Embedding Algorithms for Quantum Annealers with Chimera and Pegasus Connection Topologies

Zbinden, Stefanie; Bärtschi, Andreas; Djidjev, Hristo; Eidenbenz, Stephan

doi:10.1007/978-3-030-50743-5_10

Cited by 47 publications

(31 citation statements)

References 31 publications

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“…4. Since the embedded problems are represented by near complete graphs, CLMM is the best performer, whereas SPMM has inferior performance, as expected based on [31].…”

Section: Minor-embedding Of Mimo ML Decodingsupporting

confidence: 54%

“…In order to embed larger QUBO problems that supersede the limitations of CLIQUE, we use two heuristic approaches that aim to yield higher qubit utilization. Clique-Based MinorMiner (CLMM) and Spring-Based MinorMiner (SPMM) [31] work by finding initial chains of qubits which can be passed to MM as a parameter in hopes of finding better final embedding using these as starting points. CLMM finds initial chains using CLIQUE, which results in the near uniformity of chains.…”

Section: B Minor-embedding Methods With Extended Heuristicsmentioning

confidence: 99%

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Evaluation of Quantum Annealer Performance via the Massive MIMO Problem

Tabi¹,

Marosits

Kallus

et al. 2021

IEEE Access

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Quantum annealing offers an appealing route to handle large-scale optimization problems. Existing Quantum Annealing processing units are readily available via cloud platform access for solving Quadratic Unconstrained Binary Optimization (QUBO) problems. In particular, the novel D-Wave Advantage device has been recently released. Its performance is expected to improve upon the previous stateof-the-art D-Wave 2000Q annealer, due to higher number of qubits and the Pegasus topology. Here, we present a comparative study via an ensemble of Maximum Likelihood (ML) Channel Decoder problems for MIMO scenarios in Centralized Radio Access Network (C-RAN) architectures. The main challenge for exact optimization of ML decoders with ever-increasing demand for higher data rates is the exponential increase of the solution space with problem sizes. Since current 5G solutions mainly use approximate methodologies, Kim et al. leveraged Quantum Annealing for large MIMO problems with Phase Shift Keying and Quadrature Amplitude Modulation scenarios. Here, we extend their work and analyze experiments for more complex modulations and larger MIMO antenna array sizes. By implementing the extended QUBO formulae on the novel annealer architecture, we uncover the limits of state-of-the-art quantum optimization for the massive MIMO ML decoder. We report on the improvements and discuss the uncovered limiting factors learned from the 64-QAM extension. We include the enhanced evaluation of raw annealer sampling via implementation of post-processing methods in the comparative analysis between D-Wave 2000Q and D-Wave Advantage system.

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“…4. Since the embedded problems are represented by near complete graphs, CLMM is the best performer, whereas SPMM has inferior performance, as expected based on [31].…”

Section: Minor-embedding Of Mimo ML Decodingsupporting

confidence: 54%

Section: B Minor-embedding Methods With Extended Heuristicsmentioning

confidence: 99%

Evaluation of Quantum Annealer Performance via the Massive MIMO Problem

Tabi¹,

Marosits

Kallus

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Meanwhile, on the physical device (whose graph is not perfect and lacks several quantum bits and couplings), we were able to embed only three out of the four cases (case 3, with the highest number of conflicts, could not be embedded). We expect that such obstacles will become less restrictive as new embedding algorithms are being developed for both the current Chimera topology and the newest D-Wave Pegasus; see [86,87]. Therefore, it is not unreasonable to expect that the range of problems that can be embedded so that they can be solved on physical hardware will substantially increase in the near future.…”

Section: Initial Studies On the D-wave Advantage Machinementioning

confidence: 99%

Quantum annealing in the NISQ era: railway conflict management

Domino,

Koniorczyk,

Krawiec

et al. 2021

Preprint

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We are in the Noisy Intermediate-Scale Quantum (NISQ) devices' era, in which quantum hardware has become available for application in real-world problems. However, demonstrating the usefulness of such NISQ devices are still rare. In this work, we consider a practical railway dispatching problem: delay and conflict management on single-track railway lines. We examine the issue of train dispatching consequences caused by the arrival of an already delayed train to the network segment being considered. This problem is computationally hard and needs to be solved almost in real-time. We introduce a quadratic unconstrained binary optimization (QUBO) model of this problem, compatible with the emerging quantum annealing technology. The model's instances can be executed on present-day quantum annealers. As a proof-of-concept, we solve selected real-life problems from the Polish railway network using D-Wave quantum annealers. As a reference, we also provide solutions calculated with classical methods, including those relevant to the community (linear integer programming) and a sophisticated algorithm based on tensor networks for solving Ising instances. Our preliminary results illustrate the degree of difficulty of real-life railway instances for the current quantum annealing technology. Moreover, our analysis shows that the new generation of quantum annealers (the advantage system) perform much worse on those instances than its predecessor.

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“…Using the minorminer feature "f ind clique embedding()" that uses heuristics from Ref. [88,89] to adapt the optimal embeddings to broken graphs, we found that a per n optimization via full clique embeddings at each n produced immeasurably high TTS after n > 32, even with the use of "V irtualGraphComposite()", an advanced method for tuning logical qubits for a particular embedding. (This method involves correcting the flux biases of the physical qubits that are skewed due to extended-range ferromagnetic couplings used to form logical qubits.)…”

Section: D-wavementioning

confidence: 99%

3-Regular 3-XORSAT Planted Solutions Benchmark of Classical and Quantum Heuristic Optimizers

Kowalsky,

Albash,

Hen

et al. 2021

Preprint

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With current semiconductor technology reaching its physical limits, special-purpose hardware has emerged as an option to tackle specific computing-intensive challenges. Optimization in the form of solving Quadratic Unconstrained Binary Optimization (QUBO) problems, or equivalently Ising spin glasses, has been the focus of several new dedicated hardware platforms. These platforms come in many different flavors, from highly-efficient hardware implementations on digital-logic of established algorithms to proposals of analog hardware implementing new algorithms. In this work, we use a mapping of a specific class of linear equations whose solutions can be found efficiently, to a hard constraint satisfaction problem (3-regular 3-XORSAT, or an Ising spin glass) with a 'golf-course' shaped energy landscape, to benchmark several of these different approaches. We perform a scaling and prefactor analysis of the performance of Fujitsu's Digital Annealer Unit (DAU), the D-Wave Advantage quantum annealer, a Virtual MemComputing Machine, Toshiba's Simulated Bifurcation Machine (SBM), the SATonGPU algorithm from the JANUS collaboration, and our implementation of parallel tempering. We identify the SATonGPU and DAU as currently having the smallest scaling exponent for this benchmark, with SATonGPU having a small scaling advantage and in addition having by far the smallest prefactor thanks to its use of massive parallelism. Our work provides an objective assessment and a snapshot of the promise and limitations of dedicated optimization hardware relative to a particular class of optimization problems.

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Embedding Algorithms for Quantum Annealers with Chimera and Pegasus Connection Topologies

Cited by 47 publications

References 31 publications

Evaluation of Quantum Annealer Performance via the Massive MIMO Problem

Evaluation of Quantum Annealer Performance via the Massive MIMO Problem

Quantum annealing in the NISQ era: railway conflict management

3-Regular 3-XORSAT Planted Solutions Benchmark of Classical and Quantum Heuristic Optimizers

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