diff --git a/qdp/qdp-python/README.md b/qdp/qdp-python/README.md
index b4fd5230b..96828fc34 100644
--- a/qdp/qdp-python/README.md
+++ b/qdp/qdp-python/README.md
@@ -15,11 +15,11 @@ engine = QdpEngine(0)
 
 # Encode data from Python list
 data = [0.5, 0.5, 0.5, 0.5]
-dlpack_ptr = engine.encode(data, num_qubits=2, encoding_method="amplitude")
+qtensor = engine.encode(data, num_qubits=2, encoding_method="amplitude")
 
 # Or encode from file formats
-tensor_parquet = engine.encode_from_parquet("data.parquet", 10, "amplitude")
-tensor_arrow = engine.encode_from_arrow_ipc("data.arrow", 10, "amplitude")
+tensor_parquet = engine.encode("data.parquet", 10, "amplitude")
+tensor_arrow = engine.encode("data.arrow", 10, "amplitude")
 ```
 
 ## Build from source
@@ -41,8 +41,9 @@ uv run maturin develop
 
 ## File format support
 
-- **Parquet** - `encode_from_parquet(path, num_qubits, encoding_method)`
-- **Arrow IPC** - `encode_from_arrow_ipc(path, num_qubits, encoding_method)`
+- **Parquet** - `.parquet` files
+- **Arrow IPC** - `.arrow` or `.feather` files
+- **NumPy** - `.npy` files
 
 ## Adding new bindings
 
diff --git a/qdp/qdp-python/benchmark/benchmark_e2e.py b/qdp/qdp-python/benchmark/benchmark_e2e.py
index c4a632020..b0e19be8f 100644
--- a/qdp/qdp-python/benchmark/benchmark_e2e.py
+++ b/qdp/qdp-python/benchmark/benchmark_e2e.py
@@ -267,23 +267,23 @@ def run_mahout_parquet(engine, n_qubits, n_samples):
 
     # Direct Parquet to GPU pipeline
     parquet_encode_start = time.perf_counter()
-    batched_tensor = engine.encode_from_parquet(DATA_FILE, n_qubits, "amplitude")
+    qtensor = engine.encode(DATA_FILE, n_qubits)
     parquet_encode_time = time.perf_counter() - parquet_encode_start
     print(f"  Parquet->GPU (IO+Encode): {parquet_encode_time:.4f} s")
 
-    # Convert to torch tensor (single DLPack call)
+    # Convert to torch tensor
     dlpack_start = time.perf_counter()
-    gpu_batched = torch.from_dlpack(batched_tensor)
+    gpu_batched = torch.from_dlpack(qtensor)
     dlpack_time = time.perf_counter() - dlpack_start
     print(f"  DLPack conversion: {dlpack_time:.4f} s")
 
-    # Tensor is already 2D [n_samples, state_len] from to_dlpack()
+    # Tensor is already 2D [n_samples, state_len]
     state_len = 1 << n_qubits
     assert gpu_batched.shape == (n_samples, state_len), (
         f"Expected shape ({n_samples}, {state_len}), got {gpu_batched.shape}"
     )
 
-    # Convert to float for model (batch already on GPU)
+    # Convert to float for model
     reshape_start = time.perf_counter()
     gpu_all_data = gpu_batched.abs().to(torch.float32)
     reshape_time = time.perf_counter() - reshape_start
@@ -318,16 +318,16 @@ def run_mahout_arrow(engine, n_qubits, n_samples):
     start_time = time.perf_counter()
 
     arrow_encode_start = time.perf_counter()
-    batched_tensor = engine.encode_from_arrow_ipc(ARROW_FILE, n_qubits, "amplitude")
+    qtensor = engine.encode(ARROW_FILE, n_qubits)
     arrow_encode_time = time.perf_counter() - arrow_encode_start
     print(f"  Arrow->GPU (IO+Encode): {arrow_encode_time:.4f} s")
 
     dlpack_start = time.perf_counter()
-    gpu_batched = torch.from_dlpack(batched_tensor)
+    gpu_batched = torch.from_dlpack(qtensor)
     dlpack_time = time.perf_counter() - dlpack_start
     print(f"  DLPack conversion: {dlpack_time:.4f} s")
 
-    # Tensor is already 2D [n_samples, state_len] from to_dlpack()
+    # Tensor is already 2D [n_samples, state_len]
     state_len = 1 << n_qubits
     assert gpu_batched.shape == (n_samples, state_len), (
         f"Expected shape ({n_samples}, {state_len}), got {gpu_batched.shape}"
diff --git a/qdp/qdp-python/benchmark/benchmark_latency.py b/qdp/qdp-python/benchmark/benchmark_latency.py
index de26114db..0b89d8fd9 100644
--- a/qdp/qdp-python/benchmark/benchmark_latency.py
+++ b/qdp/qdp-python/benchmark/benchmark_latency.py
@@ -136,7 +136,7 @@ def run_mahout(num_qubits: int, total_batches: int, batch_size: int, prefetch: i
 
