Add links to TurboQuant article (#2354)

* Add links to TQ article

* Add link to articles for other quantization methods

* Update TQ article preview images

qdrant-landing/content/blog/qdrant-1.18.x.md

@@ -66,7 +66,7 @@ The following table shows recall for 1-bit TurboQuant (TQ1) compared to uncompre
 
 Compared to 1-bit binary quantization, 1-bit TurboQuant offers better recall at equivalent storage budgets, albeit at a lower speed. Similar trends are observed for 1.5-bit and 2-bit configurations.
 
-We will soon publish an article with detailed numbers, including throughput and indexing times.
+For more details about Qdrant's TurboQuant implementation, refer to our article [TurboQuant in Qdrant](/articles/turboquant-quantization/).
 
 ### Get Started with TurboQuant
 

qdrant-landing/content/documentation/manage-data/quantization.md

@@ -54,7 +54,7 @@ Depending on your requirements for recall, compression, and distance metrics, co
 
 TurboQuant is [a quantization method developed by Google](https://research.google/blog/turboquant-redefining-ai-efficiency-with-extreme-compression/). It operates by applying a fast random rotation to vectors before compression, which evenly redistributes data across coordinates. This allows applying a single pre-computed, globally optimized quantization mapping across the dataset, enabling TurboQuant to work effectively with any vector distribution and overcoming a key limitation found in binary quantization.
 
-Qdrant's implementation of TurboQuant extends the original algorithm to close the gap between the algorithm's theoretical assumptions and real-world embeddings.
+[Qdrant's implementation of TurboQuant](/articles/turboquant-quantization/) extends the original algorithm to close the gap between the algorithm's theoretical assumptions and real-world embeddings.
 
 TurboQuant uses asymmetric quantization automatically: only stored vectors are compressed, while queries are scored in full precision. This improves accuracy and requires no additional configuration.
 
@@ -85,7 +85,7 @@ Manhattan (L1) distance is supported but requires full vector reconstruction per
 
 *Available as of v1.1.0*
 
-Scalar quantization, in the context of vector search engines, is a compression technique that compresses vectors by reducing the number of bits used to represent each vector component.
+[Scalar quantization](/articles/scalar-quantization/), in the context of vector search engines, is a compression technique that compresses vectors by reducing the number of bits used to represent each vector component.
 
 For instance, Qdrant uses 32-bit floating numbers to represent the original vector components. Scalar quantization allows you to reduce the number of bits used to 8.
 In other words, Qdrant performs `float32 -> uint8` conversion for each vector component.
@@ -106,7 +106,7 @@ Please refer to the [Quantization Tips](#quantization-tips) section for more inf
 
 *Available as of v1.5.0*
 
-Binary quantization is an extreme case of scalar quantization.
+[Binary quantization](/articles/binary-quantization/) is an extreme case of scalar quantization.
 This feature lets you represent each vector component as a single bit, effectively reducing the memory footprint by a factor of 32. This is the fastest quantization method, since it lets you perform a vector comparison with a few CPU instructions. Binary quantization can achieve up to a 40x speedup compared to the original vectors.
 
 However, binary quantization is only efficient for high-dimensional vectors and require a centered distribution of vector components.
@@ -191,7 +191,7 @@ See how to set up Asymmetric Quantization quantization in the [following section
 
 *Available as of v1.2.0*
 
-Product quantization is a method of compressing vectors to minimize their memory usage by dividing them into
+[Product quantization](/articles/product-quantization/) is a method of compressing vectors to minimize their memory usage by dividing them into
 chunks and quantizing each segment individually.
 Each chunk is approximated by a centroid index that represents the original vector component.
 The positions of the centroids are determined through the utilization of a clustering algorithm such as k-means.