The approach outlines a way to teach an AI to make an interconnected set of adjustments to quantum dots, which are among the many promising devices for creating the quantum bits (qubits) that form the switches in a quantum computer’s processor. Precisely tweaking the dots, say the researchers, is crucial for transforming them into properly functioning qubits, and until now the job had to be done painstakingly by human operators, requiring hours of work to create even a small handful of qubits for a single calculation.
A practical quantum computer with many interacting qubits requires far more dots – and adjustments – than a human could manage, so the accomplishment, say the researchers, might bring quantum dot-based processing closer from the realm of theory to engineered reality.
“Quantum computer theorists imagine what they could do with hundreds or thousands of qubits, but the elephant in the room is that we can actually make only a handful of them work at a time,” says Justyna Zwolak, a NIST mathematician. “Now we have a path forward to making this real.”
A quantum dot typically contains electrons that are confined to a tight boxlike space in a semiconductor material. The box’s “walls” are several metallic electrodes – or gates – above the semiconductor surface that have electric voltage applied to them, influencing the quantum dot’s position and number of electrons. Depending on their position relative to the dot, the gates control the electrons in different ways.
To control the dots – for example, to make them act as one sort of qubit logic switch or another – the gate voltages must be tuned to just the right values. This tuning is done manually, by measuring currents flowing through the quantum dot system, then changing the gate voltages a bit, then checking the current again. And the more dots (and gates) involved, the harder it is to tune them all simultaneously so that the qubits work together properly.
“It’s usually a job done by a graduate student,” says graduate student Tom McJunkin of the University of Wisconsin-Madison’s physics department and a co-author of a paper on the research. “I could tune one dot in a few hours, and two might take a day of twiddling knobs. I could do four, but not if I need to go home and sleep. As this field grows, we can’t spend weeks getting the system ready – we need to take the human out of the picture.”
The data McJunkin worked with were visual images, which the researchers realized were something that AI is good at recognizing. Convolutional neural networks have become the “go-to” technique for automated image classification, as long as they are exposed to lots of examples of what they need to recognize. So the researchers created a simulator that would generate thousands of images of quantum dot measurements they could feed to the AI as a training exercise.
“We simulate the qubit setup we want and run it overnight, and in the morning we have all the data we need to train the AI to tune the system automatically,” says Zwolak. “And we designed it to be usable on any quantum dot-based system, not just our own.”
The researchers began by using a setup of two quantum dots, and verified that within certain constraints their trained AI could auto-tune the system to the setup they desired. While the approach wasn’t perfect and they can’t use it to tune thousands of interconnected quantum dots as yet, they say, even at this early stage its practical power is undeniable, allowing a skilled researcher to spend valuable time elsewhere.
“It’s a way to use machine learning to save labor, and — eventually — to do something that human beings aren’t good at doing,” says Zwolak. “We can all recognize a three-dimensional cat, and that’s basically what a single dot with a few properly-tuned gates is. Lots of dots and gates are like a 10-dimensional cat. A human can’t even see a 10-D cat. But we can train an AI to recognize one.”
For more, see “Auto-tuning of double dot devices in situ with machine learning.”
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