Research
We Publish Our Results
Our claims aren't marketing — they're on arXiv, with the circuits, methods, and numbers to check them. Two papers show Google's “quantum supremacy” circuits running on hardware you can actually get.
arXiv:2512.07311 · December 2025
Revisiting Quantum Supremacy: Simulating Sycamore-Class Circuits Using Hybrid CPU/GPU HPC Workloads
Bob Wold, Venkateswaran Kasirajan
arXiv:2512.07311The paper presents a hybrid pipeline for the circuits Google used to claim quantum supremacy: a single NVIDIA A100 GPU constructs the quantum state — a process that takes about six minutes — and then Nparallel CPU jobs (each with 8 cores and 16 GB of RAM, orchestrated through SLURM with the Quantum Rings SDK) perform distributed measurement sampling.
On the 53-qubit, 14-cycle Sycamore circuit, the simulation reaches a linear cross-entropy benchmarking (XEB) score of 0.549 — against the 0.002published with Google's reference data for the same circuit.
For runtime, the harder 53-qubit, 20-cycle circuit was run end to end: the full 2.5-million-shot workload completed across 100 CPU jobs in 1h 15m 36s — a 6.95×10⁷ speedup over the 10,000 years Google originally estimated for classical simulation. Scaling the sampling stage to 1,000 jobs puts the estimated total at 17m 35s, about 12 minutes slower than the original quantum hardware run itself.
“These results illustrate that ‘quantum supremacy’ is not fixed and continues to be a moving target.”
274× the Published Fidelity
Linear cross-entropy benchmarking (XEB) on the 53-qubit, 14-cycle Sycamore circuit — higher is closer to ideal quantum execution.
Source: Wold & Kasirajan, arXiv:2512.07311. Google reference XEB from the published Sycamore dataset.
It Scales Near-Linearly
Sampling time for the 53-qubit, 20-cycle Sycamore workload as CPU parallelism increases (lower is better).
Source: Wold & Kasirajan, arXiv:2512.07311, Table 2.
arXiv:2411.12131 · November 2024
Empowering Large-Scale Quantum Circuit Development: Effective Simulation of Sycamore Circuits
Venkateswaran Kasirajan, Torey Battelle, Bob Wold
arXiv:2411.12131The earlier work that the HPC paper builds on: it demonstrates that circuits as large and complex as the random circuit sampling (RCS) circuits from Google's quantum-supremacy experiments can be simulated with high fidelity on classical systems commonly available to developers, using the universal simulator in the Quantum Rings SDK.
Across the studied circuits it achieved an average XEB score of 0.678 — indicating strong correlation with ideal quantum execution and exceeding the XEB values Google reported for the same circuits — while completing execution in about 2.5 days on a laptop, a fraction of the 10,000 years Google predicted classical methods would need.
The practical point: researchers and developers can build, debug, and execute large-scale quantum circuits today, ahead of the general availability of low-error-rate quantum computers.
Independent research
Research Using Quantum Rings
Papers and research artifacts by outside authors that use or cite Quantum Rings products — newest first. Links open on the publisher's site.
Bell-Pair Clifford Memory for Pauli-Channel Spectroscopy on a Noisy QPU: A Hardware Channel-Use Crossover
A. Berrada
Memory-assisted Pauli-channel spectroscopy experiments executed on a superconducting QPU accessed through the Open Quantum platform.
Read the paperHardware-Oriented Hidden-Code-Sampling-Inspired Certification on a 60-Qubit Superconducting Processor
A. Berrada
Certification circuits run on the Rigetti Cepheus-1-108Q processor through the Open Quantum platform and Python SDK.
Read the paperBenchmarking Zero-Setup Quantum Circuit Simulators
A. R. Mazumder, M. Z. Mullath, H. Tepanyan
BlueQubit’s benchmark of zero-setup simulation platforms, comparing the Quantum Rings simulator against its own and others on quantum-volume circuits.
Read the paperFamily-Aware Residual Architecture for Predicting Quantum Circuit Simulation Performance
H. Xing, Y. Jiang, X. Wang, Z. Wang, Z. Jiang
Grew out of the Quantum Rings challenge at MIT iQuHACK 2026 — all training data was generated on the Quantum Rings tensor-network simulator.
Read the paperShallow-depth GHZ state generation on NISQ devices
S. S. Chelluri, S. Schuster, Sumeet, R. Roma
Recommends the Quantum Rings SDK for more efficient and tailored large-scale simulations.
Read the paperUsing Quantum Rings in Your Research?
If you've published a paper, thesis, or research artifact that uses the Quantum Rings simulator or Open Quantum, we'd love to feature it here.
Check the Numbers Yourself
The same simulator from both papers installs with one pip command — run the circuits on your own hardware, free.
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