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Category: quantum physics – Page 25

Dephasing enabled fast charging of quantum batteries

We propose and analyze a universal method to obtain fast charging of a quantum battery by a driven charger system using controlled, pure dephasing of the charger. While the battery displays coherent underdamped oscillations of energy for weak charger dephasing, the quantum Zeno freezing of the charger energy at high dephasing suppresses the rate of transfer of energy to the battery. Choosing an optimum dephasing rate between the regimes leads to a fast charging of the battery. We illustrate our results with the charger and battery modeled by either two-level systems or harmonic oscillators. Apart from the fast charging, the dephasing also renders the charging performance more robust to detuning between the charger, drive, and battery frequencies for the two-level systems case.

npj Quantum Inf ormation volume 11, Article number: 9 (2025) Cite this article.

Hunting for quantum-classical crossover in condensed matter problems

The intensive pursuit for quantum advantage in terms of computational complexity has further led to a modernized crucial question of when and how will quantum computers outperform classical computers. The next milestone is undoubtedly the realization of quantum acceleration in practical problems. Here we provide a clear evidence and arguments that the primary target is likely to be condensed matter physics. Our primary contributions are summarized as follows: 1) Proposal of systematic error/runtime analysis on state-of-the-art classical algorithm based on tensor networks; 2) Dedicated and high-resolution analysis on quantum resource performed at the level of executable logical instructions; 3) Clarification of quantum-classical crosspoint for ground-state simulation to be within runtime of hours using only a few hundreds of thousand physical qubits for 2d Heisenberg and 2d Fermi-Hubbard models, assuming that logical qubits are encoded via the surface code with the physical error rate of p = 10–3. To our knowledge, we argue that condensed matter problems offer the earliest platform for demonstration of practical quantum advantage that is order-of-magnitude more feasible than ever known candidates, in terms of both qubit counts and total runtime.

Yoshioka, N., Okubo, T., Suzuki, Y. et al. Hunting for quantum-classical crossover in condensed matter problems. npj Quantum Inf 10, 45 (2024). https://doi.org/10.1038/s41534-024-00839-4

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Topological quantum processor marks breakthrough in computing

In a leap forward for quantum computing, a Microsoft team led by UC Santa Barbara physicists on Wednesday unveiled an eight-qubit topological quantum processor, the first of its kind. The chip, built as a proof-of-concept for the scientists’ design, opens the door to the development of the long-awaited topological quantum computer.

“We’ve got a bunch of stuff that we’ve been keeping under wraps that we’re dropping all at once now,” said Microsoft Station Q Director Chetan Nayak, a professor of physics at UCSB and a Technical Fellow for Quantum Hardware at Microsoft. The chip was revealed at Station Q’s annual conference in Santa Barbara, and accompanies a paper published in the journal Nature, authored by Station Q, their Microsoft teammates and a host of collaborators that presents the research team’s measurements of these new qubits. (Circa Feb 20 2025)

Microsoft team led by UC Santa Barbara physicists unveils first-of-its-kind topological qubit, paving the way for a more fault-tolerant quantum computer.

German scientists create material that never existed before and could transform semiconductors, lasers, and quantum technology

German scientists have achieved a breakthrough. They have created a novel material, CSiGeSn. This alloy combines carbon, silicon, germanium, and tin. The new compound is stable. Experts believe it will revolutionize electronics and quantum computing. The team used existing chip manufacturing technology. This ensures compatibility. The discovery paves the way for advanced components. It also allows for scalable production.

Peter Putnam, the Wittgenstein of quantum physics, takes on the Multiverse

I have for a long time been searching for applications of the philosophy of Wittgenstein, particularly later Wittgenstein, to physics. I believe I have found that application in the work of Peter Putnam, who, building on the philosophy of Sir Arthur Eddington, Everett (of Many Worlds fame), and John Wheeler, constructed, in his private musings, the beginnings of a verbal, syntactical representation theory for quantum physics.

There have been a couple of articles lately about Putnam, starting with this one in Nautilus less than a month ago.

He was a relatively unknown figure who might have been as famous as Wittgenstein himself if not for a meddling mother.

Toward quantum enhanced coherent Ising machines

The Graduate School of Information Science (GSIS) at Tohoku University, together with the Physics and Informatics (PHI) Lab at NTT Research, Inc., have jointly published a paper in the journal Quantum Science and Technology. The study involved studying a combinatorial clustering problem, a representative task in unsupervised machine learning.

Together, the two institutions are researching methods to bring to life a large-scale CIM simulation platform using conventional high-performance computing (HPC). This large-scale CIM will be critical to enabling cyber CIMs that will be widely accessible for solving hard NP, NP-complete and NP-hard problems.

The collaboration kicked off in 2023 with Hiroaki Kobayashi, Professor at the GSIS at Tohoku University, acting as the principal investigator for the joint research agreement (JRA), with PHI Lab Director Yoshihisa Yamamoto joining as the NTT Research counterpart to Kobayashi.

Human-AI teamwork uncovers hidden magnetic states in quantum spin liquids

At the forefront of discovery, where cutting-edge scientific questions are tackled, we often don’t have much data. Conversely, successful machine learning (ML) tends to rely on large, high-quality data sets for training. So how can researchers harness AI effectively to support their investigations?

In Physical Review Research, scientists describe an approach for working with ML to tackle complex questions in condensed matter physics. Their method tackles hard problems which were previously unsolvable by physicist simulations or by ML algorithms alone.

The researchers were interested in frustrated magnets— in which competing interactions lead to exotic magnetic properties. Studying these materials has helped to advance our understanding of quantum computing and shed light on . However, frustrated magnets are very difficult to simulate, because of the constraints arising from the way magnetic ions interact.

Scientists achieve ‘magic state’ quantum computing breakthrough 20 years in the making — quantum computers can never be truly useful without it

Scientists demonstrate a process called “magic state distillation” in logical qubits for the first time, meaning we can now build quantum computers that are both error-free and more powerful than supercomputers.

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