Quantum Supremacy: Checking a Quantum computer with a classical supercomputer
As microelectronics technology nears the end of exponential growth over time, known as Moore’s law, there is a renewed interest in new computing paradigms such as quantum computing. A key step in the roadmap to build a scientifically or commercially useful quantum computer will be to demonstrate its exponentially growing computing power. I will explain how a 7 by 7 array of superconducting xmon qubits with nearest-neighbor coupling, and with programmable single- and two-qubit gate with errors of about 0.2%, can execute a modest depth quantum computation that fully entangles the 49 qubits. Sampling of the resulting output can be checked against a classical simulation to demonstrate proper operation of the quantum computer and compare its system error rate with predictions. With a computation space of 2^49 = 5 x 10^14 states, the quantum computation can only be checked using the biggest supercomputers. I will show experimental data towards this demonstration from a 9 qubit adjustable-coupler “gmon” device, which implements the basic sampling algorithm of quantum supremacy for a computational (Hilbert) space of about 500. We have begun testing of the quantum supremacy chip.
SQUIDS - From Ideas to Instruments and Applications
Still after more than 5 decades after the invention of Superconducting Quantum Interference Devices (SQUIDs), they are driving research as an enabling technology and lead to emerging applications due to their unique properties. This presentation will not provide an exhaustive review on the background, theory and working principles of SQUID sensors and the Josephson effects, but will review the key facets of SQUID design, fabrication, readout circuitry and operation. In terms of fabrication technology, a short excursus will be provided on the differences of low and high temperature SQUIDs, new developments, and specific aspects in their readout circuitry. There is a variety of SQUID readout electronics which enable to use SQUIDs in a number of applications with demanding properties such as bandwidths of more than 100 MHz, exceptional slew rate and dynamic range without compromises on the usable resolution even at very low frequencies. Some examples will be introduced and discussed in view of specific applications.
Of course there is no review article without the fascinating insights into applications of SQUIDs. We will shortly review a number of areas such as non-destructive evaluation, biomagnetic, NMR and geophysical measurements as well as emerging applications in detector physics as high frequency amplifiers and multiplexing circuits.
Superconducting computing: present status and perspectives
Recent rapid growth in high-performance processor applications, such as AI and cryptocurrency, ultimately enhances the demand of more energy-efficient computing technologies, which would not be achievable by the CMOS technology nearing the end of Moore’s law. Superconducting computing based on Boolean logic is thought to be the most promising candidate as post-CMOS computing in terms of performance and energy efficiency. A distinguished feature in the superconducting computing is the availability of two unique logic styles: one is high-speed single- flux-quantum logic and the other is energy-efficient adiabatic logic. After reviewing the present research status in superconducting computing, this talk will present a perspective on energy-efficient superconducting computing based on adiabatic quantum flux parametron (QFP) with introducing new circuit technologies, including EDA tools, a direct-coupled QFP, a reversible QFP, three-dimensional integration and hybridization with CMOS memories. A roadmap toward the realization of superconducting computing will be discussed.
“Super” Neuromorphic Computing with Photonic and Superconducting Devices
We present a hardware platform combining integrated photonics with superconducting electronics for large-scale “super” neuromorphic computing. It is widely recognized that neural networks are effective at providing solutions to problems that are difficult to solve with conventional computational architectures and algorithms. Today, implementation of complex neural networks in dedicated hardware is an active field both in industry and academia. We believe a new approach to is required to implement neuromorphic hardware roughly equivalent to the brain in numbers of neurons and level of interconnectivity. I will describe our progress towards building a superconducting optoelectronic network of devices that uses semiconductor devices and “photons” for communications and “superconducting electronics” for local computation to implement a spiking neural network that has the potential to be scaled to billions of neurons each directly connected to ~10,000 other neurons.