Combining multiplexed gate-based readout and isolated CMOS quantum dot arrays
Pierre Hamonic, Martin Nurizzo, Jayshankar Nath, Matthieu C., Dartiailh, Victor El-Homsy, Mathis Fragnol, Biel Martinez, Pierre-Louis, Julliard, Bruna Cardoso Paz, Mathilde Ouvrier-Buffet, Jean-Baptiste, Filippini, Benoit Bertrand, Heimanu Niebojewski, Christopher B\"auerle

TL;DR
This paper demonstrates a scalable method for reading out and controlling individual spins in large quantum dot arrays by combining electron loading and multiplex gate-based reflectometry, avoiding the need for charge sensors.
Contribution
It introduces a novel approach that isolates quantum dot arrays and employs multiplex gate-based reflectometry for scalable, sensor-free spin readout in quantum computing architectures.
Findings
Achieved single-spin occupancy in foundry-fabricated arrays.
Demonstrated multiplex gate-based reflectometry for charge and spin state detection.
Proved arrays can be electrostatically tuned and scaled without charge sensors.
Abstract
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 limited sensitivity of large-footprint charge sensors, achieving single-spin occupancy in each dot in a growing quantum dot array is exceedingly complex. Therefore, to scale-up a spin-based architecture we must change how individual charges are readout and controlled. Here, we demonstrate single-spin occupancy of each dot in a foundry-fabricated array by combining two methods. 1/ Loading a finite number of electrons into the quantum dot array; simplifying electrostatic tuning by isolating the array from the reservoirs. 2/ Deploying multiplex gate-based reflectometry to dispersively probe charge tunneling and spin states without charge sensors or…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
