AQUA@home: Difference between revisions

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D-Wave Systems built AQUA@home to support the development of its experimental superconducting [[wikipedia:adiabatic quantum computation|adiabatic quantum optimization]] processors. Before committing hardware resources to a given class of problem, D-Wave researchers wanted to estimate in advance how a quantum annealing processor of a given size and connectivity could be expected to perform.<ref>{{Cite web |url=https://www.nextbigfuture.com/2008/12/dwave-quantum-computer-performance.html |title=Dwave Quantum Computer Performance Estimates and Calculations |website=NextBigFuture.com |date=2008-12-07 |access-date=2026-06-27}}</ref> Simulating the relevant quantum-mechanical spin systems classically using [[wikipedia:Quantum Monte Carlo|Quantum Monte Carlo]] (QMC) methods is computationally demanding, which made the problem well suited to distributed volunteer computing.<ref>{{Cite web |url=https://www.nextbigfuture.com/2008/12/dwave-quantum-computer-performance.html |title=Dwave Quantum Computer Performance Estimates and Calculations |website=NextBigFuture.com |date=2008-12-07 |access-date=2026-06-27}}</ref>

The earliest computations run on AQUA@home calculated expected runtimes for [[wikipedia:spin glass|spin glass]] problems, a class of [[wikipedia:NP-hardness|NP-hard]] [[combinatorial optimization]] problem well suited to the project's processor architecture; this work formed the basis of the project's first publications.<ref>{{Cite web |url=https://www.nextbigfuture.com/2008/12/dwave-quantum-computer-performance.html |title=Dwave Quantum Computer Performance Estimates and Calculations |website=NextBigFuture.com |date=2008-12-07 |access-date=2026-06-27}}</ref> A later phase of the project used AQUA@home to estimate the runtime of the quantum adiabatic algorithm on binary classification problems for [[machine learning]], developed in part with researchers from [[wikipedia:Google|Google]].<ref>{{Cite web |url=https://www.nextbigfuture.com/2008/12/dwave-quantum-computer-performance.html |title=Dwave Quantum Computer Performance Estimates and Calculations |website=NextBigFuture.com |date=2008-12-07 |access-date=2026-06-27}}</ref>

The earliest computations run on AQUA@home calculated expected runtimes for [[wikipedia:spin glass|spin glass]] problems, a class of [[wikipedia:NP-hardness|NP-hard]] [[wikipedia:combinatorial optimization|combinatorial optimization]] problem well suited to the project's processor architecture; this work formed the basis of the project's first publications.<ref>{{Cite web |url=https://www.nextbigfuture.com/2008/12/dwave-quantum-computer-performance.html |title=Dwave Quantum Computer Performance Estimates and Calculations |website=NextBigFuture.com |date=2008-12-07 |access-date=2026-06-27}}</ref> A later phase of the project used AQUA@home to estimate the runtime of the quantum adiabatic algorithm on binary classification problems for [[wikipedia:machine learning|machine learning]], developed in part with researchers from [[wikipedia:Google|Google]].<ref>{{Cite web |url=https://www.nextbigfuture.com/2008/12/dwave-quantum-computer-performance.html |title=Dwave Quantum Computer Performance Estimates and Calculations |website=NextBigFuture.com |date=2008-12-07 |access-date=2026-06-27}}</ref>

== Software and technical milestones ==

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{{#ev:youtube|VyQgGkj_Rss|700|center|AQUA@home screensaver in action}}

In May 2010, AQUA@home deployed the first [[OpenCL]] application to run under BOINC, allowing its computations to be accelerated across a mix of multicore CPUs and GPUs from different vendors using a single, portable codebase.<ref>{{Cite web |url=https://boinc.berkeley.edu/old_news.php |title=News archive |website=BOINC |access-date=2026-06-27}}</ref> Project researchers subsequently published a direct performance comparison between [[CUDA]] and OpenCL implementations of the same Quantum Monte Carlo computation kernel.<ref>{{Cite journal |last1=Karimi |first1=Kamran |last2=Dickson |first2=Neil |last3=Hamze |first3=Firas |title=A Performance Comparison of CUDA and OpenCL |date=2011-05-16 |url=https://arxiv.org/abs/1005.2581}} DOI: [https://doi.org/10.48550/arXiv.1005.2581 10.48550/ARXIV.1005.2581]</ref>

In May 2010, AQUA@home deployed the first [[wikipedia:OpenCL|OpenCL]] application to run under BOINC, allowing its computations to be accelerated across a mix of multicore CPUs and GPUs from different vendors using a single, portable codebase.<ref>{{Cite web |url=https://boinc.berkeley.edu/old_news.php |title=News archive |website=BOINC |access-date=2026-06-27}}</ref> Project researchers subsequently published a direct performance comparison between [[wikipedia:CUDA|CUDA]] and OpenCL implementations of the same Quantum Monte Carlo computation kernel.<ref>{{Cite journal |last1=Karimi |first1=Kamran |last2=Dickson |first2=Neil |last3=Hamze |first3=Firas |title=A Performance Comparison of CUDA and OpenCL |date=2011-05-16 |url=https://arxiv.org/abs/1005.2581}} DOI: [https://doi.org/10.48550/arXiv.1005.2581 10.48550/ARXIV.1005.2581]</ref>

