> ## Documentation Index
> Fetch the complete documentation index at: https://private-7c7dfe99-mintlify-8a08bda2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

> Documentation for the QBit data type in ClickHouse, which allows fine-grained quantization for approximate vector search

# QBit Data Type

The `QBit` data type reorganizes vector storage for faster approximate searches. Instead of storing each vector's elements together, it groups the same binary digit positions across all vectors.
This stores vectors at full precision while letting you choose the fine-grained quantization level at search time: read fewer bits for less I/O and faster calculations, or more bits for higher accuracy. You get the speed benefits of reduced data transfer and computation from quantization, but all the original data remains available when needed.

To declare a column of `QBit` type, use the following syntax:

```sql theme={null}
column_name QBit(element_type, dimension)
```

* `element_type` – the type of each vector element. The allowed types are `BFloat16`, `Float32` and `Float64`
* `dimension` – the number of elements in each vector

<h2 id="creating-qbit">
  Creating QBit
</h2>

Using the `QBit` type in table column definition:

```sql theme={null}
CREATE TABLE test (id UInt32, vec QBit(Float32, 8)) ENGINE = Memory;
INSERT INTO test VALUES (1, [1, 2, 3, 4, 5, 6, 7, 8]), (2, [9, 10, 11, 12, 13, 14, 15, 16]);
SELECT vec FROM test ORDER BY id;
```

```text theme={null}
┌─vec──────────────────────┐
│ [1,2,3,4,5,6,7,8]        │
│ [9,10,11,12,13,14,15,16] │
└──────────────────────────┘
```

<h2 id="qbit-subcolumns">
  QBit subcolumns
</h2>

`QBit` implements a subcolumn access pattern that allows you to access individual bit planes of the stored vectors. Each bit position can be accessed using the `.N` syntax, where `N` is the bit position:

```sql theme={null}
CREATE TABLE test (id UInt32, vec QBit(Float32, 8)) ENGINE = Memory;
INSERT INTO test VALUES (1, [0, 0, 0, 0, 0, 0, 0, 0]);
INSERT INTO test VALUES (1, [-0, -0, -0, -0, -0, -0, -0, -0]);
SELECT bin(vec.1) FROM test;
```

```text theme={null}
┌─bin(tupleElement(vec, 1))─┐
│ 00000000                  │
│ 11111111                  │
└───────────────────────────┘
```

The number of accessible subcolumns depends on the element type:

* `BFloat16`: 16 subcolumns (1-16)
* `Float32`: 32 subcolumns (1-32)
* `Float64`: 64 subcolumns (1-64)

<h2 id="vector-search-functions">
  Vector search functions
</h2>

These are the distance functions for vector similarity search that use `QBit` data type:

* [`L2DistanceTransposed`](/reference/functions/regular-functions/distance-functions#L2DistanceTransposed)
* [`cosineDistanceTransposed`](/reference/functions/regular-functions/distance-functions#cosineDistanceTransposed)