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IEEE Transactions on Applied Superconductivity

Implementation of new superconducting neural circuits using coupled SQUIDs

Yoshinao Mizugaki, Kensuke Nakajima, Yasuji Sawada, Tsutomu Yamashita

Published Mar 1, 1994• 39 citations
Publisher

Abstract

A novel superconducting neuron circuit and two types of variable synapses, which are based on superconducting quantum interferometer devices (SQUIDs), are presented. A neuron circuit with good input-output isolation and steep threshold characteristics is accomplished using a combination of a single-junction SQUID coupled to a double-junction SQUID. The quantum state of the single-junction SQUID represents the neuron state, and the output voltage of the double-junction SQUID, which is operated in a nonlatching mode with shunt resistors, is a sigmoid-shaped function of the input. Both variable synapse circuits are composed of multiple shunted double-junction SQUIDs. The first type changes its conductance value by using both superconducting and voltage states. The second variable synapse circuit changes its output current digitally by switching its bias currents. Besides numerical simulations of the circuit characteristics, we have fabricated superconducting neural chips in a Nb/AlO/sub x//Nb Josephson junction technology. The fundamental operation of each element and a 2-bit neural-based A/D converter have been successfully tested. A learning system with a variable synapse is also discussed.>

DOI
10.1109/77.273058

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A novel superconducting neuron circuit and two types of variable synapses, which are based on superconducting quantum interferometer devices (SQUIDs), are presented. A neuron circuit with good input-output isolation and steep threshold characteristics is accomplished using a combination of a single-junction SQUID coupled to a double-junction SQUID. The quantum state of the single-junction SQUID represents the neuron state, and the output voltage of the double-junction SQUID, which is operated in a nonlatching mode with shunt resistors, is a sigmoid-shaped function of the input. Both variable synapse circuits are composed of multiple shunted double-junction SQUIDs. The first type changes its conductance value by using both superconducting and voltage states. The second variable synapse circuit changes its output current digitally by switching its bias currents. Besides numerical simulations of the circuit characteristics, we have fabricated superconducting neural chips in a Nb/AlO/sub x//Nb Josephson junction technology. The fundamental operation of each element and a 2-bit neural-based A/D converter have been successfully tested. A learning system with a variable synapse is also discussed.>
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We present the experimental implementation of an RS flip-flop (RS-FF) composed of dc-biased coupled-SQUID (C-SQUID) gates. The C-SQUID gate is a combination of a single-junction SQUID and a double-junction SQUID. This gate utilizes nonhysteretic Josephson junctions and it is operated in nonlatching mode with dc-biasing. Several logical functions are able to be realized with a C-SQUID gate by adjusting the input bias and the input signal levels. The speed performance of the gate is evaluated by simulation for ring oscillators, and the minimum switching delay of 6.5 ps/stage is obtained under Josephson critical current density of 10 kA/cm/sup 2/. We have fabricated the RS-FF composed of two C-SQUID NOR gates. The circuit is integrated using a Nb/AlO/sub x//Nb junction technology and its operation is demonstrated experimentally.
High speed, single flux quantum (SFQ) binary scalers are important components in superconducting analog-to-digital converters (ADC). This paper reviews the concept for a SQUID ADC and the design of an SFQ binary ripple counter, and reports the simulation of key components, and fabrication and performance of non-latching SQUID scalers and SFQ binary ripple counters. The SQUIDs were fabricated with Nb/Nb2O5/PbIn junctions and interconnected by monolithic-superconducting transmission lines and isolation resistors. Each SQUID functioned as a bistable flip-flop with the input connected to the center of the device and the output across one junction. All junctions were critically damped to optimize the pulse response. Operation was verified by observing the do I-V curves of successive SQUIDs driven by a cw pulse train generated on the same chip. Each SQUID exhibited constant-voltage current steps at 1/2 the voltage of the preceding device as expected from the Josephson voltage-to-frequency relation. Steps were observed only for the same voltage polarity of successive devices and for proper phase bias of the SQUID. Binary frequency division was recorded up to 40GHz for devices designed to operate to 28GHz.
The utility of classical neural networks as universal approximators suggests that their quantum analogues could play an important role in quantum generalizations of machine-learning methods. Inspired by the proposal in Torrontegui and Garc\'{\i}a-Ripoll [Europhys. Lett. 125, 30004 (2019)], we demonstrate a superconducting qubit implementation of a controlled gate, which generalizes the action of a classical perceptron as the basic building block of a quantum neural network. In a two-qubit setup we show full control over the steepness of the perceptron activation function, the input weight and the bias by tuning the gate length, the coupling between the qubits, and the frequency of the applied drive, respectively. In its general form, the gate realizes a multiqubit entangling operation in a single step, whose decomposition into single- and two-qubit gates would require a number of gates that is exponential in the number of qubits. Its demonstrated direct implementation as perceptron in quantum hardware may therefore lead to more powerful quantum neural networks when combined with suitable additional standard gates.