Two-dimensional supersolidity is demonstrated using highly magnetic, ultracold dysprosium atoms.
Category: quantum physics – Page 559
Partition functions are ubiquitous in physics: They are important in determining the thermodynamic properties of many-body systems and in understanding their phase transitions. As shown by Lee and Yang, analytically continuing the partition function to the complex plane allows us to obtain its zeros and thus the entire function. Moreover, the scaling and nature of these zeros can elucidate phase transitions. Here, we show how to find partition function zeros on noisy intermediate-scale trapped-ion quantum computers in a scalable manner, using the XXZ spin chain model as a prototype, and observe their transition from XY-like behavior to Ising-like behavior as a function of the anisotropy. While quantum computers cannot yet scale to the thermodynamic limit, our work provides a pathway to do so as hardware improves, allowing the future calculation of critical phenomena for systems beyond classical computing limits.
Interacting quantum systems exhibit complex phenomena including phase transitions to various ordered phases. The universal nature of critical phenomena reduces their description to determining only the transition temperature and the critical exponents. However, numerically calculating these quantities for systems in new universality classes is complicated because of critical slowing down, requiring increasing resources near the critical point. An alternative approach is to analytically continue the calculation of the partition function to the complex plane and determine its zeros.
The partition function is a positive function of multiple real parameters representing physical quantities such as temperature and applied fields. When the partition function is analytically continued in one of the respective parameters, its zeros show notable structure for a variety of models of interest. Lee and Yang (1, 2) studied the partition function zeros of Ising-like systems in the complex plane of the magnetic field h and found that, at the critical temperature (and in the thermodynamic limit), the loci of zeros pinch to the real axis. Alternatively, Fisher (3) studied the partition function zeros by making the inverse temperature β complex.
MIT researchers suggest a way to protect qubit states using a phenomenon called many-body localization (MBL) — a peculiar phase of matter that is unlike solid or liquid, and never reaches equilibrium.
A new project will use the electric field in an accelerator cavity to try to levitate a tiny metallic particle, allowing it to store quantum information.
Quantum computing could solve problems that are difficult for traditional computer systems. It may seem like magic. One step toward achieving quantum computing even resembles a magician’s trick: levitation. A new project at the U.S. Department of Energy’s Thomas Jefferson National Accelerator Facility will attempt this trick by levitating a microscopic particle in a superconducting radiofrequency (SRF) cavity to observe quantum phenomena.
Typically at Jefferson Lab and other particle accelerator facilities, SRF cavities enable studies of the atom.
Quantum matter can be solid and fluid at the same time—a situation known as supersolidity. Researchers led by Francesca Ferlaino have now created for the first time this fascinating property along two dimensions. They now report in the journal Nature on the realization of supersolidity along two axes of an ultracold quantum gas. The experiment offers many possibilities for further investigation of this exotic state of matter.
Quantum gases are very well suited for investigating the microscopic consequences of interactions in matter. Today, scientists can precisely control individual particles in extremely cooled gas clouds in the laboratory, revealing phenomena that cannot be observed in the every-day world. For example, the individual atoms in a Bose-Einstein condensate are completely delocalized. This means that the same atom exists at each point within the condensate at any given time. Two years ago, the research group led by Francesca Ferlaino from the Department of Experimental Physics at the University of Innsbruck and the Institute of Quantum Optics and Quantum Information at the Austrian Academy of Sciences in Innsbruck managed for the first time to generate supersolid states in ultracold quantum gases of magnetic atoms. The magnetic interaction causes the atoms to self-organize into droplets and arrange themselves in a regular pattern.
“Normally, you would think that each atom would be found in a specific droplet, with no way to get between them,” says Matthew Norcia of Francesca Ferlaino’s team. “However, in the supersolid state, each particle is delocalized across all the droplets, existing simultaneously in each droplet. So basically, you have a system with a series of high-density regions (the droplets) that all share the same delocalized atoms.” This bizarre formation enables effects such as frictionless flow despite the presence of spatial order (superfluidity).
Everyone’s talking about quantum computing these days. The experts claim the future will be full of amazing tech advances, but what does that really mean? property= description.
This looks like a really big breakthrough.
A decades-old problem about how to reliably control millions of qubits in a silicon quantum computer chip has now been solved.
As reported in a new article in Nature Reviews Physics, instead of waiting for fully mature quantum computers to emerge, Los Alamos National Laboratory and other leading institutions have developed hybrid classical/quantum algorithms to extract the most performance—and potentially quantum advantage—from today’s noisy, error-prone hardware. Known as variational quantum algorithms, they use the quantum boxes to manipulate quantum systems while shifting much of the work load to classical computers to let them do what they currently do best: solve optimization problems.
“Quantum computers have the promise to outperform classical computers for certain tasks, but on currently available quantum hardware they can’t run long algorithms. They have too much noise as they interact with environment, which corrupts the information being processed,” said Marco Cerezo, a physicist specializing in quantum computing, quantum machine learning, and quantum information at Los Alamos and a lead author of the paper. “With variational quantum algorithms, we get the best of both worlds. We can harness the power of quantum computers for tasks that classical computers can’t do easily, then use classical computers to compliment the computational power of quantum devices.”
Current noisy, intermediate scale quantum computers have between 50 and 100 qubits, lose their “quantumness” quickly, and lack error correction, which requires more qubits. Since the late 1990s, however, theoreticians have been developing algorithms designed to run on an idealized large, error-correcting, fault tolerant quantum computer.
Quantum engineers from UNSW Sydney have removed a major obstacle that has stood in the way of quantum computers becoming a reality. They discovered a new technique they say will be capable of controlling millions of spin qubits—the basic units of information in a silicon quantum processor.
Until now, quantum computer engineers and scientists have worked with a proof-of-concept model of quantum processors by demonstrating the control of only a handful of qubits.
But with their latest research, published today in Science Advances, the team have found what they consider “the missing jigsaw piece” in the quantum computer architecture that should enable the control of the millions of qubits needed for extraordinarily complex calculations.