== Outcome ==

@@ Line 49: / Line 49: @@
 D-Wave Systems built AQUA@home to support the development of its experimental superconducting [[wikipedia:adiabatic quantum computation|adiabatic quantum optimization]] processors. Before committing hardware resources to a given class of problem, D-Wave researchers wanted to estimate in advance how a quantum annealing processor of a given size and connectivity could be expected to perform.<ref>{{Cite web |url=https://www.nextbigfuture.com/2008/12/dwave-quantum-computer-performance.html |title=Dwave Quantum Computer Performance Estimates and Calculations |website=NextBigFuture.com |date=2008-12-07 |access-date=2026-06-27}}</ref> Simulating the relevant quantum-mechanical spin systems classically using [[wikipedia:Quantum Monte Carlo|Quantum Monte Carlo]] (QMC) methods is computationally demanding, which made the problem well suited to distributed volunteer computing.<ref>{{Cite web |url=https://www.nextbigfuture.com/2008/12/dwave-quantum-computer-performance.html |title=Dwave Quantum Computer Performance Estimates and Calculations |website=NextBigFuture.com |date=2008-12-07 |access-date=2026-06-27}}</ref>
-The earliest computations run on AQUA@home calculated expected runtimes for [[wikipedia:spin glass|spin glass]] problems, a class of [[wikipedia:NP-hardness|NP-hard]] [[combinatorial optimization]] problem well suited to the project's processor architecture; this work formed the basis of the project's first publications.<ref>{{Cite web |url=https://www.nextbigfuture.com/2008/12/dwave-quantum-computer-performance.html |title=Dwave Quantum Computer Performance Estimates and Calculations |website=NextBigFuture.com |date=2008-12-07 |access-date=2026-06-27}}</ref> A later phase of the project used AQUA@home to estimate the runtime of the quantum adiabatic algorithm on binary classification problems for [[machine learning]], developed in part with researchers from [[wikipedia:Google|Google]].<ref>{{Cite web |url=https://www.nextbigfuture.com/2008/12/dwave-quantum-computer-performance.html |title=Dwave Quantum Computer Performance Estimates and Calculations |website=NextBigFuture.com |date=2008-12-07 |access-date=2026-06-27}}</ref>
+The earliest computations run on AQUA@home calculated expected runtimes for [[wikipedia:spin glass|spin glass]] problems, a class of [[wikipedia:NP-hardness|NP-hard]] [[wikipedia:combinatorial optimization|combinatorial optimization]] problem well suited to the project's processor architecture; this work formed the basis of the project's first publications.<ref>{{Cite web |url=https://www.nextbigfuture.com/2008/12/dwave-quantum-computer-performance.html |title=Dwave Quantum Computer Performance Estimates and Calculations |website=NextBigFuture.com |date=2008-12-07 |access-date=2026-06-27}}</ref> A later phase of the project used AQUA@home to estimate the runtime of the quantum adiabatic algorithm on binary classification problems for [[wikipedia:machine learning|machine learning]], developed in part with researchers from [[wikipedia:Google|Google]].<ref>{{Cite web |url=https://www.nextbigfuture.com/2008/12/dwave-quantum-computer-performance.html |title=Dwave Quantum Computer Performance Estimates and Calculations |website=NextBigFuture.com |date=2008-12-07 |access-date=2026-06-27}}</ref>
 == Software and technical milestones ==
@@ Line 57: / Line 57: @@
 {{#ev:youtube|VyQgGkj_Rss|700|center|AQUA@home screensaver in action}}
-In May 2010, AQUA@home deployed the first [[OpenCL]] application to run under BOINC, allowing its computations to be accelerated across a mix of multicore CPUs and GPUs from different vendors using a single, portable codebase.<ref>{{Cite web |url=https://boinc.berkeley.edu/old_news.php |title=News archive |website=BOINC |access-date=2026-06-27}}</ref> Project researchers subsequently published a direct performance comparison between [[CUDA]] and OpenCL implementations of the same Quantum Monte Carlo computation kernel.<ref>{{Cite journal |last1=Karimi |first1=Kamran |last2=Dickson |first2=Neil |last3=Hamze |first3=Firas |title=A Performance Comparison of CUDA and OpenCL |date=2011-05-16 |url=https://arxiv.org/abs/1005.2581}} DOI: [https://doi.org/10.48550/arXiv.1005.2581 10.48550/ARXIV.1005.2581]</ref>
+In May 2010, AQUA@home deployed the first [[wikipedia:OpenCL|OpenCL]] application to run under BOINC, allowing its computations to be accelerated across a mix of multicore CPUs and GPUs from different vendors using a single, portable codebase.<ref>{{Cite web |url=https://boinc.berkeley.edu/old_news.php |title=News archive |website=BOINC |access-date=2026-06-27}}</ref> Project researchers subsequently published a direct performance comparison between [[wikipedia:CUDA|CUDA]] and OpenCL implementations of the same Quantum Monte Carlo computation kernel.<ref>{{Cite journal |last1=Karimi |first1=Kamran |last2=Dickson |first2=Neil |last3=Hamze |first3=Firas |title=A Performance Comparison of CUDA and OpenCL |date=2011-05-16 |url=https://arxiv.org/abs/1005.2581}} DOI: [https://doi.org/10.48550/arXiv.1005.2581 10.48550/ARXIV.1005.2581]</ref>
 == Outcome ==