     for batch in prefetched_batches(total_batches, batch_size, vector_len, prefetch):
         normalized = normalize_batch(batch)
-        qtensor = engine.encode_batch(normalized, num_qubits, "amplitude")
+        qtensor = engine.encode(normalized, num_qubits, "amplitude")
         _ = torch.utils.dlpack.from_dlpack(qtensor)
         processed += normalized.shape[0]
 
diff --git a/qdp/qdp-python/benchmark/benchmark_numpy_io.py b/qdp/qdp-python/benchmark/benchmark_numpy_io.py
index 412e0ab72..2a3f3774a 100644
--- a/qdp/qdp-python/benchmark/benchmark_numpy_io.py
+++ b/qdp/qdp-python/benchmark/benchmark_numpy_io.py
@@ -79,8 +79,8 @@ def run_mahout_numpy(num_qubits: int, num_samples: int, npy_path: str):
     start_total = time.perf_counter()
 
     try:
-        # Use the NumPy reader API
-        qtensor = engine.encode_from_numpy(npy_path, num_qubits, "amplitude")
+        # Use the unified encode API with file path
+        qtensor = engine.encode(npy_path, num_qubits, "amplitude")
         tensor = torch.utils.dlpack.from_dlpack(qtensor)
 
         # Small computation to ensure GPU has processed the data
diff --git a/qdp/qdp-python/benchmark/benchmark_throughput.py b/qdp/qdp-python/benchmark/benchmark_throughput.py
index b87c9e11b..54b04fd1f 100644
--- a/qdp/qdp-python/benchmark/benchmark_throughput.py
+++ b/qdp/qdp-python/benchmark/benchmark_throughput.py
@@ -127,8 +127,8 @@ def run_mahout(num_qubits: int, total_batches: int, batch_size: int, prefetch: i
         total_batches, batch_size, 1 << num_qubits, prefetch
     ):
         normalized = np.ascontiguousarray(normalize_batch(batch), dtype=np.float64)
-        qtensor = engine.encode_batch(normalized, num_qubits, "amplitude")
-        tensor = torch.utils.dlpack.from_dlpack(qtensor).abs().to(torch.float32)
+        qtensor = engine.encode(normalized, num_qubits)
+        tensor = torch.from_dlpack(qtensor).abs().to(torch.float32)
         _ = tensor.sum()
         processed += normalized.shape[0]
 
diff --git a/qdp/qdp-python/src/lib.rs b/qdp/qdp-python/src/lib.rs
index cf075096c..ba9bbd3ac 100644
--- a/qdp/qdp-python/src/lib.rs
+++ b/qdp/qdp-python/src/lib.rs
@@ -14,7 +14,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-use numpy::{PyReadonlyArray2, PyUntypedArrayMethods};
+use numpy::{PyReadonlyArray1, PyReadonlyArray2, PyUntypedArrayMethods};
 use pyo3::exceptions::PyRuntimeError;
 use pyo3::ffi;
 use pyo3::prelude::*;
@@ -211,211 +211,180 @@ impl QdpEngine {
         Ok(Self { engine })
     }
 
-    /// Encode classical data into quantum state
+    /// Encode classical data into quantum state (auto-detects input type)
     ///
     /// Args:
-    ///     data: Input data as list of floats
+    ///     data: Input data - supports:
+    ///         - Python list: [1.0, 2.0, 3.0, 4.0]
+    ///         - NumPy array: 1D (single sample) or 2D (batch) array
+    ///         - PyTorch tensor: CPU tensor (will be copied to GPU)
+    ///         - String path: .parquet, .arrow, .npy file
+    ///         - pathlib.Path: Path object (converted via os.fspath())
     ///     num_qubits: Number of qubits for encoding
-    ///     encoding_method: Encoding strategy ("amplitude", "angle", or "basis")
+    ///     encoding_method: Encoding strategy ("amplitude" default, "angle", or "basis")
     ///
     /// Returns:
     ///     QuantumTensor: DLPack-compatible tensor for zero-copy PyTorch integration
-    ///         Shape: [1, 2^num_qubits]
-    ///
-    /// Raises:
-    ///     RuntimeError: If encoding fails
+    ///         Shape: [batch_size, 2^num_qubits]
     ///
     /// Example:
-    ///     >>> engine = QdpEngine(device_id=0)
-    ///     >>> qtensor = engine.encode([1.0, 2.0, 3.0, 4.0], num_qubits=2, encoding_method="amplitude")
-    ///     >>> torch_tensor = torch.from_dlpack(qtensor)
-    ///
-    /// TODO: Use numpy array input (`PyReadonlyArray1<f64>`) for zero-copy instead of `Vec<f64>`.
