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 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 with low added microwave noise, and we demonstrate optically driven Rabi oscillations in a superconducting qubit.
Learn MoreState-of-the-art classical optimization solvers set a high bar for quantum computers to deliver utility in this domain. Here, we introduce a quantum preconditioning approach based on the quantum approximate optimization algorithm. It transforms the input problem into a more suitable form for a solver with the level of preconditioning determined by the depth of the quantum circuit. We demonstrate that best-in-class classical heuristics such as simulated annealing and the Burer-Monteiro algorithm can converge more rapidly when given quantum preconditioned input for various problems, including Sherrington-Kirkpatrick spin glasses, random 3-regular graph maximum-cut problems, and a real-world grid energy problem.
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 MoreAchieving high-quality solutions faster than classical solvers on computationally hard problems is a challenge for quantum optimization to deliver utility. Using 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. We attain an average approximation ratio of 99% over a random ensemble of thousands of problem instances.
Learn MoreAn ultra-thin aluminum oxide layer is a key component for Josephson junctions in superconducting quantum bits. This layer serves as a barrier layer for Cooper pairs tunneling between the superconducting electrodes and significantly influences the overall performance of the junction. In this study, we investigate the impact of aluminum deposition rates on the microstructure and chemical variation of the aluminum oxide layer, as well as the device's yields and qubits’ lifetimes.
Learn MoreClassical symmetric encryption algorithms use N bits of a shared secret key to transmit N bits of a message over a one-way channel in an information theoretically secure manner. This paper proposes a hybrid quantum-classical symmetric cryptosystem that uses a quantum computer to generate the secret key.
Learn MoreThe performance of superconducting qubits is often limited by dissipation and two-level systems (TLS) losses. The dominant sources of these losses are believed to originate from amorphous materials and defects at interfaces and surfaces, likely as a result of fabrication processes or ambient exposure. Here, we explore a novel wet chemical surface treatment at the Josephson junction-substrate and the substrate-air interfaces by replacing a buffered oxide etch (BOE) cleaning process with one that uses hydrofluoric acid followed by aqueous ammonium fluoride.
Learn MoreWe develop a hardware-efficient ansatz for variational optimization, derived from existing ansatze in the literature, that parametrizes subsets of all interactions in the Cost Hamiltonian in each layer. We treat gate orderings as a variational parameter and observe that doing so can provide significant performance boosts in experiments. We carried out experimental runs of a compilation-optimized implementation of fully-connected Sherrington-Kirkpatrick Hamiltonians on a 50-qubit linear-chain subsystem of Rigetti Aspen-M-3 transmon processor.
Learn MoreHere, we demonstrate low-latency feedback with a scalable FPGA decoder integrated into the control system of a superconducting quantum processor. We perform an 8-qubit stability experiment with up to 25 decoding rounds and a mean decoding time per round below 1 \unit\micro, showing that we avoid the backlog problem even on superconducting hardware with the strictest speed requirements.
Learn MoreQuantum approximate optimization is one of the promising candidates for useful quantum computation, particularly in the context of finding approximate solutions to Quadratic Unconstrained Binary Optimization (QUBO) problems. However, the existing QPUs are relatively small, and canonical mappings of QUBO via the Ising model require one qubit per variable, rendering direct large-scale optimization infeasible. In classical optimization, a general strategy for addressing many large-scale problems is via multilevel/multigrid methods, where the large target problem is iteratively coarsened, and the global solution is constructed from multiple small-scale optimization runs. In this work, we experimentally test how existing QPUs perform as a sub-solver within such a multilevel strategy.
Learn MoreWe demonstrate a transformational technique for controllably tuning the electrical properties of fabricated thermally oxidized amorphous aluminum-oxide tunnel junctions. Using conventional test equipment to apply an alternating bias to a heated tunnel barrier, giant increases in the room temperature resistance, greater than 70%, can be achieved.
Learn MoreWe analyze the experimental error budget of parametric resonance gates in a tunable coupler architecture. We identify and characterize various sources of errors, including incoherent, leakage, amplitude, and phase errors. By varying the two-qubit gate time, we explore the dynamics of these errors and their impact on the gate fidelity.
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