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Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Qubiter’s native TensorFlow backend was announced on May 14, 2019. The Qubiter author, Robert R. Tucci, introduced a class called SEO_simulator_tf alongside the project’s NumPy simulator, and described state-vector simulation on CPU, GPU or TPU plus back-propagation through quantum circuits. He also linked a notebook demonstrating variational quantum eigensolving (VQE). Those are claims and an example from the announcement—not a benchmark or a guarantee of compatibility with current TensorFlow releases. Qubiter repository · May 14, 2019 announcement
What is Qubiter?
Qubiter is a Python toolset for working with gate-model quantum circuits on classical computers. Its repository describes reading and writing circuit files, compiling circuits, expanding controlled gates, embedding circuits, and simulation. Circuits are stored as text, and the project includes instructional notebooks and generated Sphinx documentation. The README describes source installation by cloning the repository and also mentions an older pip package option; consult the repository for the applicable instructions rather than assuming that historical packaging details are current. Qubiter repository
Does Qubiter use TensorFlow?
Qubiter’s May 14, 2019 announcement added a TensorFlow-backed simulator named SEO_simulator_tf alongside the NumPy-based SEO_simulator. Tucci described the new backend as a native TensorFlow backend. The repository presents both NumPy and TensorFlow backends, but its retrieved README does not provide a current TensorFlow version-compatibility matrix. Tucci’s announcement · Qubiter repository
What does a TensorFlow backend change?
A simulator implemented with TensorFlow tensors can fit into tensor-based computation and differentiable workflows. Tucci specifically said the Qubiter backend allowed back-propagation on quantum circuits. The announcement does not describe the differentiation algorithm or compare results against the NumPy backend, so it establishes the author’s stated capability—not a measured speed or accuracy advantage. Tucci’s announcement
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Can Qubiter run on a GPU or TPU?
In the 2019 announcement, Tucci claimed that SEO_simulator_tf could evolve state vectors on CPU, GPU or TPU. The announcement does not give hardware requirements, a tested device matrix, or benchmark results. Qubiter’s README likewise says the simulator had not been benchmarked; therefore, no supported speedup or scaling figure is established by these sources. Tucci’s announcement · Qubiter repository
Can I use Qubiter for VQE?
Tucci linked a Jupyter notebook demonstrating VQE, or variational quantum eigensolving, which he described as mean Hamiltonian minimization. This makes the notebook evidence of an intended example workflow; it does not establish that the notebook runs unchanged with present-day dependencies. The announcement provides no measured VQE results. Tucci’s announcement
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How does Qubiter differ from TensorFlow Quantum?
TensorFlow Quantum (TFQ) is a separate project, not another name for Qubiter and not evidence of Qubiter’s API or compatibility. TFQ describes a Python framework for hybrid quantum-classical machine learning that brings together Cirq circuits, qsim simulation, and TensorFlow/Keras abstractions, with automatic differentiation. Its repository lists a tested Linux stack of Python 3.10–3.12, TensorFlow 2.19.1, TF-Keras 2.19.0, NumPy 2.0, and Cirq 1.5.0. Those versions apply to TFQ, not Qubiter. TensorFlow Quantum repository
TFQ’s tfq.layers.State API documents a default native TFQ state-vector simulator and allows an external Cirq execution object implementing cirq.SimulatesFinalState. The layer does not support C++ density-matrix simulation; its API points readers to Cirq’s DensityMatrixSimulator for that task. These documented interface details describe TFQ only and should not be attributed to Qubiter. TFQ State API
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsWhat is known about Qubiter’s current compatibility?
The May 2019 announcement establishes what its author said at that time, not whether the backend works with a particular TensorFlow release today. The retrieved Qubiter README provides installation pathways but no current TensorFlow compatibility matrix. A GitHub topic listing showed a repository update date of December 25, 2023; that date is a limited activity signal and does not establish that the code is unusable or that the TensorFlow backend has stopped working. Qubiter repository · GitHub quantum-compiler topic listing
TFQ’s installation guide covers that separate project’s browser tutorials, pip installation and source builds. Its instructions and dependencies do not explain how to install Qubiter’s historical TensorFlow backend. TFQ installation guide
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the benchmark and license caveats?
Qubiter’s README expressly says its simulator has not been benchmarked. It adds that it “should be pretty fast” because it relies on NumPy, but that expectation is not a test result and does not quantify the TensorFlow backend’s performance. The available material supplies no basis for a speedup, qubit-capacity, or scalability claim. Qubiter repository
The repository README describes different license terms for different parts of the project: BSD three-clause terms with an added patent-rights clause for material outside quantum_CSD_compiler, and GPLv2 for that folder. Check the repository’s license files and the relevant component before relying on a single license label for the whole project. Qubiter repository
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