Managing Qubit Interaction and Stability in Quantum Computing

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

🚨 Exciting #quantumcomputing alert! Now #QEC primitives actually make #quantumcomputers more powerful! 75 qubit GHZ state on a superconducting #QPU 🚨 In our latest work we address the elephant in the room about #quantumerrorcorrection - in the current era where qubit counts are a bottleneck in the systems available, adopting full-blown QEC can be a step backwards in terms of computational capacity. This is because even when it delivers net benefits in error reduction, QEC consumes a lot of qubits to do so and we just don't have enough right now... So how do we maximize value for end users while still pushing hard on the underpinning QEC technology? To answer this the team at Q-CTRL set out to determine new ways to significantly reduce the overhead penalties of QEC while delivering big benefits! In this latest demonstration we show that we can adopt parts of QEC -- indirect stabilizer measurements on ancilla qubits -- to deliver large performance gains without the painful overhead of logical encoding. And by combining error detection with deterministic error suppression we can really improve efficiency of the process, requiring only about 10% overhead in ancillae and maintaining a very low discard rate of executions with errors identified! Using this approach we've set a new record for the largest demonstrated entangled state at 75 qubits on an IBM quantum computer (validated by MQC) and also demonstrated a totally new way to teleport gates across large distances (where all-to-all connectivity isn't possible). The results outperform all previously published approaches and highlight the fact that our journey in dealing with errors in quantum computers is continuous. Of course it isn't a panacea and in the long term as we try to tackle even more complex algorithms we believe logical encoding will become an important part of our toolbox. But that's the point - logical QEC is just one tool and we have many to work with! At Q-CTRL we never lose sight of the fact that our objective is to deliver maximum capability to QC end users. This work on deploying QEC primitives is a core part of how we're making quantum technology useful, right now. https://lnkd.in/gkG3W7eE

MIT Sets Quantum Computing Record with 99.998% Fidelity Researchers at MIT have achieved a world-record single-qubit fidelity of 99.998% using a superconducting qubit known as fluxonium. This breakthrough represents a significant step toward practical quantum computing by addressing one of the field’s greatest challenges: mitigating noise and control imperfections that lead to operational errors. Key Highlights: 1. The Problem: Noise and Errors • Qubits, the building blocks of quantum computers, are highly sensitive to noise and imperfections in control mechanisms. • Such disturbances introduce errors that limit the complexity and duration of quantum algorithms. “These errors ultimately cap the performance of quantum systems,” the researchers noted. 2. The Solution: Two New Techniques To overcome these challenges, the MIT team developed two innovative techniques: • Commensurate Pulses: This method involves timing quantum pulses precisely to make counter-rotating errors uniform and correctable. • Circularly Polarized Microwaves: By creating a synthetic version of circularly polarized light, the team improved the control of the qubit’s state, further enhancing fidelity. “Getting rid of these errors was a fun challenge for us,” said David Rower, PhD ’24, one of the study’s lead researchers. 3. Fluxonium Qubits and Their Potential • Fluxonium qubits are superconducting circuits with unique properties that make them more resistant to environmental noise compared to traditional qubits. • By applying the new error-mitigation techniques, the team unlocked the potential of fluxonium to operate at near-perfect fidelity. 4. Implications for Quantum Computing • Achieving 99.998% fidelity significantly reduces errors in quantum operations, paving the way for more complex and reliable quantum algorithms. • This milestone represents a major step toward scalable quantum computing systems capable of solving real-world problems. What’s Next? The team plans to expand its work by exploring multi-qubit systems and integrating the error-mitigation techniques into larger quantum architectures. Such advancements could accelerate progress toward error-corrected, fault-tolerant quantum computers. Conclusion: A Leap Toward Practical Quantum Systems MIT’s achievement underscores the importance of innovation in error correction and control to overcome the fundamental challenges of quantum computing. This breakthrough brings us closer to the realization of large-scale quantum systems that could transform fields such as cryptography, materials science, and complex optimization problems.

A quantum computer that learns from its own errors while it's computing. That's the framing in a recent paper from Google Quantum AI and Google DeepMind on reinforcement learning control of quantum error correction. Large quantum processors drift. The standard fix is to halt the computation and recalibrate, which won't scale to algorithms expected to run for days or weeks. The authors ask whether QEC can calibrate itself from the data it already produces. The idea: repurpose error detection events as a training signal for a reinforcement learning agent that continuously tunes the physical control parameters (pulse amplitudes, detunings, DRAG coefficients, CZ parameters, and so on). Rather than optimizing logical error rate directly, which is expensive and global, the agent minimizes average detector-event rate, a cheap local proxy whose gradient is approximately aligned with the gradient of LER in the small-perturbation regime. The results on a Willow superconducting processor: - On distance-5 surface and color codes, RL fine-tuning after conventional calibration and expert tuning yields about 20% additional LER suppression - Against injected drift, RL steering improves logical stability 2.4x, rising to 3.5x when decoder parameters are also steered - New record logical error per cycle: 7.72(9)×10⁻⁴ for a distance-7 surface code (with the AlphaQubit2 decoder) and 8.19(14)×10⁻³ for a distance-5 color code (with Tesseract) - In simulation, the framework scales to a distance-15 surface code with roughly 40,000 control parameters, with a convergence rate that is independent of system size The broader takeaway: calibration and computation may not need to be separate phases. If detector statistics can carry enough information to steer a large control stack online, fault tolerance becomes less about pausing to retune and more about a processor that keeps learning while it computes. Worth noting that the current experiments rely on short repeated memory circuits, so real-time steering during a single long logical algorithm (where exploration noise would affect the computation directly) remains future work. Paper: https://lnkd.in/gVQXnpzZ

🔴 NEW ARTICLE: Quantum Now Has a Path to Scale. Seed IQ Just Proved It. This isn’t theoretical. This isn’t simulated. ➡️ We ran Seed IQ (Intelligence + Quantum)™ on live IBM quantum hardware ➡️ Under real noise conditions ➡️ And held system-level fidelity at ~0.969, while preserving coherence and entanglement with two bell pairs across 3 logical qubits ▪️ While standard approaches decohere and collapse under these same NISQ conditions. This changes the quantum conversation entirely. 🔸 🔸 Seed IQ just surpassed the most advanced solutions for QEC (Quantum Error Correction) that exist in the quantum computing field today (in known literature and published research)... … while introducing something quantum has never had: ▪️ A way to operate reliably under real conditions without breaking, using system-level adaptive multiagent autonomous control. This is what makes scaling quantum possible. This is what makes computing under quantum entanglement possible. ➡️ The current state of Quantum doesn’t fail because of the physics ➡️ It fails because there is no adaptive control layer governing it 🔸🔸 And that’s what we just demonstrated with Seed IQ. What Seed IQ demonstrated is that stability in quantum systems does not have to emerge solely from better hardware or more complex encoding schemes. It can be actively enforced at the system level, in real time, under real-world conditions. And it changes the economics of quantum entirely. The implications of this — and what these results establish as a new benchmark for quantum system performance — become clear when evaluated in direct comparison with current state-of-the-art quantum error correction approaches. This article included a detailed execution summary of the hardware runs by my partner and Chief Innovations Officer, Denis O., followed by a side-by-side comparison of the latest top QEC achievements in field, including Google's Willow chip. This is the shift from lab-controlled validation → real world quantum compute. ➡️ Seed IQ introduces a new path for quantum computing to scale under real hardware operating conditions. 🥳 #AIX #SeedIQ #QuantumAI #QuantumComputing #MultiAgentSystems #ActiveInference #Willow AIX Global Innovations