Abstractions considered helpful: A tools architecture for quantum annealers

Booth, Michael; Dahl, Edward D.; Furtney, Mark; Reinhardt, Steven P.

doi:10.1109/hpec.2016.7761625

Cited by 8 publications

(4 citation statements)

References 3 publications

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“…We discovered that 9 of the selected MQlib instances appear not to meet the selection criteria of only a single heuristic achieving the best known solution, so those instances are not as hard as expected. 1 Due to the limited detail in [14], we cannot compare our results to those of their hybrid solver.…”

Section: Miscellanymentioning

confidence: 99%

“…This approach consists of four main threads. First, given the uncertainty in when quantum advantage will be delivered and in the details of potential early QCs (e.g., architecture, number of qubits, and gates natively implemented) that may deliver quantum advantage, application-development formulations and tools must insulate developers from that uncertainty, including machine-specific details, to the extent practical while still delivering quantum advantage to user applications as soon as QC hardware makes it possible [1]. (Obviously the presence or absence of huge QC performance speed-ups cannot be hidden, but the differences in programming those QCs can be.)…”

Section: Introductionmentioning

confidence: 99%

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QCI Qbsolv Delivers Strong Classical Performance for Quantum-Ready Formulation

Booth,

Berwald,

Chukwu

et al. 2020

Preprint

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Many organizations that vitally depend on computation for their competitive advantage are keen to exploit the expected performance of quantum computers (QCs) as soon as quantum advantage is achieved. The best approach to deliver hardware quantum advantage for high-value problems is not yet clear. This work advocates establishing quantum-ready applications and underlying tools and formulations, so that software development can proceed now to ensure being ready for quantum advantage. This work can be done independently of which hardware approach delivers quantum advantage first. The quadratic unconstrained binary optimization (QUBO) problem is one such quantum-ready formulation. We developed the next generation of qbsolv, a tool that is widely used for sampling QUBOs on early QCs, focusing on its performance executing purely classically, and deliver it as a cloud service today. We find that it delivers highly competitive results in all of quality (low energy value), speed (time to solution), and diversity (variety of solutions). We believe these results give quantum-forward users a reason to switch to quantum-ready formulations today, reaping immediate benefits in performance and diversity of solution from the quantum-ready formulation, preparing themselves for quantum advantage, and accelerating the development of the quantum computing ecosystem.

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Section: Miscellanymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

QCI Qbsolv Delivers Strong Classical Performance for Quantum-Ready Formulation

Booth,

Berwald,

Chukwu

et al. 2020

Preprint

Self Cite

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“…Our approach is to target QMASM [49], which is a "quantum macro assembler" in the sense that it provides small but important programming conveniences over the raw hardware model. Just as it is more convenient to express an x86 addition instruction symbolically as addl %esi, %eax than as the binary 0000 0001 1111 0000, QMASM lets programmers write functions symbolically, as in Listing 1, as opposed to numerically, as in [46]), or run them indirectly through qbsolv [8,21], which can split large problems into sub-problems that fit on the D-Wave hardware, • reports the solution to Equation (1) in terms of the program-specified symbolic names rather than as physical qubit numbers, • accepts a command-line option to bias specified variables toward True or False, and • can run a program arbitrarily many times and report statistics on the results-important because all quantum computers are fundamentally stochastic.…”

Section: Lowering Edif To Qmasmmentioning

confidence: 99%

Targeting Classical Code to a Quantum Annealer

Pakin

2019

Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Syst

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From a compiler's perspective, a quantum annealer represents a fundamentally different hardware target from a CPU, GPU, or other von Neumann architecture. Quantum annealers are special-purpose computers that use quantum effects to heuristically determine the set of Boolean variables that minimize a quadratic pseudo-Boolean function (an NP-hard problem). Natively programming such systems involves supplying them with a vector of function coefficients and receiving a vector of function-minimizing Booleans in return. The contribution of this work is to demonstrate how to compile conventional code into a minimization problem for solution on a quantum annealer. The resulting code can run either forward (from inputs to outputs) or backward (from outputs to inputs). We show how this capability can be exploited to simplify the expression and solution of problems in the NP complexity class.

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“…However, the translation of the SVM construction to QUBO does not scale well and creates a graph that far exceeds the capabilities of the current generation quantum annealers. Tools exist to solve QUBOs that large classically or even with the support of the quantum annealer on specific sub-problems [35], but it was noticed that run-times jumped from sub-minute performance to over an hour. On a classical machine, the QUBO encoding has a quadratic disadvantage with respect to the number of data points used.…”

Section: B Quantum Annealermentioning

confidence: 99%

Integration and Evaluation of Quantum Accelerators for Data-Driven User Functions

Hubregtsen¹,

Segler²,

Pichlmeier³

et al. 2019

Preprint

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Quantum computers hold great promise for accelerating computationally challenging algorithms on noisy intermediate-scale quantum (NISQ) devices in the upcoming years. Much attention of the current research is directed towards algorithmic research on artificial data that is disconnected from live systems, such as optimization of systems or training of learning algorithms. In this paper we investigate the integration of quantum systems into industry-grade system architectures. In this work we propose a system architecture for the integration of quantum accelerators. In order to evaluate our proposed system architecture we investigated various data-driven functions for various accelerators, including a classical system, a gatebased quantum accelerator and a quantum annealer. The data-driven function predict user preference and is trained on real-world data. This work also includes an evaluation of the quantum enhanced kernel, that previously was only evaluated on artificial data. In our evaluation, we showed that the quantum-enhanced kernel performs at least equally well to a classical state-of-the-art kernel when simulated. We also showed a low reduction in accuracy and latency numbers within acceptable bounds when running on the gate-based IBM quantum accelerator. We therefore conclude it is feasible to integrate NISQ-era devices in industry-grade system architectures in preparation for future advancements in quantum hardware.

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Abstractions considered helpful: A tools architecture for quantum annealers

Cited by 8 publications

References 3 publications

QCI Qbsolv Delivers Strong Classical Performance for Quantum-Ready Formulation

QCI Qbsolv Delivers Strong Classical Performance for Quantum-Ready Formulation

Targeting Classical Code to a Quantum Annealer

Integration and Evaluation of Quantum Accelerators for Data-Driven User Functions

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