Qubit Design Basics

The dirty secret of Quantum Computing… Materials are the limiting factor. Everyone talks about quantum algorithms, error correction, and qubit counts. But the real killer of quantum computing isn’t software, it’s materials. Superconducting qubits don’t decohere because we lack clever code. They decohere because: – Surface oxides introduce two-level system noise. – Impurities and defects act like microscopic time bombs. – Atomic-scale disorder destroys coherence before circuits can compute anything useful. That’s why the biggest breakthroughs aren’t happening in code, they’re happening in materials labs. → Google is building qubits with ultra-clean Al/Si interfaces to suppress noise. → IBM is investing in substrate purification to push coherence times further. → Labs worldwide are chasing epitaxial aluminum films with sub-ppm impurity levels. The “quantum revolution” is being held back by dirt, literally. Until we tame materials noise, scaling qubits is just scaling errors. Quantum doesn’t need another hype cycle. It needs a materials breakthrough. #QuantumComputing #MaterialScience #GrowthAndInnovation #DeepTech

Excited to announce a new #QuantumComputing result from JPMorganChase's Global Technology Applied Research, titled “Fast Convex Optimization with Quantum Gradient Descent,” which has just appeared on arXiv! Convex #optimization is a fundamental subroutine in #MachineLearning, engineering, and #DataScience, with many applications in financial engineering. We develop new #QuantumAlgorithms in the “derivative-free” setting where the algorithm only uses the function value and not its gradient. We show that #quantum algorithms without gradient access can match the convergence of classical gradient-descent methods, which do assume gradient access! In the derivative-free setting, this translates to an exponential speedup in terms of the dimension.   Our results also have applications outside the black-box setting. By leveraging a connection between semi-definite programming and eigenvalue optimization, we develop algorithms that exhibit the best known quantum or classical runtimes for semi-definite programming, linear programming, and zero-sum games, which are the three most well-studied classes of structured convex optimization problems. These classes model many practical problems of interest, including portfolio optimization and least-squares regression problems. Coauthors: Brandon Augustino, Dylan Herman, Enrico Fontana, Junhyung Lyle Kim, Jacob Watkins, Shouvanik Chakrabarti, and Marco Pistoia. Link to the article: https://lnkd.in/eMtqXM-r

Major milestone achieved in new quantum computing architecture "A team led by the U.S. Department of Energy (DOE)’s Argonne National Laboratory has achieved a major milestone toward future quantum computing. They have extended the coherence time for their novel type of qubit to an impressive 0.1 milliseconds — nearly a thousand times better than the previous record." "The team’s qubit is a single electron trapped on an ultraclean solid-neon surface in a vacuum. The neon is important because it resists disturbance from the surrounding environment. Neon is one of a handful of elements that do not react with other elements. The neon platform keeps the electron qubit protected and inherently guarantees a long coherence time." "Yet another important attribute of a qubit is its scalability to link with many other qubits. The team achieved a significant milestone by showing that two-electron qubits can couple to the same superconducting circuit such that information can be transferred between them through the circuit. This marks a pivotal stride toward two-qubit entanglement, a critical aspect of quantum computing." "The team has not yet fully optimized their electron qubit and will continue to work on extending the coherence time even further as well as entangling two or more qubits." This research was published in Nature Physics (https://lnkd.in/d5Y5Dfea) https://lnkd.in/dkXd_Uje

