Speaker
Description
Digital quantum computers promise computational capabilities beyond classical systems, but scarce qubits and limited coherence make data loading a persistent bottleneck. Approximate quantum loaders (AQLs) address this bottleneck by trading preparation fidelity for circuit complexity. We develop AQER, an AQL method that constructs loading circuits through maximal entanglement reduction. We first cast tensor-network, variational, and non-variational loaders into a unified optimization framework and derive information-theoretic bounds showing that AQL infidelity is controlled by the single-qubit entanglement entropy of the inverse-evolved target state. Guided by this result, AQER appends two-qubit blocks to reduce entanglement, analytically fits a product-state approximation, and refines all parameters. Experiments on classical image and language data, random-circuit states, and transverse-field Ising ground states up to 50 qubits show consistently lower infidelity with equal or fewer two-qubit gates than representative baselines.
| Topic | Encoding and processing of data in quantum systems |
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