Barbara M. Terhal

Neural network decoders can achieve a lower logical error rate compared to conventional decoders, like minimum-weight perfect matching, when decoding the surface code. Furthermore, these decoders require no prior information about the physical error rates, making them highly adaptable. In this study, we investigate the performance of such a decoder using both simulated and experimental data obtained from a transmon-qubit processor, focusing on small-distance surface codes.

Erwin Schrödinger coined the word entanglement in 1935 in a three-part paper 1 on the “present situation in quantum mechanics.” His article was prompted by Albert Einstein, Boris Podolsky, and Nathan Rosen’s now celebrated EPR paper that had raised fundamental questions about quantum mechanics earlier that year.

One of the main problems in computational physics is predicting the low-energy behavior of many-body quantum systems. The computational complexity of this problem, however, is relatively poorly understood. A recent major progress in this direction has been the no low-energy trivial states (NLTS) theorem; it gives a family of qubit Hamiltonians whose low-energy states cannot be reached by shallow quantum circuits.