Solving optimization problems on real quantum hardware

Solving optimization problems on real quantum hardware Ask Question Asked 1 year, 10 months ago Modified 6 days ago Viewed 426 times 0 I want to know how to solve a 01 Knapsack problem (or any optimization problem) on real Quantum Hardware only. I don't want to use an Application class or any classical simulation technique. If anyone has done it then please give me your code so that I can compare the solution time for classical and quantum solvers qiskitquantum-operationoptimizationqaoaresearch Share Improve this question Follow edited May 1, 2024 at 14:38 MonteNero 3,4228 8 silver badges 27 27 bronze badges asked May 1, 2024 at 11:53 Maths_hawk 634 4 bronze badges Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. –  Community Bot Commented May 1, 2024 at 15:44 Hi thanks for your response. I agree with you that DWave quantum annealers are good choice for optimization problem. And I run a KP problem on their Hybrid CQM solver.I found that though the solution quality is compromised but computational time is far better than Classical computers. So inorder to have better comparison I want to run KP problem on circuit based QC as well. I have some idea about QAOA but don't know how to implement them for my problem. If you know how to do that then please. –  Maths_hawk Commented May 2, 2024 at 17:22 Add a comment 2 Answers Sorted by: Highest score (default) Date modified (newest first) Date created (oldest first) 1 Current quantum hardware based on a quantum circuit model has a limited number of qubits, which are prone to errors and decoherence. So, comparing solutions from classical solvers with those produced by quantum hardware is almost pointless at the moment. Classical solvers that run on classical hardware will always outperform their quantum counterparts by a very large margin, at least in the near future. If you still want to play with the real quantum hardware, then IBM has several publicly available backends where you can try running circuit-base quantum algorithms like QAOA or VQE. The QAOA and VQE codes are on their website. Another venue that is perhaps slightly more mature for optimization is D-Wave's quantum annealer. It can handle relatively large problems with moderate success. For example, here is the implementation of the KP problem from D-Wave's repo. A basic Google search will also show you a number of GitHub repos that implement KP problems for annealers and circuit-based quantum computers. Overall, I suggest reading basic information about the current state of quantum computers and quantum optimization before running any code. It is best to understand the most basic theory behind quantum algorithms before looking at the code. Share Improve this answer Follow answered May 1, 2024 at 14:56 MonteNero 3,4228 8 silver badges 27 27 bronze badges Add a comment 0 There is a new class of Solver- Ising Machines that are available for solving large optimization problems of QUBO nature, 100K variable class. The Quanfluence Coherent Ising Machine is quantum inspired using optic components and FPGA, can give a highly reliable solution and 10-100X faster than classical computers. This machine can be access remotely over the AWS cloud. https://quanfluence.com/time-multiplexed-coherent-ising-machine/ Yossi Share Improve this answer Follow answered Mar 10 at 13:20 Yossi Yehiel 1 New contributor Add a comment Your Answer Sign up or log in Sign up using Google Sign up using Email and Password Post as a guest Name Email Required, but never shown Post Your Answer By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy. 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