The Schrödinger Equation Gets Practical: Quantum Algorithm Speeds Up Real-World Simulations Quantum computing has taken a major leap forward with a new algorithm designed to simulate coupled harmonic oscillators, systems that model everything from molecular vibrations to bridges and neural networks. By reformulating the dynamics of these oscillators into the Schrödinger equation and applying Hamiltonian simulation methods, researchers have shown that complex physical systems can be simulated exponentially faster on a quantum computer than with traditional algorithms. This breakthrough demonstrates not only a practical use of the Schrödinger equation but also the deep connection between quantum dynamics and classical mechanics. The study introduces two powerful quantum algorithms that reduce the required resources to only about log(N) qubits for N oscillators, compared to the massive computational demands of classical methods. This exponential speedup could transform fields such as engineering, chemistry, neuroscience, and material science, where coupled oscillators serve as the backbone of real-world modeling. By bridging theory and application, this research underscores how quantum computing is redefining problem-solving in physics and beyond. With proven exponential advantages and the ability to simulate systems once thought computationally impossible, this quantum algorithm marks a milestone in quantum simulation, Hamiltonian dynamics, and real-world physics applications. The findings point toward a future where quantum computers can accelerate scientific discovery, optimize engineering designs, and even open new frontiers in AI and computational neuroscience. #QuantumComputing #SchrodingerEquation #HamiltonianSimulation #QuantumAlgorithm #CoupledOscillators #QuantumPhysics #ComputationalScience #Neuroscience #Chemistry #Engineering

In finance, Monte Carlo simulations help us to measure risks like VaR or price derivatives, but they’re often painfully slow because you need to generate millions of scenarios. Matsakos and Nield suggest something different: they build everything directly into a quantum circuit. Instead of precomputing probability distributions classically, they simulate the future evolution of equity, interest rate, and credit variables inside the quantum computer, including binomial trees for stock prices, models for rates, and credit migration or default models. All that is done within the circuit, and then quantum amplitude estimation is used to extract risk metrics without any offline preprocessing. This means you keep the quadratic speedup of quantum MC while also removing the bottleneck of classical distribution generation. If you want to explore the topic further, here is the paper: https://lnkd.in/dMHeAGnS #physics #markets #physicsinfinance #derivativespricing #quant #montecarlo #simulation #finance #quantitativefinance #financialengineering #modeling #quantum

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

I've been deep-diving into Google's recent claim of Quantum Advantage—an algorithm supposedly 13,000X faster than a supercomputer! It's a complex topic involving concepts like Out-of-Time-Ordered Correlators (OTOC), but incredibly rewarding to explore. I took the challenge to implement the same code (which involves circuits like the one pictured) and now I'm sharing all my learnings. For those interested in understanding how this breakthrough works—from the quantum physics to the circuit mechanics: My Substack Explainer (For Laymen): [Google's Quantum Breakthrough Explained] (https://lnkd.in/gNMe-Ey4) The Code (My Implementation): [GitHub Repo] (https://lnkd.in/gMWqSYYK) This experience provided a fantastic hands-on look at the Quantum Information Scrambling Process. Check out the links and let me know your thoughts on the code or the explanation! #QuantumComputing #QuantumMechanics #GitHubProject #TechImplementation #SoftwareEngineering #CodingLife #QuantumLeap

Wave-based inverse problems are prevalent in disciplines such as seismology, medical imaging, nondestructive testing and metamaterial research. However, these fields are fundamentally limited by the current state of conventional high-performance computing resources due to the excessive computational cost of the numerical wave simulation. Future quantum computers are expected to offer promising runtime improvements for numerous computational problems.   In this work, led by Cyrill Bösch, Malte Schade, Giacomo Aloisi and Scott Keating, we present a quantum algorithmic framework for simulating linear, anti-Hermitian (lossless) wave equations in heterogeneous, anisotropic media. It encompasses a broad class of wave equations, including the acoustic wave equation, Maxwell’s equations and the elastic wave equation. Our formulation is compatible with standard numerical discretization schemes and allows for the efficient implementation of multiple practically relevant time- and space-dependent sources. Furthermore, we demonstrate that subspace energies can be extracted and wave fields compared through an L2 loss function, achieving optimal precision scaling with the number of samples taken. Additionally, we introduce techniques for incorporating boundary conditions and linear constraints that preserve the anti-Hermitian nature of the equations.   Leveraging the Hamiltonian simulation algorithm, our framework achieves a quartic speedup over classical solvers in three-dimensional simulations, under conditions of sufficiently global measurements and compactly supported sources and initial conditions. This quartic speedup is optimal for time-domain solutions, as the Hamiltonian of the discretized wave equations has local couplings. In summary, our framework provides a versatile approach for simulating wave equations on quantum computers, offering substantial speedups over state-of-the-art classical methods. The open-access paper can be found here: https://lnkd.in/de9ubsyK This work would not have been possible without the help and advice of Marion Dugué, Patrick Marty, Ines Ulrich, Václav Hapla and several colleagues at Google Quantum AI (Ryan Babbush, Rolando Somma and many others). #quantumcomputing #highperformancecomputing #waves #physics #metamaterials #seismology #ndt #medicalimaging #science #research

