Quantum Hardware Tuning Using Software Tools

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

“Before you can use a quantum computer, you first need to be able to turn it on.” Research that I carried out during my PhD at Oxford has brought us closer to that goal. I'm pleased to share that our paper titled “Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning” has been published in Nature Scientific Reports. We developed CATSAI (pronounced: Cats-eye), an algorithm capable of tuning three different semiconductor quantum devices—silicon finFET, Ge/Si nanowire, and Ge/SiGe heterostructure— to double quantum dots, using a single approach Forming double quantum dots in these devices is a key step towards creating qubits, the essential building blocks of quantum computers. Not long ago, it was thought that each device type would need its own specialized algorithm. CATSAI changes that by tuning different devices and revealing the complex hypersurfaces that separate regions where current flows from those where it’s blocked. In some cases, finding a double quantum dot is like finding a needle in a haystack—sometimes in just 0.002% of the search space. CATSAI does this on the order of minutes —far quicker than what would typically be possible manually. I remember when I first tried to tune a double quantum dot at the start of my PhD - it took me two weeks. That became the last time I tried to do it by hand. CATSAI relies on two key strategies: 1. Training a machine learning model to recognize single quantum dot features. 2. Leveraging reliable data on where these single dots are located in voltage space to narrow down the search for double quantum dots. This work wouldn’t have been possible without the support of our co-authors and collaborators at IST Austria and the University of Basel. Special thanks to Natalia Ares, who supervised my PhD research and provided invaluable guidance and support throughout this project. I’m also grateful for the opportunity she gave me to work with such an amazing team and technology. Interested in learning more? You can read the full paper here: https://lnkd.in/e7Vz8We9 The possibilities ahead are vast, and I’m eager to see where AI software for semiconductor quantum devices takes us next!

**Qubits drift. Google Just Gave It an Autopilot.** Quantum processors are not stable machines – they slowly drift out of tune. Tiny changes in temperature, vibrations, and electronics mean the gate you calibrated in the morning is slightly wrong by the afternoon. Over time, that drift quietly increases the error rate, and even with quantum error correction (QEC), your logical qubit fidelity starts to fall off. The standard fix today is brutal: stop the computation, recalibrate, then resume. That’s barely acceptable for short experiments, and totally unrealistic for fault-tolerant algorithms that might run for hours or days. Google Quantum AI’s new paper, “Reinforcement Learning Control of Quantum Error Correction”, takes a different approach: they merge calibration with computation. Instead of pausing the QEC cycles, they: • Treat QEC syndromes (error signals) as feedback about how the hardware is drifting. • Use a reinforcement learning (RL) agent to nudge thousands of control parameters (pulse amplitudes, frequencies, couplings) while the code is running. • Optimize for lower logical error rate, not just pretty single-qubit gate metrics. On their superconducting Willow processor, this RL “autopilot”: • Improves logical error-rate stability of a distance-5 surface code by about 3.5× against injected drift. • Gives ~20% extra suppression of logical error rate on top of already hand-tuned, state-of-the-art calibration. • Scales in simulation to larger surface codes (up to distance-15) with optimization speed that doesn’t degrade with code size. How does this compare to other decoders? • Classical decoders (like matching decoders) assume the noise model is roughly fixed and then compute the best correction from the syndrome history. • Learned decoders try to map syndromes → corrections more accurately, but still assume a mostly stable device. • RL-QEC doesn’t replace the decoder – it steers the hardware and decoder together so the same QEC stack keeps working even as the environment drifts. If we want truly useful quantum computers, adding more qubits isn’t enough. We’ll also need systems that learn to stay calibrated while they compute and this paper is one of the first serious demonstrations of that idea. Paper: https://lnkd.in/ek2pDgek #QuantumComputing #QuantumErrorCorrection #ReinforcementLearning #GoogleQuantumAI