+    ///     >>> engine = QdpEngine(0)
+    ///     >>> # From list
+    ///     >>> tensor = engine.encode([1.0, 2.0, 3.0, 4.0], 2)
+    ///     >>> # From NumPy batch
+    ///     >>> tensor = engine.encode(np.random.randn(64, 4), 2)
+    ///     >>> # From file path string
+    ///     >>> tensor = engine.encode("data.parquet", 10)
+    ///     >>> # From pathlib.Path
+    ///     >>> from pathlib import Path
+    ///     >>> tensor = engine.encode(Path("data.npy"), 10)
+    #[pyo3(signature = (data, num_qubits, encoding_method="amplitude"))]
     fn encode(
         &self,
-        data: Vec<f64>,
-        num_qubits: usize,
-        encoding_method: &str,
-    ) -> PyResult<QuantumTensor> {
-        let ptr = self
-            .engine
-            .encode(&data, num_qubits, encoding_method)
-            .map_err(|e| PyRuntimeError::new_err(format!("Encoding failed: {}", e)))?;
-        Ok(QuantumTensor {
-            ptr,
-            consumed: false,
-        })
-    }
-
-    /// Encode a batch of samples from NumPy array (zero-copy, most efficient)
-    ///
-    /// Args:
-    ///     batch_data: 2D NumPy array of shape [num_samples, sample_size] with dtype float64
-    ///     num_qubits: Number of qubits for encoding
-    ///     encoding_method: Encoding strategy ("amplitude", "angle", or "basis")
-    ///
-    /// Returns:
-    ///     QuantumTensor: DLPack tensor containing all encoded states
-    ///         Shape: [num_samples, 2^num_qubits]
-    ///
-    /// Example:
-    ///     >>> engine = QdpEngine(device_id=0)
-    ///     >>> batch = np.random.randn(64, 4).astype(np.float64)
-    ///     >>> qtensor = engine.encode_batch(batch, 2, "amplitude")
-    ///     >>> torch_tensor = torch.from_dlpack(qtensor)  # Shape: [64, 4]
-    fn encode_batch(
-        &self,
-        batch_data: PyReadonlyArray2<f64>,
+        data: &Bound<'_, PyAny>,
         num_qubits: usize,
         encoding_method: &str,
     ) -> PyResult<QuantumTensor> {
-        let shape = batch_data.shape();
-        let num_samples = shape[0];
-        let sample_size = shape[1];
-
-        // Get contiguous slice from numpy array (zero-copy if already contiguous)
-        let data_slice = batch_data
-            .as_slice()
-            .map_err(|_| PyRuntimeError::new_err("NumPy array must be contiguous (C-order)"))?;
+        // Check if it's a string path
+        if let Ok(path) = data.extract::<String>() {
+            return self.encode_from_file(&path, num_qubits, encoding_method);
+        }
 
-        let ptr = self
-            .engine
-            .encode_batch(
-                data_slice,
-                num_samples,
-                sample_size,
-                num_qubits,
-                encoding_method,
-            )
-            .map_err(|e| PyRuntimeError::new_err(format!("Batch encoding failed: {}", e)))?;
-        Ok(QuantumTensor {
-            ptr,
-            consumed: false,
-        })
-    }
+        // Check if it's a pathlib.Path or os.PathLike object (has __fspath__ method)
+        if data.hasattr("__fspath__")? {
+            let path: String = data.call_method0("__fspath__")?.extract()?;
+            return self.encode_from_file(&path, num_qubits, encoding_method);
+        }
 
-    /// Encode from PyTorch Tensor
-    ///
-    /// Args:
-    ///     tensor: PyTorch Tensor (must be on CPU)
-    ///     num_qubits: Number of qubits for encoding
-    ///     encoding_method: Encoding strategy
-    ///
-    /// Returns:
-    ///     QuantumTensor: DLPack-compatible tensor
-    fn encode_tensor(
-        &self,
-        tensor: &Bound<'_, PyAny>,
