Unlike digital bits in a classical computer, which are represented as either "on" or "off," a quantum bit (qubit) has a much higher failure rate — roughly 1 in 1,000, compared with 1 in 1 billion for digital bits. That's because quantum computers are susceptible to "noise" — interference that's often cited as the biggest barrier preventing quantum computers from being more capable than the fastest supercomputers.
That's because noise comes from various sources, many of which scientists have no control over. These include unpredictable disturbances in Earth's magnetic field, nearby radiation from Wi-Fi routers and other electronic devices, cosmic rays from space, and even neighboring qubits. This unpredictability has made it difficult to study this noise.
In the new study, published May 9 in the journal Nature Communications, the researchers described how this quantum computing chip uses photons captured from laser pulses as qubits. It also has what the researchers called a "side channel" that photons can be diverted to so the team could imitate the losses that occur under normal operating conditions and study them in detail.
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The chip can be programmed to imitate errors in multiple ways, thus making it possible to simulate specific types of loss due to noise. The researchers can essentially modulate the amount of noise the system simulates in order to generate conditions for practical study. They do this by adjusting the number of photons that get sidetracked and the degree of quantum superposition, in which qubits share information over space and time through a process called quantum entanglement.
This means the noise itself becomes an asset that scientists can use to further improve quantum computing systems, rather than trying to eliminate it.
Related storiesAccording to the study, the novel chip design can model errors in any type of quantum system — even a non-photonic system, like a superconducting qubit-based quantum computer or one designed with neutral atom qubits.
"Understanding how quantum systems behave under this messiness is crucial if we want our experiments to say something about nature as it really is, not just idealized setups," Krishna said.
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