IS my QUBO too big for QAOA using IBM Quantum computer?

IS my QUBO too big for QAOA using IBM Quantum computer? Ask Question Asked 2 years, 8 months ago Modified 6 days ago Viewed 226 times 1 On qiskit, I have defined a QUBO, called qp as follows: print(qp.prettyprint()) Minimize -1.7324559764718837*x10^2 + 0.00619189721450626*x10*x11 ... + 3.6972673229589947*x27*x29 - 32.42658793825545*x28^2 + 7.39453464591802*x28*x29 - 61.155908553552145*x29^2 Subject to No constraints Binary variables (20) x10 x11 x12 x13 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x24 x25 x26 x27 x28 x29 There are actually 104 lines in the minimization problem. Using a classical solver, I can easily solve this problem: cplex_optimizer = CplexOptimizer() qubo_minimization_result = cplex_optimizer.solve(qp) This gives me: print("minimum point (binary): ", qubo_minimization_result.x) minimum point (binary): [0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 1. 1. 1. 0. 1. 1. 1. 1. 0.] I want now to solve it using QAOA on a IBM quantum computer (QC). But before doing it on a real QC, I would like to solve it using a IBM simulator ibmq_qasm_simulator: # Select a backend. backend = provider.get_backend("ibmq_qasm_simulator") # QAOA parameters number_of_shots = 100000 qaoa_reps = 1 # Betas and Gammas as described in the original QAOA paper betas = [0] gammas = [0] algorithm_globals.random_seed = 10598 quantum_instance = QuantumInstance( backend, seed_simulator=algorithm_globals.random_seed, seed_transpiler=algorithm_globals.random_seed, shots=number_of_shots) qaoa_mes = QAOA(quantum_instance=quantum_instance, reps=qaoa_reps, initial_point=[*betas, *gammas]) qaoa = MinimumEigenOptimizer(qaoa_mes) # using QAOA qaoa_result = qaoa.solve(qp) When I run this qiskit code, I never obtain a result in the sense that the code is running but never stops. Do you think my QUBO is too big for solving it on a 32-qubits IBM simulator? Should I try directly on a real 127-qubits QC? qiskitibm-q-experienceqaoa Share Improve this question Follow asked Jun 26, 2023 at 8:57 Stéphane 1137 7 bronze badges Add a comment 2 Answers Sorted by: Highest score (default) Date modified (newest first) Date created (oldest first) 0 I think the circuit might be too large. Simulating wide and deep circuits can be extremely demanding. You could try using the AerSimulator with method="matrix_product_state" to improve performance — see the Qiskit documentation here. If you want to include realistic noise effects, you can also add a noise_model to emulate a real device — more details here. Share Improve this answer Follow answered Oct 7, 2025 at 8:47 kernel123 976 6 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. Quanfluence will provide their SDK and programs written in other environments can also be executed. https://quanfluence.com/time-multiplexed-coherent-ising-machine/ Yossi Share Improve this answer Follow answered Mar 10 at 13:25 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. Start asking to get answers Find the answer to your question by asking. Ask question Explore related questions qiskitibm-q-experienceqaoa See similar questions with these tags. 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