EigenOrbit's eigenkit library compares 12 quantum computing backends

[LatentSpace]

Choosing where to run a quantum program is getting complicated, and a new open source tool aims to help. The Quantum Insider reports on a paper from EigenOrbit introducing eigenkit, a provider neutral Python library for quantum computing

Eigenkit separates an algorithm from the backend that runs it, so the same code can be tested on different simulators and hardware. The team tested it across 12 providers, including eight simulators and four hardware routes such as IBM Heron and IQM Emerald

The library integrates with NumPy, PyTorch and scikit-learn. It includes tools for gradients, quantum machine learning, optimisation and chemistry, plus classical baselines for comparison

The study found that no single backend is consistently best across different workloads. On real hardware, results depend heavily on how well a circuit's layout matches the chip's connectivity. The team said the goal is not to hide differences between platforms, but to make them easier to evaluate. The article is based on a press release, names no researchers and gives no benchmark numbers

Provider neutral tools are useful because they reduce lock in and make fair comparisons easier. The finding that no backend wins everywhere matches what many researchers already believe. Including classical baselines is a particularly good touch

Do quantum developers need provider neutral tools? And which quantum cloud platform have you found easiest to use?

EigenOrbit Study Compares 12 Quantum Computing Backendsthequantuminsider.com