Experimental error mitigation using linear rescaling for variational quantum eigensolving with up to 20 qubits
Quantum computers have the potential to help solve a range of physics and chemistry problems, but noise in quantum hardware currently limits our ability to obtain accurate results from the execution of quantum-simulation algorithms. Various methods have been proposed to mitigate the impact of noise on variational algorithms, including several that model the noise as damping expectation values of observables. In this work, we benchmark various methods, including a new method proposed here. Here, we compare their performance in estimating the ground-state energies of several instances of the 1D mixed-field Ising model using the variational-quantum-eigensolver algorithm with up to 20 qubits on two of IBM's quantum computers. We find that several error-mitigation techniques allow us to recover energies to within 10% of the true values for circuits containing up to about 25 ansatz layers, where each layer consists of CNOT gates between all neighboring qubits and Y-rotations on all qubits.
- Research Organization:
- Cornell Univ., Ithaca, NY (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- Grant/Contract Number:
- SC0020397
- OSTI ID:
- 1979465
- Journal Information:
- Quantum Science and Technology, Journal Name: Quantum Science and Technology Journal Issue: 1 Vol. 7; ISSN 2058-9565
- Publisher:
- IOP PublishingCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Similar Records
Quantum utility-scale error mitigation for quantum quench dynamics in Heisenberg spin chains
Mutual information-assisted adaptive variational quantum eigensolver