Quantum computing company D-Wave has doubled the scale of its cloud-based computing platform and said it is already solving real-world problems in minutes that would take traditional computers a whole day of number crunching. D-Wave, through a service called Leap, grants developers access to a cloud-based quantum processor that can be used to test and trial applications in real-time. An introduction to cloud computing from IaaS and PaaS to hybrid, public and private cloud. In the previous iteration of Leap, the quantum processor was 2,000-qubits strong, with each qubit capable of connecting to six other qubits. D-Wave has more than doubled the performance of the technology: Advantage's quantum processor – which is available through the Leap platform – boasts 5,000 qubits, and each qubit can connect to 15 others.
Majorana bound states (MBSs) are peculiar quasiparticles that may one day become the cornerstone of topological quantum computing. To engineer these states, physicists have used semiconductor nanowires in contact with a superconductor. Although many of the observed properties align with theoretical predictions, a closer look into the creation of MBSs is desirable. Deng et al. fabricated nanowires with a quantum dot at one end that served as a spectrometer for the states that formed inside the superconducting gap of the nanowire. Using this setup, topologically trivial bound states were seen to coalesce into MBSs as the magnetic field was varied.
Japanese tech giant NEC announced on Thursday it has teamed up with D-Wave to work on systems that combine the former's supercomputers with the latter's quantum annealers. The pair will use D-Wave's Leap quantum cloud service as the basis for the hybrid services, which are said to have applications in transportation, materials science, and machine learning. "The two companies will apply D-Wave's collection of over 200 early customer applications to six markets identified by NEC, such as finance, manufacturing and distribution," NEC said in a statement. "The two companies will also explore the possibility of enabling the use of NEC's supercomputers on D-Wave's Leap quantum cloud service." NEC and D-Wave will also work together on a number of marketing and sales activities to spur interest in hybrid quantum systems, with NEC parting with $10 million as an investment in D-Wave.
Antonio J. Martinez, We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators. We provide an overview of the software architecture and building blocks through several examples and review the theory of hybrid quantum-classical neural networks. We illustrate TFQ functionalities via several basic applications including supervised learning for quantum classification, quantum control, and quantum approximate optimization. Moreover, we demonstrate how one can apply TFQ to tackle advanced quantum learning tasks including meta-learning, Hamiltonian learning, and sampling thermal states.
Skoltech scientists have shown that quantum enhanced machine learning can be used on quantum (as opposed to classical) data, overcoming a significant slowdown common to these applications and opening a "fertile ground to develop computational insights into quantum systems". The paper was published in the journal Physical Review A. Quantum computers utilize quantum mechanical effects to store and manipulate information. While quantum effects are often claimed to be counterintuitive, such effects will enable quantum enhanced calculations to dramatically outperform the best supercomputers. In 2019, the world saw a prototype of this demonstrated by Google as quantum computational superiority. Quantum algorithms have been developed to enhance a range of different computational tasks; more recently this has grown to include quantum enhanced machine learning.