In this research, scientists have made an exciting discovery involving “time crystals,” a special kind of phase of matter that behaves in unexpected ways when driven by periodic forces.
In a new study published in Nature Communications, scientists have implemented the topologically ordered time crystal on a quantum processor for the first time.
For error-resistant quantum computers, creating superpositions or entanglement between states is relatively easy. In contrast, adding magic to the state or dislocating them further from easy-to-simulate stabilizer states is expected to be highly challenging.
“In the literature of quantum information, you often encounter terms like ‘magic state distillation’ or ‘magic state cultivation,’ which refer to pretty arduous processes to create special quantum states with magic that the quantum computer can make use of,” said Niroula.
“Prior to this paper, we had written a paper that observed a similar phase transition in entanglement, in which we had observed phases where measurements of a quantum system preserved or destroyed entanglement depending on how frequent they are.”
Scientists have revolutionized the field of quantum photonics by employing high-performance computing to analyze quantum detectors at an unprecedented scale.
Their innovative approach involves the tomographic reconstruction of experimental data, enabling rapid and efficient characterization of photon detectors. This development promises to enhance quantum research significantly, paving the way for advanced applications in quantum computing and communication.
Breakthrough in quantum photonics with high-performance computing.
Quantum squeezing is a method that sharpens precision by redistributing uncertainty within a system, already advancing technologies like atomic clocks. This concept promises even wider impacts as researchers work on applying it to more complex measurements.
Quantum squeezing is a technique in quantum physics that reduces uncertainty in one aspect of a system while increasing it in another. Imagine a balloon filled with air: when it’s untouched, the balloon is perfectly round. If you squeeze one side, it flattens in that spot but stretches in the opposite direction.
Similarly, in a squeezed quantum state, reducing uncertainty (or noise) in one variable, like position, causes increased uncertainty in a related variable, such as momentum. The total uncertainty remains the same, but redistributing it in this way allows for far more precise measurement of one of the variables.
A new all-optical switch uses circularly polarized light and an innovative semiconductor to process data faster and more efficiently in fiber-optic systems.
This technology facilitates significant energy savings and introduces a method to control quantum properties in materials, promising major advancements in optical computing and fundamental science.
Modern high-speed internet relies on light to transmit large amounts of data quickly and reliably through fiber-optic cables. However, when data needs to be processed, the light signals face a bottleneck. They must first be converted into electrical signals for processing before they can continue being transmitted.
Quantum computers hold the promise to emulate complex materials, helping researchers better understand the physical properties that arise from interacting atoms and electrons. This may one day lead to the discovery or design of better semiconductors, insulators, or superconductors that could be used to make ever faster, more powerful, and more energy-efficient electronics.
The close relationship between AI and highly complicated scientific computing can be seen in the fact that both the 2024 Nobel Prizes in Physics and Chemistry were awarded to scientists for devising AI for their respective fields of study. KAIST researchers have now succeeded in dramatically shortening the calculation time of highly sophisticated quantum mechanical computer simulations by predicting atomic-level chemical bonding information distributed in 3D space using a novel approach to teach AI.
For the first time, EPFL researchers have directly observed molecules engaging in hydrogen bonds within liquid water, capturing electronic and nuclear quantum effects that had previously been accessible only through theoretical simulations.
Water is synonymous with life, but the dynamic, multifaceted interaction that brings H2O molecules together – the hydrogen bond – remains mysterious. These hydrogen bonds form as hydrogen and oxygen atoms from neighboring water molecules connect, exchanging electronic charge in the process.
This charge-sharing is a key feature of the three-dimensional ‘H-bond’ network that gives liquid water its unique properties, but quantum phenomena at the heart of such networks have thus far been understood only through theoretical simulations.
A U.S. Naval Research Laboratory (NRL) multi-disciplinary team developed a new paradigm for the control of quantum emitters, providing a new method for modulating and encoding quantum photonic information on a single photon light stream.
The discovery of the quantum tunneling (QT) effect—the transmission of particles through a high potential barrier—was one of the most impressive achievements of quantum mechanics made in the 1920s. Responding to the contemporary challenges, I introduce a deep neural network (DNN) architecture that processes information using the effect of QT. I demonstrate the ability of QT-DNN to recognize optical illusions like a human. Tasking QT-DNN to simulate human perception of the Necker cube and Rubin’s vase, I provide arguments in favor of the superiority of QT-based activation functions over the activation functions optimized for modern applications in machine vision, also showing that, at the fundamental level, QT-DNN is closely related to biology-inspired DNNs and models based on the principles of quantum information processing.
