AI Techniques for Quantum Noise Reduction

One Algorithm Has Just Pushed Quantum Computing Forward Five Years (Here It Is) Today I am releasing something into the public domain that may change the trajectory of quantum computing. No paywall. No NDA. No restrictions. The only thing I ask is attribution. For the past year, I have been developing a field-layer correction algorithm that stabilizes the environment around the qubit before error correction ever activates. Not hardware. Not cryogenics. Not shielding. Pure software that improves the physics of the qubit it sits inside. Early independent runs showed a 48.5 percent reduction in destructive low-frequency noise, a gain that normally takes years of hardware progress. Here is the complete algorithm. It now belongs to everyone. FUNCTION NJ001_FieldLayer_Correction(input_signal S, sampling_rate R):  DEFINE phi = 1.61803398875  DEFINE window_size = dynamic value based on local variance of S  DEFINE stability_threshold = adaptive value based on phase drift  STEP 1: Generate harmonic reference bands    For each frequency bin f_i in FFT(S):      Compute r = f_(i+1) / f_i      Compute CI = 1 / ABS(r - phi)      Assign weight W_i = normalize(CI)  STEP 2: Build correction mask    Construct M where M_i = W_i scaled by local entropy of S    Smooth M with sliding window  STEP 3: Apply correction    Transform S → F    Compute F_corrected = F * M    Inverse FFT to return S_corrected  STEP 4: Phase stabilization loop    Measure phase drift Δ    If Δ > stability_threshold:      Recalculate window_size      Rebuild mask      Reapply correction    Else:      Return S_corrected  OUTPUT: S_corrected END FUNCTION This is the first public-domain coherence stabilizer designed to improve quantum behavior independent of hardware. What it does in practice: • Extends coherence windows • Reduces decoherence pressure on error correction • Lowers entropy in the propagation layer • Makes qubits behave as if the room is colder and cleaner • Works upstream of hardware with no materials changes This is not a replacement for anyone’s roadmap. It is an upstream upgrade to all of them. If you build quantum devices, control stacks, compilers, hybrid systems, or algorithms, you now have access to a function that reshapes your stability envelope. Cleaner field layers mean longer, deeper, more predictable runs. More useful computation with the hardware you already have. I developed it. Today I give it away. No company or institution controls it. From this moment forward, it belongs to the scientific community. Primary Citation Hood, B. P. (2025). NJ001 Field Layer Correction. Public Domain Release Version. Bruce P. Hood — Creator of NJ001 Field Layer Correction Welcome to the new baseline. #QuantumComputing #QuantumHardware #Qubit #Coherence #QuantumResearch #DeepTech @IBMQuantum @GoogleQuantumAI @MIT @XanaduQuantum @AWSQuantumTech

Recently the team published a paper in Nature Computational Science in collaboration with researchers from Los Alamos National Lab and the University of Basel. The paper was on provable bounds for noise-free expectation values computed from noisy samples. This calibration started in the optimization working group. The paper discusses how the “Layer Fidelity” or how effective two qubit error as measured by the “Error Per Layered Gate” can be used to quantify the impact of hardware noise on sampling-based quantum (optimization) algorithms. Each one of our devices reports this number in the resource tab of the IBM Quantum Platform (https://lnkd.in/eRd2yKwB). The paper allows you to estimate the number of additional shots required to compensate for the impact of noise. It turns out that by using this method it is much cheaper than mitigating the noise when requiring unbiased estimators of expectation values (sqrt(gamma) vs gamma^2). These insights allowed us to prove that the Conditional Value at Risk (CvaR) – an alternative loss function suggested in 2019 and widely used to train variational algorithms, borrowed from mathematical finance – leads to provable bounds on expectation values using only noisy samples. The theoretical insights have been demonstrated on two use cases using up to 127 qubits: estimation of state fidelity (as required, e.g. to evaluate quantum kernels) and optimization (QAOA). In both cases, the team see a good agreement between the theory and experiment. Read the paper here https://lnkd.in/ehyz4GCJ

