Abstract
Photon loss is destructive to the performance of quantum photonic devices and therefore suppressing the effects of photon loss is paramount to photonic quantum technologies. We present two schemes to mitigate the effects of photon loss for a Gaussian Boson Sampling device, in particular, to improve the estimation of the sampling probabilities. Instead of using error correction codes which are expensive in terms of their hardware resource overhead, our schemes require only a small amount of hardware modifications or even no modification. Our loss-suppression techniques rely either on collecting additional measurement data or on classical post-processing once the measurement data is obtained. We show that with a moderate cost of classical post processing, the effects of photon loss can be significantly suppressed for a certain amount of loss. The proposed schemes are thus a key enabler for applications of near-term photonic quantum devices.
Highlights
Error is the main hindrance for large scale quantum computation
We present two schemes to mitigate the effects of photon loss for a Gaussian Boson Sampling device, in particular, to improve the estimation of the sampling probabilities
A proof of principle experiment has been performed in a superconducting device and the accuracy of the variational eigensolver has been significantly improved [18]. Another error mitigation technique that is capable of improving the estimation of expectation values is called quasi-probability decomposition technique, in which a noise-free circuit is simulated by a collection of randomly selected noisy circuits following a particular quasiprobability distribution [16, 19]
Summary
To improve the performance of these near-term quantum devices, several error mitigation techniques have been developed to suppress the noise. A proof of principle experiment has been performed in a superconducting device and the accuracy of the variational eigensolver has been significantly improved [18] Another error mitigation technique that is capable of improving the estimation of expectation values is called quasi-probability decomposition technique, in which a noise-free circuit is simulated by a collection of randomly selected noisy circuits following a particular quasiprobability distribution [16, 19]. The first scheme exploits the Richardson extrapolation technique and is tailored to extrapolate the probability of a photon number pattern or a collection of patterns for a loss-free GBS circuit. The second scheme estimates the probability of a click pattern of a loss-free circuit by linearly combining the probabilities of click patterns with higher total photon numbers in the presence of loss This requires no modifications of the circuits since one does not need to vary the loss value as the first scheme.
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