Menu

Quantum-computing related developments

On this page we post about interesting quantum-computing related research and news which we are following.

Decoding quantum errors with subspace expansions

Decoding quantum errors with subspace expansions

Quantum error correction

Currently, the latest state-of-the-art quantum computers are so-called NISQ (noisy intermediate-scale quantum) devices, meaning they have a number of qubits which approaches competition with classical simulation of the output of such systems, yet the systems are noisy and no fault-tolerance can be achieved yet. The question is: are there methods which can sufficiently compensate for their noisy nature, enabling the emergence of quantum advantage on these devices? In recent years, many error correction and mitigation schemes have been developed: from Richardson extrapolation techniques to extend results down to `zero noise’, to parity check measurements and more. But typically, those techniques require additional complicated circuitry, ancillary qubits, pulse modifications, or calibration/tuning steps. In this paper, an alternative strategy based on the general principle of a class of methods called Quantum Subspace Expansion (QSE) is proposed. In this strategy, one performs clever post-processing of classical data with or without additional measurements with (at most) simple additional operations in the circuit and no (scaling) ancillary qubits. This paper generalizes the application of QSE error mitigation to any quantum computation, not restricting itself necessarily to problem-specifics like chemistry. Another interesting idea presented here is to use NISQ devices to experimentally study small quantum codes for later use in larger-scale quantum computers implementing error correcting code, such as in future FTQC (fault-tolerant quantum computing).

Improved error threshold for surface codes with biased noise

Improved error threshold for surface codes with biased noise

Quantum error correction

Topological codes, and the surface code in particular, are popular choices for many quantum computing architectures, because of high error thresholds and local stabilizers. In this paper, Tuckett et al. show that a simple modification of the surface code can exhibit a fourfold  gain in the error correction threshold for a noise model in which Pauli Z errors (dephasing) occur more frequently than X or Y errors (which is common in many quantum architectures, including superconducting qubits). For pure dephasing an improved threshold of 43,7% is found (versus 10.9% for the optimal surface code), while 28,2% applies with a noise-bias-ratio of 10 (more realistic regime).

ZX calculus as a language for surface code lattice surgery in quantum error correction

ZX calculus as a language for surface code lattice surgery in quantum error correction

Quantum error correction

One of the most popular techniques for error-correction is the surface code with logical 2-qubit operations realized via so-called lattice surgery. This popularity is explained a.o. by its high estimated error-correction threshold of 1% and relatively simple correction procedure. In this paper, De Beaudrap et al. demonstrate that lattice surgery is a model for the ZX calculus, an abstract graphical language for tensor networks. ZX calculus therefore provides a ready-made practical 'language' for discussing computations realized using surface codes via lattice surgery.