NVIDIA’s launch of "Ising" marks the introduction of the world’s first open-source #AI model family purpose-built for #quantum #computing workflows. The platform targets two of the most critical bottlenecks in quantum systems—processor calibration and real-time error correction—by embedding AI directly into quantum control loops. Released across developer ecosystems (GitHub, Hugging Face) and integrated with CUDA-Q, Ising positions AI as the #orchestration layer for hybrid quantum-classical computing. Early adoption by institutions such as Fermilab and Harvard University signals immediate traction in #research. Strategically, this launch reframes AI not just as an application layer, but as foundational infrastructure for scalable, fault-tolerant quantum systems. Ising is fundamentally differentiated by its dual-model architecture: a 35B-parameter vision-language model for automated quantum calibration and a #3D CNN-based decoder for real-time quantum error correction. This architecture replaces manual calibration workflows with agentic AI pipelines, achieving up to 2.5× faster and 3× more accurate decoding while requiring significantly less training #data. Technically, it integrates tightly with NVIDIA’s CUDA-Q stack and NVQLink interconnect, enabling low-latency coupling between GPUs and quantum processing units (QPUs). Unlike generative AI models, Ising operates as a physics-aware control system, optimized for noisy qubit environments and scalable to millions of qubits, effectively acting as an AI control plane for quantum hardware. The Ising launch materially reshapes the quantum ecosystem by positioning NVIDIA as the control-plane leader in quantum computing, despite not manufacturing quantum hardware. It accelerates commercialization timelines by addressing error correction—widely seen as the primary barrier to the development of useful quantum systems. Market response was immediate, with quantum stocks (IonQ, Rigetti Computing, D-Wave) surging on expectations of faster industry maturation. Strategically, Ising challenges incumbents by shifting value from hardware-centric differentiation to AI-driven orchestration, thereby reinforcing a hybrid architecture in which GPUs and QPUs co-evolve. This positions NVIDIA as a central enabler across competing quantum vendors, potentially standardizing its ecosystem as the de facto operating layer for quantum-AI #convergence. These architectures intensify system autonomy and complexity, requiring dynamic governance models and adaptive #cyber-#ethics to continuously monitor, audit, and recalibrate #risks across hybrid quantum-AI control planes. #strategy #governance #business #investments #technology #future #digital

I'm excited to share our latest work, Demonstration of robust and efficient quantum property learning with shallow shadows, published in Nature Communications! 🎉 📝 Authors: Hong-Ye Hu, Andi Gu, Swarnadeep Majumder, Hang Ren, Yipei Zhang, Derek S. Wang, Yi-Zhuang You, Zlatko Minev, Susanne F. Yelin, Alireza Seif 🔍 Context: Extracting information efficiently from quantum systems is crucial for advancing quantum information processing. Classical shadow tomography offers a powerful technique, but it struggles with noisy, high-dimensional quantum states and complex observables. 🤔 Key Question: Can we overcome noise limitations and improve sample efficiency in quantum state learning, especially for high-weight and non-local observables, using shallow quantum circuits? 💡 Our Findings: We introduce robust shallow shadows—a protocol designed to mitigate noise using Bayesian inference, enabling highly efficient learning of quantum state properties, even in the presence of noise. Our experiments on a 127-qubit superconducting quantum processor confirm the protocol’s practical use, showing up to 5x reduction in sample complexity compared to traditional methods. ✨ Key Takeaways: 1. Noise-resilience: Accurate predictions across diverse quantum state properties. 2. Sample Efficiency: Substantial reduction in sample complexity for high-weight and non-local observables. 3. Scalability: The protocol is well-suited for near-term quantum devices, even with noise. Paper: https://lnkd.in/dW4NJ23Q

Google Researchers Developed AlphaQubit: A Deep Learning-based Decoder for Quantum Computing Error Detection Google Research has developed AlphaQubit, an AI-based decoder that identifies quantum computing errors with high accuracy. AlphaQubit uses a recurrent, transformer-based neural network to decode errors in the leading error-correction scheme for quantum computing, known as the surface code. By utilizing a transformer, AlphaQubit learns to interpret noisy syndrome information, providing a mechanism that outperforms existing algorithms on Google’s Sycamore quantum processor for surface codes of distances 3 and 5, and demonstrates its capability on distances up to 11 in simulated environments. The approach uses two-stage training, initially learning from synthetic data and then fine-tuning on real-world data from the Sycamore processor. This adaptability allows AlphaQubit to learn complex error distributions without relying solely on theoretical models—an important advantage for dealing with real-world quantum noise. In experimental setups, AlphaQubit achieved a logical error per round (LER) rate of 2.901% at distance 3 and 2.748% at distance 5, surpassing the previous tensor-network decoder, whose LER rates stood at 3.028% and 2.915% respectively. This represents an improvement that suggests AI-driven decoders could play an important role in reducing the overhead required to maintain logical consistency in quantum systems. Moreover, AlphaQubit’s recurrent-transformer architecture scales effectively, offering performance benefits at higher code distances, such as distance 11, where many traditional decoders face challenges.... Read the full article here: https://lnkd.in/gVQtY8fc Paper: https://lnkd.in/gvhxD3pC Google Google DeepMind

