Independent researcher Giancarlo Lelli derived a 15-bit elliptic curve key on a publicly accessible quantum computer, Project Eleven said.
Category: quantum physics – Page 19
On computing quantum waves exactly from classical action
The fundamental quantum postulates on the existence of a wave function, its propagation with the Schrödinger equation in theorem 3.2 and the wave collapse at a measurement in lemma 3.3 are derived from the classical theorem 2.4. Furthermore, analytic computations of the classical action are simpler than solving the Feynman path integral and potentially easier than solving the Schrödinger equation directly. In addition, theorem 3.2 is a multi-particle result.
The J classical multipaths in theorem 3.2 and lemma 3.3 are strictly determined by the initial and final conditions. In the double slit experiment, the probabilistic quantum observation results from the non-Lipschitz constraint force in the slit. For the harmonic oscillator, the Coulomb wave, the particle in the box, or the spinning particle, the initial probabilistic density distribution is classically propagated forward in time. In the EPR experiment [64,65], theorem 2.4 determines a constant angular momentum χo↑,χo↓ over time, and lemma 3.3 in turn allows a classical interpretation that the decision which spin correlation is sensed behind the filters is already taken when the particles separate.
Nuclei Limit Neural Network Quantum Simulations
For a fixed number of configurations, representing quantum states becomes less accurate as their non-stabilizerness increases. This demonstrates a clear limit to how well restricted Boltzmann machines can compress and represent highly entangled systems. Calculations using ground states of medium-mass atomic nuclei reveal non-stabilizerness as a key property governing neural network performance.
Quantum chips could scale faster with new spin-qubit readout that reduces sensors and wiring
Quantum computers, devices that process information leveraging quantum mechanical effects, could tackle some tasks that are difficult or impossible to solve using classical computers. These systems represent data as qubits, units of information that can exist in multiple states at once, unlike the bits used by classical computers that represent data using binary values (“0” or “1”).
Some of the quantum computers developed in recent years store quantum information in the spin (i.e., intrinsic angular momentum) of electrons or nuclei that are trapped in small semiconductor-based structures, known as quantum dots. For these devices to operate reliably, however, engineers need to be able to precisely measure the quantum states of the spin qubits they rely on, a process that is known as qubit readout. It would also be advantageous for these states to be precisely measured in a way that is architecturally compact, or in other words, using space-efficient hardware as opposed to numerous bulkier components.
Researchers at Quantum Motion and University College London (UCL) recently introduced a new approach to clearly read out the states of spin qubits leveraging high-frequency electrical signals. This method, introduced in a paper published in Nature Electronics, was developed by Jacob F. Chittock-Wood and his colleagues while he was completing his Ph.D. at UCL.
Quasiparticles reveal a magneto-optical transport phenomenon
Excitons are being explored in materials science and information technology as a means of storing light. These luminous quasiparticles move through individual layers of quantum materials and can absorb and emit light with high efficiency. They form when a laser pulse excites an electron, leaving behind a positively charged “hole.” The electron and hole attract each other and behave together like a new, independent particle. When the quasiparticle recombines, it emits light and can be detected in high-tech laboratories.
Excitons in ultrathin quantum materials have been intensively studied for more than a decade, including by Alexey Chernikov and his team. At the Cluster of Excellence ctd.qmat—Complexity, Topology and Dynamics in Quantum Matter—at the Universities of Würzburg and Dresden, Chernikov and an international research team based in Dresden have now made a surprising discovery: excitons can be carried along by the magnetic excitations of a quantum material and, as a result, accelerated to ultrahigh speeds. The findings are published in the journal Nature Nanotechnology.
“The fact that the motion of optical particles can be controlled by magnetism is new. Until now, we only knew that the transport of electrons could be controlled by the magnetic order in a quantum material—this is how some sensors in smartphones work, for example. This newly discovered link between optics and magnetism could open up entirely new technological possibilities,” explains Florian Dirnberger, head of an Emmy Noether Junior Research Group at the Technical University of Munich and formerly a postdoctoral researcher in Alexey Chernikov’s Chair of Ultrafast Microscopy and Photonics, where he was responsible for carrying out the research project.
AI automates quantum dot voltage tuning for scaling up quantum computing
Semiconductor spin qubits are a promising candidate for the building blocks of next-generation quantum computers due to their high potential for integration and compatibility with existing semiconductor technologies. Qubits—like the 0s and 1s of a traditional computer—serve as a basic unit of information for quantum computers. However, the practical realization of these computers requires a massive number of qubits, making the development of more efficient adjustment methods a critical challenge for the field.
A research group including Yui Muto from Tohoku University’s Graduate School of Engineering, Assistant Professor Motoya Shinozaki and Associate Professor Tomohiro Otsuka from the Advanced Institute for Materials Research (WPI-AIMR), and their colleagues have successfully demonstrated a method that may help make this massive number of qubits much more manageable, moving us one step closer toward scaling up quantum computing. The findings are published in Scientific Reports.
AI accelerators deliver accurate models for challenging quantum chemistry calculations
The most demanding calculations in quantum chemistry can now be solved with graphics processing unit (GPU) supercomputers. A recently published study shows that software adapted to use GPU hardware can provide not just speed, but also the accuracy needed to solve complex chemistry problems. The work solved the two chemical structures often seen as too complex and expensive to tackle. The advance, published in the Journal of Chemical Theory and Computation, could allow researchers to make meaningful progress in designing new catalysts and improve predicted behaviors of magnetic and electronic materials.
Specifically, the research team—led by computational chemists from NVIDIA, Sandbox AQ, the Wigner Research Centre in Hungary, the Institute for Advanced Study of the Technical University of Munich in Germany, and the Department of Energy’s Pacific Northwest National Laboratory—showed that NVIDIA Blackwell architecture effectively tackles complex simulations. Here, the researchers used a mixture of mathematically precise and approximated approaches to accomplish their goal.
“Our study shows that AI-oriented hardware can do more than provide speed—it can also power chemically accurate, strongly correlated quantum chemistry at the frontier of what is computationally feasible,” said Sotiris Xantheas, a computational chemist at PNNL and study author. Xantheas also serves as the principal investigator of Scalable Predictive methods for Excitations and Correlated phenomena (SPEC), a Department of Energy initiative.