First Direct Evidence of ‘Nuclear-Spin Dark State’ Could Stabilize Quantum Computers Researchers at the University of Rochester have directly confirmed the existence of a “nuclear-spin dark state”, a long-theorized quantum phenomenon that could dramatically improve the stability of quantum systems. This breakthrough validates decades of theoretical predictions and may pave the way for more reliable and powerful quantum computers. What is a Nuclear-Spin Dark State? • Quantum computers are extremely fragile, as their qubits are easily disrupted by environmental noise, leading to errors and instability. • A nuclear-spin dark state is a unique quantum state where the nucleus of an atom effectively becomes “hidden” from external disturbances, protecting qubits from decoherence. • This state reduces quantum noise, meaning quantum computers could maintain stable operations for much longer periods. Why This Discovery is Significant • First Experimental Confirmation of a Long-Theorized Concept • Scientists have predicted the existence of nuclear-spin dark states for decades, but this is the first direct evidence proving their reality. • Potential for Error-Resistant Quantum Computing • By utilizing nuclear-spin dark states, quantum computers could become far more resilient to environmental interference, reducing the need for error correction. • Opens Doors for Advanced Quantum Technologies • This discovery sets the stage for quantum systems that can operate more stably, making quantum computing more viable for large-scale applications. What’s Next? • Integrating Dark State Protection into Quantum Processors • Researchers will work on applying nuclear-spin dark state techniques to existing quantum hardware. • Reducing the Need for Costly Error Correction • Quantum error correction is currently a major bottleneck in making quantum computers practical. If nuclear-spin dark states can mitigate errors naturally, it could accelerate quantum computing development. • Scaling Up to Multi-Qubit Quantum Systems • Future research will explore whether these dark states can be applied to more complex quantum networks, potentially leading to stable, fault-tolerant quantum computers. This breakthrough represents a major step toward building quantum systems that are not only more stable but also more practical for real-world applications, bringing us closer to error-free, large-scale quantum computing.