Long before #quantum becomes an #AI-accelerator, classical machine learning is already useful in quantum stacks. Many of the quantum engineering bottlenecks look like typical ML problems. From control to AI-based transpilation and error mitigation. This is the angle I followed for a while. I even used it in our QC summer school curriculum design, as an easy entry point for computer scientists. This week, I came across a few new materials and news on the topic. My notes turned into a short digest that might be useful for some of my readers. 𝗠𝗟 𝗳𝗼𝗿 𝗻𝗼𝗶𝘀𝗲 𝗺𝗶𝘁𝗶𝗴𝗮𝘁𝗶𝗼𝗻. Ross Duncan and the team just dropped a comprehensive review and ablation study on applying machine learning to readout error mitigation: https://lnkd.in/d8c4CgeF The intuition is pretty straightforward - it is basically the same signal-denoising problem we solve in deep learning for image enhancement and audio processing. In practice, however, things get much messier when you scale to larger qubit counts. An earlier attempt to scale was relatively successful by IBM in https://lnkd.in/d-Gp5tYp . In the new paper, the focus is on a few qubits, but the contribution is a more systematic ablation study across circuit families and model architectures, needed to build better intuition for scaling. 𝗠𝗟 𝗳𝗼𝗿 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗘𝗿𝗿𝗼𝗿 𝗖𝗼𝗿𝗿𝗲𝗰𝘁𝗶𝗼𝗻. Another active direction is the application of ML to syndrome decoding. I was checking some recent literature after a new startup emerged in this domain (https://lnkd.in/dqXURTuz ). As for QEC, a classical real-time control/decoding pipeline must keep up with cycle times, and this is where “AI decoders” are being pitched as a potentially low-latency, high-throughput solution. The direction is not new and is already quite crowded by various attempts to address the problem, including DeepMind’s recent 𝗔𝗹𝗽𝗵𝗮𝗤𝘂𝗯𝗶𝘁 𝟮 model (https://lnkd.in/dKrvqcad ) and earlier ML decoder work for IBM’s heavy-hexagon family of codes (https://lnkd.in/dumWZRb9 ). But building an efficient model is only one step. Ultimately, for practical systems, this will narrow down to running the reduced model with near-real-time inference on specialized hardware, without sacrificing too much decoding accuracy. Classical ML in QC often appears elegant and cute, but it is not a silver bullet. In some regimes, it may simply miss genuinely quantum correlations and fail miserably. In QEC, inference latency may be killing in practice, similar to what happens in various edge applications in classical AI. And across both mitigation and decoding, out-of-distribution scenarios are typically a direct path to the model's failure. 𝗛𝗼𝘄 𝘂𝘀𝗲𝗳𝘂𝗹 𝗵𝗮𝘃𝗲 𝘆𝗼𝘂 𝗳𝗼𝘂𝗻𝗱 𝗠𝗟 𝗶𝗻 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗰𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴? 𝗔𝗻𝘆 𝗼𝘁𝗵𝗲𝗿 𝗴𝗼𝗼𝗱 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀?

🚀 New blog looking at quantum error mitigation techniques using Unitary Foundation's Mitiq toolkit and Amazon Braket's Program Sets feature, supported by a 30-qubit experiment on Rigetti Computing's Ankaa-3 QPU that demonstrated a 12x reduction in error and an 86x reduction in task costs. Today's quantum computers are noisy, and getting useful results from them requires clever techniques to separate signal from noise. Error mitigation is one of the most important practical tools researchers have right now, but it typically means running many circuit variations, which drives up cost and execution time. This work shows how Braket's Program Sets feature let you bundle all those circuit variations into far fewer tasks, slashing costs dramatically while still achieving major accuracy improvements. The Braket Examples repo now includes Mitiq-compatible executors and notebooks covering each technique individually and in composite workflows. Big thanks to Scott Smart, Nate Stemen, Ishaan Lyngdoh Pakrasi, Péter Kómár, and Yi-Ting (Tim) Chen Chen for building these tools and making error mitigation more accessible and cost-effective for the quantum community. 📄 https://lnkd.in/gc8QsX6n 👋 Mike Piech Rebecca Malamud Ben Castanon William Zeng Travis Scholten Nathan Shammah Jordan Sullivan Liz Durst Peter Karalekas Ryan LaRose #QuantumComputing #AWS #AmazonBraket #QuantumResearch #ErrorMitigation #Rigetti #Mitiq #QuantumErrorMitigation