Related papers: Towards scalable cryogenic quantum dot biasing usi…
Quantum dots are frequently used as charge sensitive devices in low temperature experiments to probe electric charge in mesoscopic conductors where the current running through the quantum dot is modulated by the nearby charge environment.…
Our recent work (Ayral et al., 2020 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)) showed the first implementation of the Quantum Divide and Compute (QDC) method, which allows to break quantum circuits into smaller fragments with…
This paper presents the first experimental investigation and physical discussion of the cryogenic behavior of a commercial 28 nm bulk CMOS technology. Here we extract the fundamental physical parameters of this technology at 300, 77 and 4.2…
In this article, we proposed a Variable threshold MOSFET(VTMOS)approach which is realized from Dynamic Threshold MOSFET(DTMOS), suitable for sub-threshold digital circuit operation. Basically the principle of sub- threshold logics is…
Machine learning algorithms have been proven effective for essential quantum computation tasks such as quantum error correction and quantum control. Efficient hardware implementation of these algorithms at cryogenic temperatures is…
Semiconductor quantum dot arrays are a promising platform to perform spin-based error-corrected quantum computation with large numbers of qubits. However, due to the diverging number of possible charge configurations combined with the…
Pulsed readout of Direct Current (DC) SUperconducting Quantum Interference Device (SQUID) is crucial for experiments which need to be performed at millikelvin temperatures, such as the readout of superconducting and electron spin based…
At the Walther-Mei{\ss}ner-Institut (WMI), a new cryogen-free 3He/4He dilution refrigerator (DR) has been completed; the cryostat will be employed to cool experiments on superconducting quantum circuits for quantum information technology…
Due to its remarkable energy compaction properties, the discrete cosine transform (DCT) is employed in a multitude of compression standards, such as JPEG and H.265/HEVC. Several low-complexity integer approximations for the DCT have been…
Scaling quantum computers requires tight integration of cryogenic control electronics with quantum processors, where Digital-to-Analog Converters (DACs) face severe power and area constraints. We investigate quantum neural network (QNN)…
Solid-state quantum emitters are excellent sources of on-demand indistinguishable or entangled photons and can host long-lived spin memories, crucial resources for photonic quantum information applications. However, their scalability…
Cryogenic semiconductor device models are essential in designing control systems for quantum devices and in benchmarking the benefits of cryogenic cooling for high-performance computing. In particular, the saturation of subthreshold swing…
Single epitaxially-grown semiconductor quantum dots have great potential as single photon sources for photonic quantum technologies, though in practice devices often exhibit non-ideal behavior. Here, we demonstrate that amplitude modulation…
The new emerging non-volatile memory (NVM) devices known as memristors could be the promising candidate for future digital architecture, owing to their nanoscale size and its ability to integrate with the exciting CMOS technology. In this…
A switched-capacitor matrix multiplier is presented for approximate computing and machine learning applications. The multiply-and-accumulate operations perform discrete-time charge-domain signal processing using passive switches and 300 aF…
The extremely low threshold voltage (Vth) of native MOSFETs (Vth~0V@300K) is conducive to the design of cryogenic circuits. Previous research on cryogenic MOSFETs mainly focused on the standard threshold voltage (SVT) and low threshold…
Although classical thermal machines power industries and modern living, quantum thermal engines have yet to prove their utility. Here, we demonstrate a useful quantum absorption refrigerator formed from superconducting circuits. We use it…
Machine learning imitates the basic features of biological neural networks to efficiently perform tasks such as pattern recognition. This has been mostly achieved at a software level, and a strong effort is currently being made to mimic…
The prospect of large-scale quantum computation with an integrated chip of spin qubits is imminent as technology improves. This invites us to think beyond the traditional 2-qubit-gate framework and consider a naturally supported…
Given the prevalence of superconducting platforms for uses in quantum computing and quantum sensing, the simulation of quantum superconducting circuits has become increasingly important for identifying system characteristics and modeling…