In one reported H200 benchmark, Python-facing TensorCircuit-NG with JAX was faster than the tested Julia implementation for a 26-qubit VQE workload. That result compares specific software stacks and methods—not Python and Julia in general. The fastest TensorCircuit-NG variant also paid a much larger initial evaluation-related cost, so the practical winner depends partly on how many runs you need.
What did the H200 benchmark measure?
In an October 2026 article, Shixin Zhang compared TensorCircuit-NG using its JAX backend with Yao.jl using a CUDA state-vector implementation. The workload was a variational quantum eigensolver (VQE) for the one-dimensional transverse-field Ising model: 26 qubits, 16 circuit layers, 816 parameters, and double precision. Both implementations reportedly started from the same uniform superposition state. Each layer applied RZZ gates between neighboring qubits, followed by RX gates on every qubit. The article’s reported timing figures are:
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| Implementation | Initial evaluation-related time | Warmed-up energy plus gradient |
|---|---|---|
TensorCircuit-NG/JAX, scan |
11.7 seconds | 0.864 seconds |
TensorCircuit-NG/JAX, unrolled |
206.9 seconds | 0.396 seconds |
| Yao.jl/CUDA state vector | 29.0 seconds | 3.938 seconds |
These are author-reported results from Zhang’s October 2026 benchmark article. Its benchmark scripts and dependency versions are linked from that article, but they were not independently audited here. Treat the figures as a report of this particular setup, not as a controlled language-wide ranking.
Which implementation was faster?
For the warmed-up energy-and-gradient evaluation, TensorCircuit-NG/JAX scan took 0.864 seconds, compared with 3.938 seconds for the tested Yao.jl state-vector route. The JAX unrolled variant took 0.396 seconds, the shortest reported steady-state time. In this workload, that makes the unrolled variant about ten times as fast as the reported Yao.jl run for this evaluation, while the scan variant was about 4.6 times as fast. Those ratios are calculations from the article’s timings; they do not predict results on other circuits, hardware, or software versions.
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The initial-time column changes the picture for short runs. The unrolled variant’s 206.9-second initial evaluation-related time was far longer than its warmed-up evaluation. Zhang estimates that its speed advantage over the scan form offsets the extra initial cost after roughly 400 evaluations. That break-even point is the author’s estimate for the reported timing setup, not a universal threshold.
Energy-only results are a separate comparison
For forward-only energy evaluation, the article reports warmed-up median times of 0.095 seconds for TensorCircuit-NG and 0.192 seconds for Yao.jl. These figures followed warm-up and ten repeated calculations, and the energy results were identical. They measure a different task from calculating both energy and gradient, so they should not be substituted for the combined-workload timings.
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Why this does not prove Python is faster than Julia
The benchmark compares complete implementations: a Python interface using JAX transformations, XLA compilation, and an optimized tensor-contraction order, against a Julia implementation using a GPU state-vector representation and reverse-mode automatic differentiation. The measured performance reflects those choices—including algorithms, representation, compilation, and backend—not just the languages’ interpreter or runtime overhead.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsTensorCircuit-NG’s paper describes a framework built on JAX, TensorFlow, and PyTorch, with automatic differentiation and distributed simulation capabilities. Its project repository provides further framework context. These project descriptions do not independently validate the H200 timings.
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The benchmark article also describes a tested YaoToEinsum/OMEinsum/cuTENSOR tensor-network route that could perform GPU contraction, but whose reverse-mode differentiation failed through in-place mutation in the conversion path. That is a limitation of the tested route and interface; it is not evidence that Julia cannot perform the task. Separately, a study of Julia for high-energy-physics analyses found Julia implementations competitive for computationally dominated tasks. It is a different workload, which reinforces why language performance depends on the problem and implementation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to judge the result for your own workload
A meaningful comparison should hold the task and correctness requirements constant, then account for the cost of reaching steady state. For a quantum-circuit benchmark, check:
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- Workload: qubit count, circuit depth, gate sequence, parameter count, and initial state.
- Task completeness: energy-only execution is not equivalent to energy plus gradients.
- Hardware and precision: record the GPU model and numeric precision; this result concerns an NVIDIA H200 and double precision.
- Implementation path: identify the backend, compiler, data representation, automatic-differentiation method, and contraction strategy.
- Startup versus repeated runs: report compilation or other initial costs separately from warmed-up timings, and compare the total time for the number of evaluations you expect to run.
- Correctness: verify that both implementations produce matching results, not merely that one completes sooner.
Zhang reports that the resulting parameters agreed element by element and that the maximum difference between the complete gradients was at machine precision. Because the underlying scripts and dependency versions were not independently inspected, this should be read as the article’s reported correctness check.
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TensorCircuit-NG’s FAQ advises benchmarking backend choices and notes that contraction-path search and execution cost involve trade-offs. For practical work, benchmark the actual circuit and software versions you intend to use, including setup time and the full calculation you need.
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