Learning-based quantum error mitigation

If NISQ-era quantum computers are to perform useful tasks, they will need to employ powerful error mitigation techniques. Quasi-probability methods can permit perfect error compensation at the cost of additional circuit executions, provided that the nature of the error model is fully understood and sufficiently local both spatially and temporally. Unfortunately these conditions are challenging to satisfy. Here we present a method by which the proper compensation strategy can instead be learned ab initio. Our training process uses multiple variants of the primary circuit where all non-Clifford gates are substituted with gates that are efficient to simulate classically. The process yields a configuration that is near-optimal versus noise in the real system with its non-Clifford gate set. Having presented a range of learning strategies, we demonstrate the power of the technique both with real quantum hardware (IBM devices) and exactly-emulated imperfect quantum computers. The systems suffer a range of noise severities and types, including spatially and temporally correlated variants. In all cases the protocol successfully adapts to the noise and mitigates it to a high degree.Comment: 28 pages, 19 figure

Error statistics and scalability of quantum error mitigation formulas

Quantum computing promises advantages over classical computing in many problems. Nevertheless, noise in quantum devices prevents most quantum algorithms from achieving the quantum advantage. Quantum error mitigation provides a variety of protocols to handle such noise using minimal qubit resources . While some of those protocols have been implemented in experiments for a few qubits, it remains unclear whether error mitigation will be effective in quantum circuits with tens to hundreds of qubits. In this paper, we apply statistics principles to quantum error mitigation and analyse the scaling behaviour of its intrinsic error. We find that the error increases linearly $O(\epsilon N)$ with the gate number $N$ before mitigation and sub-linearly $O(\epsilon' N^\gamma)$ after mitigation, where $\gamma \approx 0.5$, $\epsilon$ is the error rate of a quantum gate, and $\epsilon'$ is a protocol-dependent factor. The $\sqrt{N}$ scaling is a consequence of the law of large numbers, and it indicates that error mitigation can suppress the error by a larger factor in larger circuits. We propose the importance Clifford sampling as a key technique for error mitigation in large circuits to obtain this result

Evolocumab loaded Bio-Liposomes for efficient atherosclerosis therapy

Abstract PCSK9, which is closely related to atherosclerosis, is significantly expressed in vascular smooth muscle cells (VSMCs). Moreover, Proprotein Convertase Subtilisin/Kexin type 9 (PCSK9) mediated phenotypic transformation, abnormal proliferation, and migration of VSMCs play key roles in accelerating atherosclerosis. In this study, by utilizing the significant advantages of nano-materials, a biomimetic nanoliposome loading with Evolocumab (Evol), a PCSK9 inhibitor, was designed to alleviate atherosclerosis. In vitro results showed that (Lipo + M)@E NPs up-regulated the levels of α-SMA and Vimentin, while inhibiting the expression of OPN, which finally result in the inhibition of the phenotypic transition, excessive proliferation, and migration of VSMCs. In addition, the long circulation, excellent targeting, and accumulation performance of (Lipo + M)@E NPs significantly decreased the expression of PCSK9 in serum and VSMCs within the plaque of ApoE−/− mice