![]() ![]() These results allow us to give the first estimations of error correction thresholds for a universal non-Abelian quantum error correcting code. By simulating the effect of noise on this code, and the subsequent recovery processes, we obtain the logical error rate as a function of the intensity of the noise. We devise a set of measurement operators and the corresponding quantum circuits, which allow us to measure the charge of anyonic quasiparticles created by microscopic errors on physical qubits. Our focus is a particular topological quantum error correcting code, based on a modified version of what is known as the Fibonacci Levin-Wen string-net model. ![]() Hence, when a topological code is subjected to noise, the resulting state can be interpreted as containing clusters of anyonic excitations, which must be annihilated in pairs to recover the encoded information. One of the defining characteristics of such models is that their excited states contain anyons, quasiparticles that do not behave like bosons or fermions (the two main classifications of subatomic particles). In this approach, the logical quantum state that we wish to protect is encoded in the degenerate ground space of a 2D topological model. Here, we provide estimates on the performance of one of these codes.Ī very promising class of quantum error correcting codes are topological codes. Hence, one of the main challenges for achieving a universal quantum computer is the development of techniques, known as quantum error correcting codes, to protect quantum information against errors. Such quantum computers are, however, vulnerable to noise from the environment or imperfect hardware, as this destroys the coherence of the quantum states used in computations. Both of these schemes can be implemented with existing hardware.The use of quantum states for computing purposes will enable computations that are intractable for classical computers, such as the simulation of quantum many-body systems. Subject to a continuous-time amplitude- plus phase-damping noise model on five qubits, the sim- ulated QVECTOR algorithm learns encoding and decoding circuits which exploit the coherence among Pauli errors in the noise model to outperform the five-qubit stabilizer code and any other scheme that does not leverage such coherence. We find that, subject to phase damping noise, the simulated QVECTOR algorithm learns a three-qubit encoding and recovery which extend the effective T2 of a quantum memory six-fold. Researchers have now developed a novel error. ![]() We develop this approach for the task of preserving quantum memory and analyze its performance with simulations. This will allow researchers to run calculations on a quantum computer that they cant do without error mitigation. The quantum variational error corrector (QVECTOR) algorithm employs a quantum circuit with parameters that are variationally-optimized according to pro- cessed data originating from quantum sampling of the device, so as to learn encoding and error-recovery gate sequences. This method aims to optimize the average fidelity of encoding and recovery circuits with respect to the actual noise in the device, as opposed to that of an artificial or approximate noise model. Error correction remains an active area of research, and progress on the experimental front has brought practical implementation closer to reality, with a series of advances reported this year. Build- ing from recent quantum machine learning techniques, we propose an alternative approach to quantum error correction aimed at reducing this overhead, which can be implemented in existing quantum hard- ware and on a myriad of quantum computing architectures. Leading proposals, such as the color code and surface code schemes, must devote a large fraction of their physical quantum bits to quantum error correction. Current approaches to fault-tolerant quantum computation will not enable useful quantum computation on near term devices of 50 to 100 qubits. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |