Optimal algorithmic complexity of inference in quantum kernel methods

Quantum kernel methods are among the leading candidates for achieving quantum advantage in supervised learning. A key bottleneck is the cost of inference: evaluating a trained model on new data requires estimating a weighted sum sumi=1N αi k(x,xi) of N kernel values to additive precision varepsilon, where α is the vector of trained coefficients. The standard approach estimates each term independently via sampling, yielding a query complexity of O(NlVertαrVert22/varepsilon2). In this work, we identify two independent axes for improvement: (1) How individual kernel values are estimated (sampling versus quantum amplitude estimation), and (2) how the sum is approximated (term-by-term versus via a single observable), and systematically analyze all combinations thereof. The query-optimal combination, encoding the full inference sum as the expectation value of a single observable and applying quantum amplitude estimation, achieves a query complexity of O(lVertαrVert1/varepsilon), removing the dependence on N from the query count and yielding a quadratic improvement in both lVertαrVert1 and varepsilon. We prove a matching lower bound of Omega(lVertαrVert1/varepsilon), establishing query-optimality of our approach up to logarithmic factors. Beyond query complexity, we also analyze how these improvements translate into gate costs and show that the query-optimal strategy is not always optimal in practice from the perspective of gate complexity. Our results provide both a query-optimal algorithm and a practically optimal choice of strategy depending on hardware capabilities, along with a complete landscape of intermediate methods to guide practitioners. All algorithms require only amplitude estimation as a subroutine and are thus natural candidates for early-fault-tolerant implementations.