Towards large-scale quantum optimization solvers with few qubits
- Technology Innovation Institute, Abu Dhabi (United Arab Emirates); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Technology Innovation Institute, Abu Dhabi (United Arab Emirates); Federal Univ. of Rio de Janeiro (Brazil)
- NVIDIA Corporation, Santa Clara, CA (United States)
- Technology Innovation Institute, Abu Dhabi (United Arab Emirates)
- California Institute of Technology (CalTech), Pasadena, CA (United States)
Quantum computers hold the promise of more efficient combinatorial optimization solvers, which could be game-changing for a broad range of applications. However, a bottleneck for materializing such advantages is that, in order to challenge classical algorithms in practice, mainstream approaches require a number of qubits prohibitively large for near-term hardware. Here we introduce a variational solver for MaxCut problems over $$m={{\mathcal{O}}}({n}^{k})$$ binary variables using only n qubits, with tunable k > 1. The number of parameters and circuit depth display mild linear and sublinear scalings in m, respectively. Moreover, we analytically prove that the specific qubit-efficient encoding brings in a super-polynomial mitigation of barren plateaus as a built-in feature. Altogether, this leads to high quantum-solver performances. For instance, for m = 7000, numerical simulations produce solutions competitive in quality with state-of-the-art classical solvers. In turn, for m = 2000, experiments with n = 17 trapped-ion qubits feature MaxCut approximation ratios estimated to be beyond the hardness threshold 0.941. Our findings offer an interesting heuristics for quantum-inspired solvers as well as a promising route towards solving commercially-relevant problems on near-term quantum devices.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE Laboratory Directed Research and Development (LDRD) Program
- Grant/Contract Number:
- 89233218CNA000001
- OSTI ID:
- 2570767
- Report Number(s):
- LA-UR--24-20389; 2041-1723; 10.1038/s41467-024-55346-z
- Journal Information:
- Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 16; ISSN 2041-1723
- Publisher:
- Nature Publishing GroupCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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