-        num_qubits: usize,
-        encoding_method: &str,
-    ) -> PyResult<QuantumTensor> {
-        validate_tensor(tensor)?;
+        // Check if it's a NumPy array
+        if data.hasattr("__array_interface__")? {
+            // Get the array's ndim for shape validation
+            let ndim: usize = data.getattr("ndim")?.extract()?;
+
+            match ndim {
+                1 => {
+                    // 1D array: single sample encoding
+                    let array_1d = data.extract::<PyReadonlyArray1<f64>>().map_err(|_| {
+                        PyRuntimeError::new_err(
+                            "Failed to extract 1D NumPy array. Ensure dtype is float64.",
+                        )
+                    })?;
+                    let data_slice = array_1d.as_slice().map_err(|_| {
+                        PyRuntimeError::new_err("NumPy array must be contiguous (C-order)")
+                    })?;
+                    let ptr = self
+                        .engine
+                        .encode(data_slice, num_qubits, encoding_method)
+                        .map_err(|e| PyRuntimeError::new_err(format!("Encoding failed: {}", e)))?;
+                    return Ok(QuantumTensor {
+                        ptr,
+                        consumed: false,
+                    });
+                }
+                2 => {
+                    // 2D array: batch encoding
+                    let array_2d = data.extract::<PyReadonlyArray2<f64>>().map_err(|_| {
+                        PyRuntimeError::new_err(
+                            "Failed to extract 2D NumPy array. Ensure dtype is float64.",
+                        )
+                    })?;
+                    let shape = array_2d.shape();
+                    let num_samples = shape[0];
+                    let sample_size = shape[1];
+                    let data_slice = array_2d.as_slice().map_err(|_| {
+                        PyRuntimeError::new_err("NumPy array must be contiguous (C-order)")
+                    })?;
+                    let ptr = self
+                        .engine
+                        .encode_batch(
+                            data_slice,
+                            num_samples,
+                            sample_size,
+                            num_qubits,
+                            encoding_method,
+                        )
+                        .map_err(|e| PyRuntimeError::new_err(format!("Encoding failed: {}", e)))?;
+                    return Ok(QuantumTensor {
+                        ptr,
+                        consumed: false,
+                    });
+                }
+                _ => {
+                    return Err(PyRuntimeError::new_err(format!(
+                        "Unsupported array shape: {}D. Expected 1D array for single sample \
+                         encoding or 2D array (batch_size, features) for batch encoding.",
+                        ndim
+                    )));
+                }
+            }
+        }
 
-        // NOTE(perf): `tolist()` + `extract()` makes extra copies (Tensor -> Python list -> Vec).
-        // TODO: follow-up PR can use `numpy()`/buffer protocol (and possibly pinned host memory)
-        // to reduce copy overhead.
-        let data: Vec<f64> = tensor
-            .call_method0("flatten")?
-            .call_method0("tolist")?
-            .extract()?;
+        // Check if it's a PyTorch tensor
+        if is_pytorch_tensor(data)? {
+            validate_tensor(data)?;
+            let vec_data: Vec<f64> = data
+                .call_method0("flatten")?
+                .call_method0("tolist")?
