Noise Reduction in Quantum Computing: An MIT Study Quantum com | Big Data Science

Noise Reduction in Quantum Computing: An MIT Study
Quantum computers are very sensitive to noise interference caused by imperfect control signals, environmental disturbances, and unwanted interactions between qubits. Therefore, researchers at MIT have created QuantumNAS, a framework that can identify the most robust quantum circuit for a particular computational problem and generate a mapping pattern tailored to the target quantum processor's qubits. device. QuantumNAS is much less computationally intensive than other search methods and can identify quantum circuits that improve the accuracy of machine learning and quantum chemistry problems. In classical neural networks, including more parameters often improves model accuracy. But in variational quantum computing, more parameters require more quantum gates, which introduces more noise.
To do this, a super-circuit was first designed with all possible parameterized quantum elements in the design space. This circuit was then trained and used to search for circuit architectures with high noise tolerance. The process includes a simultaneous search for quantum circuits and qubit mappings using an evolutionary search algorithm. This algorithm generates several candidates for displaying quantum circuits and qubits, and then evaluates their accuracy using a noise model or on a real machine. The results are fed back into the algorithm, which chooses the most efficient parts and uses them to restart the process until it finds the perfect candidates. The developers have collected the results of the study into the TorchQuantum open source library https://github.com/mit-han-lab/torchquantum.
https://news.mit.edu/2022/quantum-circuits-robust-noise-0321