𝗠𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗶𝗻𝗴 𝗰𝗼𝗵𝗲𝗿𝗲𝗻𝗰𝗲 𝗶𝗻 𝘀𝘂𝗽𝗲𝗿𝗰𝗼𝗻𝗱𝘂𝗰𝘁𝗶𝗻𝗴 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗼𝗿𝘀 𝗶𝘀 𝗮 𝗰𝗼𝗻𝘀𝘁𝗮𝗻𝘁 𝗯𝗮𝘁𝘁𝗹𝗲. While many factors contribute to qubit decoherence, 𝗧𝘄𝗼-𝗟𝗲𝘃𝗲𝗹 𝗦𝘆𝘀𝘁𝗲𝗺 (𝗧𝗟𝗦) 𝗱𝗲𝗳𝗲𝗰𝘁𝘀 remain among the most 𝗳𝗿𝘂𝘀𝘁𝗿𝗮𝘁𝗶𝗻𝗴 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀. 🔹 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗧𝗟𝗦 𝗱𝗲𝗳𝗲𝗰𝘁𝘀, typically found in the surfaces and interfaces of superconducting circuits, can r𝗲𝘀𝗼𝗻𝗮𝗻𝘁𝗹𝘆 𝗰𝗼𝘂𝗽𝗹𝗲 𝘄𝗶𝘁𝗵 𝗾𝘂𝗯𝗶𝘁𝘀, leading to 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝗱𝗲𝗰𝗼𝗵𝗲𝗿𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗴𝗮𝘁𝗲 𝗲𝗿𝗿𝗼𝗿𝘀. These defects are particularly problematic due to their spatial and temporal instability, causing 𝘂𝗻𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗮𝗯𝗹𝗲 "𝗱𝗿𝗼𝗽𝗼𝘂𝘁𝘀" 𝗶𝗻 𝗾𝘂𝗯𝗶𝘁 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲. When it comes to mitigating TLS noise, several approaches exist: 🔹𝗛𝗮𝗿𝗱𝘄𝗮𝗿𝗲-𝗟𝗲𝘃𝗲𝗹 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 - 𝗠𝗮𝘁𝗲𝗿𝗶𝗮𝗹 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: High-purity materials and advanced fabrication techniques to reduce TLS density. - 𝗦𝘂𝗿𝗳𝗮𝗰𝗲 𝗧𝗿𝗲𝗮𝘁𝗺𝗲𝗻𝘁𝘀: Minimizing lossy interfaces where TLSs often reside. - 𝗖𝗶𝗿𝗰𝘂𝗶𝘁 𝗗𝗲𝘀𝗶𝗴𝗻: Engineering qubit circuits to minimize coupling to TLSs. 🔹𝗖𝗼𝗻𝘁𝗿𝗼𝗹 & 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 - 𝗤𝘂𝗯𝗶𝘁 𝗙𝗿𝗲𝗾𝘂𝗲𝗻𝗰𝘆 𝗧𝘂𝗻𝗶𝗻𝗴: Shifting qubit frequencies away from TLS resonances, widely used in tunable transmon architectures. - 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗗𝗲𝗰𝗼𝘂𝗽𝗹𝗶𝗻𝗴: Pulse sequences that average out the effect of TLS noise. - 𝗔𝗰𝘁𝗶𝘃𝗲 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸: Real-time monitoring and adaptive qubit control. While some of these techniques come with considerable overhead, new approaches are emerging to address the TLS challenge more efficiently: 🔹𝗧𝗵𝗲 𝗧𝗜𝗖-𝗧𝗔𝗤 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: 𝗔 𝗡𝗲𝘄 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 The Siddiqi group just introduced a new technique called 𝗧𝗜𝗖-𝗧𝗔𝗤 (Targeted In-situ Control of TLS and Qubits): - 𝗦𝗶𝗻𝗴𝗹𝗲 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗟𝗶𝗻𝗲: Provides local and independent control of each qubit’s noise environment with a single on-chip control line. - 𝗘𝗹𝗲𝗰𝘁𝗿𝗶𝗰 𝗙𝗶𝗲𝗹𝗱 𝗧𝘂𝗻𝗶𝗻𝗴: Instead of shifting the qubit frequency, TIC-TAQ dynamically tunes TLSs away from the qubit frequency by applying a local electric field. - 𝗖𝗼𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝗿𝘆 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲: Expected to enhance existing strategies for managing TLS-induced errors. 𝗧𝗜𝗖-𝗧𝗔𝗤 𝘀𝗵𝗼𝘄𝘀 𝗽𝗿𝗼𝗺𝗶𝘀𝗶𝗻𝗴 𝗿𝗲𝘀𝘂𝗹𝘁𝘀: - 36% Improvement in single-qubit error rates. - 17% Increase in qubit relaxation times (T₁). - 4x Suppression in TLS-induced performance outliers. 𝗪𝗵𝘆 𝗗𝗼𝗲𝘀 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿? TLS defects are a roadblock on the path to fault-tolerant quantum computing. It’s great to see how hardware innovations and smart control techniques make a measurable impact. Are you more optimistic about hardware-based or control-based solutions for mitigating TLS noise? 📸 Image Credits: Larry Chen, Kan-Heng Lee et al. (arXiv, 2025)