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The quantum annealing gap and quench dynamics in the exact cover problem
Bernhard Irsigler,Tobias Grass +1 more
TLDR
In this article, the relation between quench and annealing dynamics can be exploited to reproduce the full functional behavior of the gap from the quench data, which can be used to design optimized quantum annesaling protocols with a practical time-to-solution benefit.Abstract:
Quenching and annealing are extreme opposites in the time evolution of a quantum system: Annealing explores equilibrium phases of a Hamiltonian with slowly changing parameters and can be exploited as a tool for solving complex optimization problems. In contrast, quenches are sudden changes of the Hamiltonian, producing a non-equilibrium situation. Here, we investigate the relation between the two cases. Specifically, we show that the minimum of the annealing gap, which is an important bottleneck of quantum annealing algorithms, can be revealed from a dynamical quench parameter which describes the dynamical quantum state after the quench. Combined with statistical tools including the training of a neural network, the relation between quench and annealing dynamics can be exploited to reproduce the full functional behavior of the annealing gap from the quench data. We show that the partial or full knowledge about the annealing gap which can be gained in this way can be used to design optimized quantum annealing protocols with a practical time-to-solution benefit. Our results are obtained from simulating random Ising Hamiltonians, representing hard-to-solve instances of the exact cover problem.read more
Citations
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Hard-instance learning for quantum adiabatic prime factorization
TL;DR: In this article , a deep reinforcement learning (RL) method was applied to configure the AQC algorithm for prime factorization, where the success probability of the worst-case problem instances was set as the reward to RL.
Journal ArticleDOI
Quantum Annealing Sampling with a Bias Field
TL;DR: In this article , the authors investigate the effect of bias fields on the outcome of quantum annealing sampling, with the example of the exact cover problem, and different bias configurations are benchmarked against the unbiased sampling procedure.
References
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Quantum Computing in the NISQ era and beyond
TL;DR: Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future, and the 100-qubit quantum computer will not change the world right away - but it should be regarded as a significant step toward the more powerful quantum technologies of the future.
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A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem
TL;DR: For the small examples that the authors could simulate, the quantum adiabatic algorithm worked well, providing evidence that quantum computers (if large ones can be built) may be able to outperform ordinary computers on hard sets of instances of NP-complete problems.
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Quantum annealing in the transverse Ising model
TL;DR: In this article, the authors introduce quantum fluctuations into the simulated annealing process of optimization problems, aiming at faster convergence to the optimal state. But quantum fluctuations cause transitions between states and thus play the same role as thermal fluctuations in the conventional approach.
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Quantum annealing with manufactured spins
Mark W. Johnson,M. H. S. Amin,Gildert Suzanne,Trevor Lanting,Firas Hamze,Neil G. Dickson,Richard Harris,Andrew J. Berkley,J. Johansson,Paul I. Bunyk,E. M. Chapple,C. Enderud,Jeremy P. Hilton,Kamran Karimi,E. Ladizinsky,N. Ladizinsky,T. Oh,I. Perminov,C. Rich,Murray C. Thom,E. Tolkacheva,C. J. S. Truncik,Sergey Uchaikin,J. Wang,B. Wilson,Geordie Rose +25 more
TL;DR: This programmable artificial spin network bridges the gap between the theoretical study of ideal isolated spin networks and the experimental investigation of bulk magnetic samples, and may provide a practical physical means to implement a quantum algorithm, possibly allowing more-effective approaches to solving certain classes of hard combinatorial optimization problems.
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Quantum simulations with trapped ions
TL;DR: In this paper, the authors present a review of experiments in controlling and manipulating trapped atomic ions, together with the methods and tools that have enabled them, and provide an outlook on future directions in the field.