Hyperdimensional computing (HDC) is an emerging brain-inspired machine learning framework built upon unique properties of high-dimensional vectors. The vectors can contain floating-point (FP) or binary values, offering tradeoffs in terms of accuracy and computational cost. Previous works have proposed quantization methods to convert FP models into binary ones to improve performance. Unfortunately, these approaches not only incur an accuracy loss but also sacrifice valuable properties of HDC, such as low training time or robustness to noise. To overcome these limitations, we propose TQHD, a quantization method that transforms FP vectors into thermometer-encoded binary vectors. TQHD reduces the accuracy loss inflicted by quantization by 3.4 pp in complex scenarios compared to the state-of-the-art.