Search NASA⌕ Search

SEARCH · Search NASA

Results for “Rigetti”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

3 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Dynamical Decoupling of Crosstalk on Superconducting Qubit Devices

Current NISQ devices are prone to errors. In order to be used for practical applications or achieve fault-tolerant thresholds, strategies to suppress error rates will be needed to maximize the potential of noisy devices. Dynamical decoupling (DD) is one such strategy for suppressing — or at least alleviating — the effects of decoherence, in which sequences of pulses are applied to qubits to decouple their interaction with the environment. Through experimental runs performed on several Rigetti quantum computing units (QPUs), we first demonstrate that DD is capable of improving coherence times for isolated qubits, as well as suppressing errors caused by the ZZ coupling between pairs of qubits. Extending this framework to cycles containing2-qubit gates, we show that DD can be inserted to decouple qubits from crosstalk occurring during neighboring 2-qubit gates, and demonstrate the efficacy of this procedure on quantum approximate optimization algorithm (QAOA) circuits. We also explore the usage of tailored DD sequences for the suppression of characterized error channels. We are grateful for support from the NASA Ames Research Center and from the DARPA ONISQ program under interagency agreement IAA 8839,Annex 114. HYH is supported by the USRA Feynman QuantumAcademy funded by the NAMS R&D Student Program and a UCHellman Fellowship. JS, ZGI and ZW are supported by USRA NASAAcademic Mission Service (NNA16BD14C).

Dynamical decoupling↗
NASA NTRSDate not supplied

Hardware-Efficient Quantum Optimization Layered Algorithms and Experiments

Quantum optimization algorithms, such as QAOA, that implement parametrized stochastic optimization solvers attempt to identify low-energy solutions of Ising systems by exploiting available quantum effects in noisy-intermediate scale machines. Engineering a well-performing parametrized quantum optimization circuit is indeed an exercise in balancing the trade-off between expressivity and implementation complexity. We show that, for MaxCut QAOA circuits defined on native hardware topology (Rigetti’s Aspen Quantum Processors), error-mitigation techniques recover simulated features of the noiseless theory. Moreover, we explore a design space for QAOA-like ansatze that perform well in theory as well as in hardware for fully-connected problems. We also discuss how efficient coherence and entanglement detection methods that could be coupled with quantum optimization experiments require only linear overhead in benchmarking time.

quantum computing↗
NASA NTRSDate not supplied

A Multilevel Approach For SolvingLarge-Scale QUBO Problems With Noisy Hybrid Quantum Approximate Optimization

Quantum approximate optimization is one ofthe promising candidates for useful quantum computation,particularly in the context of finding approximate solutionsto Quadratic Unconstrained Binary Optimization (QUBO)problems. However, the existing quantum processing units(QPUs) are of relatively small size, and canonical mappingsof QUBO via the Ising model require one qubit per vari-able, rendering direct large-scale optimization infeasible.In classical optimization, a general strategy for addressingmany large-scale problems is via multilevel/multigrid meth-ods, where the large target problem is iteratively coarsenedand the global solution is constructed from multiple small-scale optimization runs. In this work, we experimentallytest how existing QPUs perform when used as a sub-solverwithin such a multilevel strategy. To this aim, we com-bine and extend (via additional classical processing steps)the recently proposed Noise-Directed Adaptive Remapping(NDAR) and Quantum Relax&Round (QRR) algorithms.We first demonstrate the effectiveness of our heuristicextensions on Rigetti’s superconducting transmon deviceAnkaa-2. We find approximate solutions to10instances offully connected82-qubit Sherrington-Kirkpatrick graphswith random integer-valued coefficients obtaining normal-ized approximation ratios (ARs) in the range∼0.98−1.0,and the same class with real-valued coefficients (ARs∼0.94−1.0). Then, we implement the extended NDAR andQRR algorithms as subsolvers in the multilevel algorithmfor6large-scale graphs with at most∼27,000variables.In practice, the QPU (with classical post-processing steps)is used to find approximate solutions to dozens of at most82-qubit problems, which are iteratively used to constructthe global solution. We observe that quantum optimizationresults are competitive in terms of the quality of solutionswhen compared to classical heuristics used as subsolverswithin the multilevel approach.Reproducibility: source code and data are available at[TBA upon acceptance]

quantum computing↗