In　this　paper,　we　develop　an　efficient　wavelet-based　regularized　linear　quantile　regression　framework　for　coefficient　estimations,　where　the　responses　are　scalars　and　the　predictors　include　both　scalars　and　function.　The　framework　consists　of　two　important　parts:　wavelet　transformation　and　regularized　linear　quantile　regression.　Wavelet　transform　can　be　used　to　approximate　functional　data　through　representing　it　by　finite　wavelet　coefficients　and　effectively　capturing　its　local　features.　Quantile　regression　is　robust　for　response　outliers　and　heavy-tailed　errors.　In　addition,　comparing　with　other　methods　it　provides　a　more　complete　picture　of　how　responses　change　conditional　on　covariates.　Meanwhile,　regularization　can　remove　small　wavelet　coefficients　to　achieve　sparsity　and　efficiency.　A　novel　algorithm,　Alternating　Direction　Method　of　Multipliers　(ADMM)　is　derived　to　solve　the　optimization　problems.　We　conduct　numerical　studies　to　investigate　the　finite　sample　performance　of　our　method　and　applied　it　on　real　data　from　ADHD　studies.