Speaker
Description
Pauli Correlation Encoding (PCE) is a qubit-efficient variational approach to combinatorial optimizations. It offers a polynomial reduction in qubit count and a super-polynomial suppression of barren plateaus. We extend the PCE-based framework to the Low Autocorrelation Binary Sequences (LABS) problem, a notoriously hard benchmarking problem for classical and quantum solvers. We simulate the solver for LABS instances of up to $N=45$ variables using only $4$ qubits and a circuit Ansatz with $30$ two-qubit gates.
We find that encodings based on anti-commuting Paulis strings attain higher solution-qualities than those with commuting strings, hinting at a possible connection between quantum correlations and solver performance.Moreover, our method displays a better scaling in time to solution than quantum schemes such as QAOA and even the best classical heuristic, Tabu search, while using only a fraction of the quantum resources required by other quantum approaches. Finally, we perform loss-function evaluations on \textrm{IonQ}'s \texttt{Forte} experimental quantum processor for $N=120$ using only $10$ qubits, showcasing good Noise reliance even without error mitigation.
Our findings highlight the potential of PCE as a promising tool to reduce the resource requirements for quantum optimization heuristics.
| Topic | Hybrid quantum-classical approaches for learning and optimization |
|---|---|
| Additional Topic | Quantum variational circuits and their applications |