The search for robust qubit technology has followed many paths. Superconducting circuits is perhaps the dominant approach but performance fluctuations have imposed limitations, among them easy scalability. A new paper by Google researchers suggests these performance fluctuations are dominated by material defects and that, fascinatingly, qubits themselves provide an effective tool for characterizing the defects in superconducting quantum processors.
“The fact that qubits can be used to investigate individual material defects – which are believed to have atomic dimensions, millions of times smaller than our qubits – demonstrates that they are powerful metrological tools,” wrote Paul Klimov, research scientist, Google AI quantum team on Google’s AI blog last week. “While it’s clear that defect research could help address outstanding problems in materials physics, it’s perhaps surprising that it has direct implications on improving the performance of today’s quantum processors.”
In “Fluctuations of Energy-Relaxation Times in Superconducting Qubits” published last week in Physical Review Letters, Google researchers examined qubits’ energy relaxation times (T1) – “a popular performance metric that gives the length of time that it takes for a qubit to undergo energy-relaxation from its excited to ground state” — as a function of operating frequency and time.
“In measuring T1, we found that some qubit operating frequencies are significantly worse than others, forming energy-relaxation hot-spots. Our research suggests that these hot spots are due to material defects, which are themselves quantum systems that can extract energy from qubits when their frequencies overlap (i.e. are “resonant”). Surprisingly, we found that the energy-relaxation hot spots are not static, but “move” on timescales ranging from minutes to hours. From these observations, we concluded that the dynamics of defects’ frequencies into and out of resonance with qubits drives the most significant performance fluctuations,” wrote Klimov.
Klimov noted defect metrology already informs processor design and fabrication, and even the mathematical algorithms used to avoid defects during quantum processor runtime.
“In addition to clarifying the origin of qubit performance fluctuations, our data shed light on the physics governing defect dynamics, which is an important piece of this puzzle. Interestingly, from thermodynamics arguments we would not expect the defects that we see to exhibit any dynamics at all. Their energies are about one order of magnitude higher than the thermal energy available in our quantum processor, and so they should be ‘frozen out.’ The fact that they are not frozen out suggests their dynamics may be driven by interactions with other defects that have much lower energies and can thus be thermally activated,” wrote Klimov.
Link to Google AI blog: https://ai.googleblog.com