Measurement error mitigation (MEM) techniques are postprocessing strategies to counteract systematic read-out errors on quantum computers (QC). Currently used MEM strategies face a tradeoff: methods that scale well with the number of qubits return negative probabilities, while those that guarantee a valid probability distribution are not scalable. Here, we present a scheme that addresses both of these issues. In particular, we present a scalable implementation of iterative Bayesian unfolding, a standard mitigation technique used in high-energy physics experiments. We demonstrate our method by mitigating QC data from experimental preparation of Greenberger-Horne-Zeilinger (GHZ) states up to 127 qubits and implementation of the Bernstein-Vazirani algorithm on up to 26 qubits.
Abstract Quantum computing is an emerging technology which will influence the future. Quantum computing is currently limited by the scalability of qubits, especially the qubits manipulation of superconducting computing. In this paper, a superconducting qubits manipulation method based on AWG (Arbitrary Waveform Generator) is proposed. Based on the superconducting quantum computing system, the overall scheme of qubits manipulation is presented, including overall framework, multichannel coherent arbitrary waveform generation design, quantum state reading and acquisition design, multichannel synchronization design and quick feedback transceiver design. The simulation and experiment results show that the proposed method has good scalability, high synchronization index and good signal quality, which can meet the requirements of superconducting quantum computing for qubits manipulation.
We have designed, fabricated and tested an XY-addressable readout system that is specifically tailored for the reading of superconducting flux qubits in an integrated circuit that could enable adiabatic quantum optimization. In such a system, the flux qubits only need to be read at the end of an adiabatic evolution when quantum mechanical tunneling has been suppressed, thus simplifying many aspects of the readout process. The readout architecture for an N-qubit adiabatic quantum optimization system comprises N hysteretic dc SQUIDs and N rf SQUID latches controlled by 2N+2 bias lines. The latching elements are coupled to the qubits and the dc SQUIDs are then coupled to the latching elements. This readout scheme provides two key advantages: First, the latching elements provide exceptional flux sensitivity that significantly exceeds what may be achieved by directly coupling the flux qubits to the dc SQUIDs using a practical mutual inductance. Second, the states of the latching elements are robust against the influence of ac currents generated by the switching of the hysteretic dc SQUIDs, thus allowing one to interrogate the latching elements repeatedly so as to mitigate the effects of stochastic switching of the dc SQUIDs. We demonstrate that it is possible to achieve single qubit read error rates of <10−6 with this readout scheme. We have characterized the system-level performance of a 128-qubit readout system and have measured a readout error probability of 8×10−5 in the presence of optimal latching element bias conditions.