+                .extract()?;
+            let ptr = self
+                .engine
+                .encode(&vec_data, num_qubits, encoding_method)
+                .map_err(|e| PyRuntimeError::new_err(format!("Encoding failed: {}", e)))?;
+            return Ok(QuantumTensor {
+                ptr,
+                consumed: false,
+            });
+        }
 
-        let ptr = self
-            .engine
-            .encode(&data, num_qubits, encoding_method)
-            .map_err(|e| PyRuntimeError::new_err(format!("Encoding failed: {}", e)))?;
+        // Fallback: try to extract as Vec<f64> (Python list)
+        if let Ok(vec_data) = data.extract::<Vec<f64>>() {
+            let ptr = self
+                .engine
+                .encode(&vec_data, num_qubits, encoding_method)
+                .map_err(|e| PyRuntimeError::new_err(format!("Encoding failed: {}", e)))?;
+            return Ok(QuantumTensor {
+                ptr,
+                consumed: false,
+            });
+        }
 
-        Ok(QuantumTensor {
-            ptr,
-            consumed: false,
-        })
+        Err(PyRuntimeError::new_err(
+            "Unsupported data type. Expected: list, NumPy array, PyTorch tensor, or file path",
+        ))
     }
 
-    /// Encode from Parquet file
-    ///
-    /// Args:
-    ///     path: Path to Parquet file
-    ///     num_qubits: Number of qubits for encoding
-    ///     encoding_method: Encoding strategy (currently only "amplitude")
-    ///
-    /// Returns:
-    ///     QuantumTensor: DLPack tensor containing all encoded states
-    ///
-    /// Example:
-    ///     >>> engine = QdpEngine(device_id=0)
-    ///     >>> batched = engine.encode_from_parquet("data.parquet", 16, "amplitude")
-    ///     >>> torch_tensor = torch.from_dlpack(batched)  # Shape: [200, 65536]
-    fn encode_from_parquet(
-        &self,
-        path: &str,
-        num_qubits: usize,
-        encoding_method: &str,
-    ) -> PyResult<QuantumTensor> {
-        let ptr = self
-            .engine
-            .encode_from_parquet(path, num_qubits, encoding_method)
-            .map_err(|e| PyRuntimeError::new_err(format!("Encoding from parquet failed: {}", e)))?;
-        Ok(QuantumTensor {
-            ptr,
-            consumed: false,
-        })
-    }
-
-    /// Encode from Arrow IPC file
-    ///
-    /// Args:
-    ///     path: Path to Arrow IPC file (.arrow or .feather)
-    ///     num_qubits: Number of qubits for encoding
-    ///     encoding_method: Encoding strategy (currently only "amplitude")
-    ///
-    /// Returns:
-    ///     QuantumTensor: DLPack tensor containing all encoded states
-    ///
-    /// Example:
-    ///     >>> engine = QdpEngine(device_id=0)
-    ///     >>> batched = engine.encode_from_arrow_ipc("data.arrow", 16, "amplitude")
-    ///     >>> torch_tensor = torch.from_dlpack(batched)
-    fn encode_from_arrow_ipc(
+    /// Internal helper to encode from file based on extension
+    fn encode_from_file(
         &self,
         path: &str,
         num_qubits: usize,
         encoding_method: &str,
     ) -> PyResult<QuantumTensor> {
-        let ptr = self
-            .engine
-            .encode_from_arrow_ipc(path, num_qubits, encoding_method)
-            .map_err(|e| {
-                PyRuntimeError::new_err(format!("Encoding from Arrow IPC failed: {}", e))
-            })?;
-        Ok(QuantumTensor {
-            ptr,
-            consumed: false,
-        })
-    }
+        let ptr = if path.ends_with(".parquet") {
+            self.engine
+                .encode_from_parquet(path, num_qubits, encoding_method)
+                .map_err(|e| {
+                    PyRuntimeError::new_err(format!("Encoding from parquet failed: {}", e))
+                })?
+        } else if path.ends_with(".arrow") || path.ends_with(".feather") {
+            self.engine
+                .encode_from_arrow_ipc(path, num_qubits, encoding_method)
+                .map_err(|e| {
+                    PyRuntimeError::new_err(format!("Encoding from Arrow IPC failed: {}", e))
+                })?
+        } else if path.ends_with(".npy") {
+            self.engine
+                .encode_from_numpy(path, num_qubits, encoding_method)
+                .map_err(|e| {
+                    PyRuntimeError::new_err(format!("Encoding from NumPy failed: {}", e))
+                })?
