Real-Time Error Correction for Quantum Computing

Abstract

An experiment shows that errors in quantum computation can be repeatedly corrected on the fly. By Philip Ball Random errors incurred during computation are one of the biggest obstacles to unleashing the full power of quantum computers. Researchers have now demonstrated a technique that allows errors to be detected and corrected in real time as the computation proceeds. It also allows error correction to be conducted several times on a single quantum bit (qubit) during the calculation [1]. Both features are needed to make the basic elements-the logical qubits-of a fully error-tolerant quantum computer that can be scaled up and used for applications beyond the specialized ones that these machines have tackled so far.

Theoretical error performance analysis for variational quantum circuit based functional regression

“…The imminent of quantum computing devices opens up possibilities for exploiting quantum machine learning (QML) [1][2][3] to improve the efficiency of classical machine learning algorithms in many scientific domains like drug discovery 4 and efficient solar conversion 5 . Although the exploitation of quantum computing devices to carry out QML is still in its early exploratory states, the rapid development in quantum hardware has motivated advances in quantum neural network (QNN) to run in noisy intermediatescale quantum (NISQ) devices [6][7][8][9] , where not enough qubits could be spared for quantum error correction and the imperfect qubits have to be directly employed at the physical layer [10][11][12] . Even though, a compromised QNN is proposed by employing a quantum-classical hybrid model that relies on an optimization of the variational quantum circuit (VQC) 13,14 .…”

Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regression

“…The imminent of quantum computing devices opens up new possibilities for exploiting quantum machine learning (QML) [1][2][3] to improve the efficiency of classical machine learning algorithms in many new scientific domains like drug discovery [4] and efficient solar conversion [5]. Although the exploitation of quantum computing devices to carry out QML is still in its early exploratory states, the rapid development in quantum hardware has motivated advances in quantum neural network (QNN) to run in noisy intermediate-scale quantum (NISQ) devices [6][7][8][9][10], where not enough qubits could be spared for quantum error correction and the imperfect qubits have to be directly employed at the physical layer [11][12][13]. Even though, a compromised QNN is proposed by employing a quantum-classical hybrid model that relies on an optimization of the variational quantum circuit (VQC) [14,15].…”