metadata
license: apache-2.0
pipeline_tag: tabular-regression
datasets:
- Allanatrix/QST
tags:
- Physics
NexaQST: Quantum State Tomography with Physics-Informed Neural Networks
NexaQST is a Quantum State Tomography model built using a Physics-Informed Neural Network (PINN) trained on synthetic 2-qubit experiments. This model leverages quantum mechanical priors such as positivity, Hermiticity, and trace constraints to ensure physically plausible reconstructions of quantum states.
Model Overview
- Task: Reconstruct quantum states (density matrices) from tomographic measurement traces
- System: Simulated 2-qubit experiments
- Architecture: Physics-Informed Neural Network (PINN)
- Constraints Embedded:
- Positivity
- Hermiticity
- Trace normalization (Tr(Ο) = 1)
Dataset Generation
- Simulation Toolkit:
QuTiP
- Process:
- Generated full 2-qubit tomography experiments via simulation
- Extracted and structured measurement traces
- Derived backward synthetic traces from known density matrices
- Created supervised training pairs:
(trace β density matrix)
- Enforced physical constraints directly inside the model during training
Model Input/Output
- Input: Vector of tomographic measurement traces
- Output: Reconstructed density matrix (complex-valued 4x4 for 2 qubits)
Example Usage
# Pseudocode for usage
from model import NexaQSTModel
import torch
model = NexaQSTModel()
model.load_state_dict(torch.load("nexaqst_model.pt"))
model.eval()
traces = load_measurement_vector("qst_trace.npy") # shape: (N,)
rho_pred = model.predict_density_matrix(traces)
# rho_pred is a 4x4 complex-valued matrix satisfying physical constraints
Applications
- Quantum error correction diagnostics
- Quantum system identification
- Educational simulation of QST techniques
- Physically consistent state estimation for quantum simulations
Licensing & Citation
- If used in publications or products, please credit the Nexa Scientific Suite.
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Created by Allan, independent quantum systems architect and ML researcher. Part of the Nexa scientific computing ecosystem.