+        } else {
+            return Err(PyRuntimeError::new_err(format!(
+                "Unsupported file format. Expected .parquet, .arrow, .feather, or .npy, got: {}",
+                path
+            )));
+        };
 
-    /// Encode from NumPy .npy file
-    ///
-    /// Args:
-    ///     path: Path to NumPy .npy file
-    ///     num_qubits: Number of qubits for encoding
-    ///     encoding_method: Encoding strategy ("amplitude", "angle", or "basis")
-    ///
-    /// Returns:
-    ///     QuantumTensor: DLPack tensor containing all encoded states
-    ///
-    /// Example:
-    ///     >>> engine = QdpEngine(device_id=0)
-    ///     >>> batched = engine.encode_from_numpy("states.npy", 10, "amplitude")
-    ///     >>> torch_tensor = torch.from_dlpack(batched)
-    fn encode_from_numpy(
-        &self,
-        path: &str,
-        num_qubits: usize,
-        encoding_method: &str,
-    ) -> PyResult<QuantumTensor> {
-        let ptr = self
-            .engine
-            .encode_from_numpy(path, num_qubits, encoding_method)
-            .map_err(|e| PyRuntimeError::new_err(format!("Encoding from NumPy failed: {}", e)))?;
         Ok(QuantumTensor {
             ptr,
             consumed: false,
diff --git a/qdp/qdp-python/tests/test_bindings.py b/qdp/qdp-python/tests/test_bindings.py
index 158462631..0efb3576e 100644
--- a/qdp/qdp-python/tests/test_bindings.py
+++ b/qdp/qdp-python/tests/test_bindings.py
@@ -164,7 +164,7 @@ def test_encode_tensor_cpu():
 
     engine = QdpEngine(0)
     data = torch.tensor([1.0, 2.0, 3.0, 4.0], dtype=torch.float64)
-    qtensor = engine.encode_tensor(data, 2, "amplitude")
+    qtensor = engine.encode(data, 2, "amplitude")
 
     # Verify result
     torch_tensor = torch.from_dlpack(qtensor)
@@ -173,8 +173,8 @@ def test_encode_tensor_cpu():
 
 
 @pytest.mark.gpu
-def test_encode_tensor_errors():
-    """Test error handling for encode_tensor."""
+def test_encode_errors():
+    """Test error handling for unified encode method."""
     pytest.importorskip("torch")
     import torch
     from _qdp import QdpEngine
@@ -184,14 +184,15 @@ def test_encode_tensor_errors():
 
     engine = QdpEngine(0)
 
-    # Test non-tensor input
-    with pytest.raises(RuntimeError, match="Object is not a PyTorch Tensor"):
-        engine.encode_tensor([1.0, 2.0], 1, "amplitude")
-
-    # Test GPU tensor input (should fail as only CPU is supported for this path)
-    if torch.cuda.is_available():
-        gpu_tensor = torch.tensor([1.0, 2.0], device="cuda:0")
-        with pytest.raises(
-            RuntimeError, match="Only CPU tensors are currently supported"
-        ):
-            engine.encode_tensor(gpu_tensor, 1, "amplitude")
+    # Test unsupported file format
+    with pytest.raises(RuntimeError, match="Unsupported file format"):
+        engine.encode("data.txt", 2, "amplitude")
+
+    # Test unsupported data type
+    with pytest.raises(RuntimeError, match="Unsupported data type"):
+        engine.encode({"key": "value"}, 2, "amplitude")
+
+    # Test GPU tensor input (should fail as only CPU is supported)
+    gpu_tensor = torch.tensor([1.0, 2.0], device="cuda:0")
+    with pytest.raises(RuntimeError, match="Only CPU tensors are currently supported"):
+        engine.encode(gpu_tensor, 1, "amplitude")
diff --git a/qdp/qdp-python/tests/test_numpy.py b/qdp/qdp-python/tests/test_numpy.py
index caa0cb46b..1616247bc 100644
--- a/qdp/qdp-python/tests/test_numpy.py
+++ b/qdp/qdp-python/tests/test_numpy.py
@@ -46,10 +46,7 @@ def test_encode_from_numpy_basic():
     try:
         np.save(npy_path, data)
 
-        # Encode from NumPy file
-        qtensor = engine.encode_from_numpy(npy_path, num_qubits, "amplitude")
-
-        # Convert to PyTorch
+        qtensor = engine.encode(npy_path, num_qubits)
