The latest in Quantum Thinking
We demonstrate a modular solid state architecture with deterministic inter-module coupling between four physically separate, interchangeable superconducting qubit integrated circuits.
Learn MoreWe present a novel transmon qubit fabrication technique that yields systematic improvements in T1 coherence times. We fabricate devices using an encapsulation strategy that involves passivating the surface of niobium and thereby preventing the formation of its lossy surface oxide.
Learn MoreUsing a superconducting quantum computer, we experimentally investigate the performance of a hybrid quantum-classical algorithm inspired by semidefinite programming approaches for solving the maximum cut problem on 3-regular graphs up to several thousand variables. We leverage the structure of the input problems to address sizes beyond what current quantum machines can naively handle.
Learn MoreWe demonstrate the benefits of using a quantum algorithm rather than its classical counterpart on one of the most fundamental problems of quantitative finance– classification of probability distributions. This problem has many direct applications to practical financial use cases including time series analysis, detection of structural breaks, and monitoring of alpha decay. We present an efficient quantum two-sample test analogous to the classical maximum mean discrepancy test. Experimental results are obtained on Rigetti’s Ankaa-2 quantum computer, applied to a specific instance of the probability distribution classification problem.
Learn MoreThis work provides insight into the formation of Nb hydrides and their role in microwave loss, thus guiding ongoing efforts to maximize coherence time in superconducting quantum devices.
Learn MoreIn this work, we explore the use of dynamical decoupling (DD) in characterizing undesired two-qubit couplings as well as the underlying single-qubit decoherence, and in suppressing them. We develop a syncopated dynamical decoupling technique which protects against decoherence and selectively targets unwanted two-qubit interactions, overcoming both significant hurdles to achieving precise quantum control and realizing quantum computing on many hardware prototypes
Learn MoreHere, we introduce an iterative quantum heuristic optimization algorithm to solve combinatorial optimization problems. The quantum algorithm reduces to a classical greedy algorithm in the presence of strong noise. We implement the quantum algorithm on a programmable superconducting quantum system using up to 72 qubits for solving paradigmatic Sherrington-Kirkpatrick Ising spin glass problems. We find the quantum algorithm systematically outperforms its classical greedy counterpart, signaling a quantum enhancement.
Learn MoreHere, we demonstrate coherent optical control of a superconducting qubit. We achieve this by developing a microwave-optical quantum transducer that operates with up to 1.18% conversion efficiency (1.16% cooperativity) and demonstrate optically-driven Rabi oscillations (2.27 MHz) in a superconducting qubit without impacting qubit coherence times (800 ns). Finally, we discuss outlooks towards using the transducer to network quantum processor nodes.
Learn MoreSuperconducting quantum processors have made significant progress in size and computing potential. As a result, the practical cryogenic limitations of operating large numbers of superconducting qubits are becoming a bottleneck for further scaling. Due to the low thermal conductivity and the dense optical multiplexing capacity of telecommunications fiber, converting qubit signal processing to the optical domain using microwave-to-optics transduction would significantly relax the strain on cryogenic space and thermal budgets. Here, we demonstrate high-fidelity multi-shot optical readout through an optical fiber of a superconducting transmon qubit connected via a coaxial cable to a fully integrated piezo-optomechanical transducer.
Learn MoreA limited set of tools exist for assessing whether the behavior of quantum machine learning models diverges from conventional models, outside of abstract or theoretical settings. We present a systematic application of explainable artificial intelligence techniques to analyze synthetic data generated from a hybrid quantum-classical neural network adapted from twenty different real-world data sets, including solar flares, cardiac arrhythmia, and speech data.
Learn MoreWe introduce a relax-and-round approach embedding the quantum approximate optimization algorithm (QAOA) with p≥1 layers.
Learn MoreWe consider the approximate state estimation of readout-mitigated expectation values, and how to best implement that procedure on the Rigetti quantum computing hardware. We discuss the theoretical aspects involved, providing an explicit computation of the effect of readout error on the estimated expectation values and how to mitigate that effect.
Learn MoreWhat is the optimal circuit depth that provides the best solution? Here, we address this question by investigating an adiabatic circuit that interpolates between the paramagnetic and ferromagnetic ground states of the one-dimensional quantum Ising model.
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