Theory suggests quantum computers should be exponentially faster on some learning tasks than classical machines

Phys.org  June 10, 2022 An international team of researchers (USA – Catech, Harvard University, UC Berkeley, Google, Microsoft, Austria) has proved that quantum machines could learn from exponentially fewer experiments than the number required by conventional experiments. This exponential advantage is shown for predicting properties of physical systems, performing quantum principal component analysis, and learning about physical dynamics. The quantum resources needed for achieving an exponential advantage are quite modest in some cases. Conducting experiments with 40 superconducting qubits and 1300 quantum gates, they demonstrated that a substantial quantum advantage is possible with today’s quantum processors…read more. TECHNICAL ARTICLE  1  […]

Researchers develop novel analog processor for high performance computing

Phys.org  August 27, 2021 The lack of modularization and lumped element reconfigurability in photonics has prevented the transition to an all-optical analog computing platform. A team of researchers in the US (George Washington University, UCLA, City College of New York) explored using numerical simulation, a nanophotonic platform based on epsilon-near-zero materials capable of solving in the analog domain partial differential equations (PDE). Wavelength stretching in zero-index media enables highly nonlocal interactions within the board based on the conduction of electric displacement, which can be monitored to extract the solution of a broad class of PDE problems. By exploiting the experimentally […]

Researchers develop one-way street for electrons

Phys.org  April 9, 2020 The underlying principle of ratcheting is to convert a fluctuating, unbiased force into unidirectional motion. A team of researchers in the US (University of North Carolina, Vanderbilt University, Duke University) reports the ratcheting of electrons at room temperature using a semiconductor nanowire with precisely engineered asymmetry. Modulation of the nanowire diameter creates a cylindrical sawtooth geometry with broken inversion symmetry on a nanometer-length scale. In a two-terminal device, this structure responded as a three-dimensional geometric diode that funnels electrons preferentially in one direction through specular reflection of quasi-ballistic electrons at the nanowire surface. The ratcheting effect […]

Intel’s new AI chips can crunch data 1,000 times faster than normal ones

MIT Technology Review  July 16, 2019 Intel has just unveiled Pohoiki Beach, a system that contains 64 of its Loihi AI processors with neuromorphic chips. They can perform certain data-crunching tasks up to 1,000 times faster than more general-purpose processors such as CPUs and GPUs, while using much less power. The company will produce a system capable of simulating 100 million neurons by the end of 2019. Researchers will then be able to apply it to a whole new set of applications…read more.