         tensor = torch.from_dlpack(qtensor)
 
         # Verify shape
@@ -93,8 +90,7 @@ def test_encode_from_numpy_large():
     try:
         np.save(npy_path, data)
 
-        # Encode
-        qtensor = engine.encode_from_numpy(npy_path, num_qubits, "amplitude")
+        qtensor = engine.encode(npy_path, num_qubits)
         tensor = torch.from_dlpack(qtensor)
 
         # Verify
@@ -125,7 +121,7 @@ def test_encode_from_numpy_single_sample():
     try:
         np.save(npy_path, data)
 
-        qtensor = engine.encode_from_numpy(npy_path, num_qubits, "amplitude")
+        qtensor = engine.encode(npy_path, num_qubits)
         tensor = torch.from_dlpack(qtensor)
 
         assert tensor.shape == (1, sample_size)
diff --git a/qdp/qdp-python/uv.lock b/qdp/qdp-python/uv.lock
index 506637ba7..36450e241 100644
--- a/qdp/qdp-python/uv.lock
+++ b/qdp/qdp-python/uv.lock
@@ -1,9 +1,19 @@
 version = 1
-revision = 2
-requires-python = ">=3.11"
+revision = 3
+requires-python = ">=3.10, <3.13"
 resolution-markers = [
     "python_full_version >= '3.12'",
-    "python_full_version < '3.12'",
+    "python_full_version == '3.11.*'",
+    "python_full_version < '3.11'",
+]
+
+[[package]]
+name = "absl-py"
+version = "2.3.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/10/2a/c93173ffa1b39c1d0395b7e842bbdc62e556ca9d8d3b5572926f3e4ca752/absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9", size = 116588, upload-time = "2025-07-03T09:31:44.05Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8f/aa/ba0014cc4659328dc818a28827be78e6d97312ab0cb98105a770924dc11e/absl_py-2.3.1-py3-none-any.whl", hash = "sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d", size = 135811, upload-time = "2025-07-03T09:31:42.253Z" },
 ]
 
 [[package]]
@@ -40,10 +50,26 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/84/ea/e16f0c423f7d83cf8b79cae9452040fb7b2e020c7439a167ee7c317de448/autograd-1.8.0-py3-none-any.whl", hash = "sha256:4ab9084294f814cf56c280adbe19612546a35574d67c574b04933c7d2ecb7d78", size = 51478, upload-time = "2025-05-05T12:49:00.585Z" },
 ]
 
+[[package]]
+name = "autoray"
+version = "0.7.2"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version < '3.11'",
+]
+sdist = { url = "https://files.pythonhosted.org/packages/24/d7/133dd5a2837e0416375b7f059f0cf3b26d22a330405ca0bd8bda43c00278/autoray-0.7.2.tar.gz", hash = "sha256:7f8f375af12deea65736b65305b86eff2e11ebaf8c58d3e599752681772ff0d7", size = 1210729, upload-time = "2025-07-09T00:26:37.235Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/dc/ca/00c61fcd5bb7ce1dfe3e9313febf2ba11fa17cf619676960f548a7c32586/autoray-0.7.2-py3-none-any.whl", hash = "sha256:f692f0f21a6f187ce877f3cc810f035d601ccd713beac02974811650bb60f580", size = 930824, upload-time = "2025-07-09T00:26:35.182Z" },
+]
+
 [[package]]
 name = "autoray"
 version = "0.8.0"
 source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version >= '3.12'",
+    "python_full_version == '3.11.*'",
+]
 sdist = { url = "https://files.pythonhosted.org/packages/5a/fe/3272078bb25736ace584cc30adc934f4de8bce6b6fa639836594558dc321/autoray-0.8.0.tar.gz", hash = "sha256:5d0d71da03cb02d5bc590a1af64e0ba58589352d628843a0ecbcfe90040dc520", size = 1215812, upload-time = "2025-08-20T18:10:26.127Z" }
 wheels = [
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@@ -73,6 +99,22 @@ version = "3.4.4"
 source = { registry = "https://pypi.org/simple" }
 sdist = { url = "https://files.pythonhosted.org/packages/13/69/33ddede1939fdd074bce5434295f38fae7136463422fe4fd3e0e89b98062/charset_normalizer-3.4.4.tar.gz", hash = "sha256:94537985111c35f28720e43603b8e7b43a6ecfb2ce1d3058bbe955b73404e21a", size = 129418, upload-time = "2025-10-14T04:42:32.879Z" }
 wheels = [
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