multiplication on finite fields reported so far (naive translations of classical algorithms introduce overhead36,37 due to the reversible nature of quantum computations). We also optimize quantum primitives for other relevant problems, ranging from arithmetic computations used, for example, in Shor’s algorithm38, to Hamiltonian simulation in quantum chemistry, for example, iron–molybdenum cofactor (FeMoco) simulation39,40. AlphaTensor–Quantum recovers the best-known hand-designed solutions, demonstrating that it can effectively optimize circuits of interest in a fully automated way. We envision that this approach can accelerate discoveries in quantum computation as it saves the numerous hours of research invested in the design of optimized circuits.

AlphaTensor–Quantum can effectively exploit the domain knowledge (provided in the form of gadgets with state-of-the-art magic-state factories12), finding constructions with lower T-count. Because of its flexibility, AlphaTensor–Quantum can be readily extended in multiple ways, for example, by considering complexity metrics other than the T-count such as the cost of two-qubit Clifford gates or the qubit topology, by allowing circuit approximations, or by incorporating new domain knowledge. We expect that AlphaTensor–Quantum will become instrumental in automatic circuit optimization with new advancements in quantum computing.

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