Circuit Approaches in Quantum Research

Google Quantum AI Demonstrates Three Dynamic Surface Codes, Advancing Fault-Tolerant Quantum Computing Introduction Quantum computers promise exponential gains but remain constrained by extreme fragility: qubits are easily disrupted by noise, making error correction the central challenge of the field. Google Quantum AI has now taken a major step toward practical fault tolerance by successfully implementing three dynamic versions of the surface code—one of the most promising quantum error-correction frameworks. Key Developments • The team realized three distinct dynamic surface code circuits—hex, iSWAP, and walking—originally proposed in theoretical work by co-author Matt McEwen. • Their experiments validate that multiple circuit variations can work on real hardware, expanding pathways for adapting error-correction codes to specific device architectures. • Hex circuit: Recompiles the surface code onto a hexagonal grid, reducing connectivity requirements from four neighbors to three. This simplifies fabrication and achieved 2.15× better error suppression. • iSWAP circuit: Replaces CZ gates with iSWAP gates, which are easier to execute and avoid leakage errors. Though they introduce CPHASE errors, the team showed strong performance even on hardware optimized for CZ gates, achieving 1.56× error suppression. • Walking circuit: Allows qubits to exchange roles, effectively “walking” logical information across the chip. This helps isolate and clean leakage errors and offers a new method for routing logical qubits, delivering 1.69× better suppression. • All three implementations successfully detected and corrected noise without disturbing quantum information, confirming the practicality of dynamic constructions. Scientific Significance • This is the strongest evidence yet that dynamic surface codes—adapted to hardware constraints—can function reliably in real quantum devices. • The team also introduced a simplified “detector budgeting” technique, enabling easier analysis of how specific error sources impact logical performance. • The work opens new avenues for designing codes tailored to imperfect hardware, enabling better yield and robustness as systems scale. • Upcoming experiments will explore even more advanced dynamic circuits, including those based on the LUCI framework for routing around faulty qubits. Why This Matters Reliable quantum error correction is the linchpin for large-scale quantum computing. Google’s demonstration shows that error-correcting codes can be adapted dynamically to real hardware constraints—unlocking higher performance, easier fabrication, and more flexible architectures. This progress accelerates the roadmap toward fault-tolerant quantum systems capable of solving real-world scientific and industrial problems. I share daily insights with 34,000+ followers across defense, tech, and policy. If this topic resonates, I invite you to connect and continue the conversation. Keith King https://lnkd.in/gHPvUttw

I’d like to draw your attention to a new paper on arXiv, “Shallow-circuit Supervised Learning on a Quantum Processor”, from IBM and Qognitive that develops a Hamiltonian-based framework for quantum machine learning. Instead of fixed amplitude or angle encodings used in many prior approaches, our method learns a local Hamiltonian embedding for classical data. https://lnkd.in/ejcxYstW We are very interested in new approaches to QML as we deal with recurring bottlenecks like expensive classical data loading and difficult training dynamics in parameterized circuit models. Here, both the feature operators and the label operator are learned during training, with predictions obtained from measurements on an approximate ground state. This aims to avoid those bottlenecks. A key enabler is Sample-based Krylov Quantum Diagonalization (SKQD), which approximates low-energy states by sampling from time-evolved Krylov states and then diagonalizing the Hamiltonian in the sampled subspace. SKQD was recently employed to estimate low-energy properties of impurity models (https://lnkd.in/epwCrG5R). In our setting, restricting to 2-local Hamiltonian embeddings keeps the required time-evolution circuits relatively shallow, which helps make the approach practical on current quantum processors. The team demonstrates end-to-end training on IBM Heron processor up to 50 qubits, with non-vanishing gradients and strong proof-of-concept performance on a binary classification task. There are many exciting next steps here, including testing on broader datasets, using more expressive operator ansatz, and performing systematic comparisons to strong classical baselines to pinpoint when Hamiltonian-based encodings offer the right inductive bias. I encourage the community to try out this approach and explore where it can be extended in meaningful ways.

Something I haven't understood for years is why not more people work on inorganic masks for superconducting circuits. It is a rabbit hole I explored during my PhD, and seeing papers like the recent one from the Robert McDermott group and Qolab only strengthens the conviction that we need a fabrication revolution. The paper does a great job confirming what many of us suspected: 𝗱𝗲𝗰𝗼𝗵𝗲𝗿𝗲𝗻𝗰𝗲 𝗶𝘀𝗻'𝘁 𝗷𝘂𝘀𝘁 𝗮 𝗴𝗲𝗻𝗲𝗿𝗶𝗰 𝗺𝗮𝘁𝗲𝗿𝗶𝗮𝗹𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺, 𝗶𝘁'𝘀 𝗮 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. Their work shows that the most damaging defects are concentrated within 500 nm of the qubit junction, precisely where residues from standard liftoff fabrication are present. Their conclusion is a call to arms: we need "new approaches to device fabrication that... avoid additive liftoff steps that are prone to leaving lossy organic residues". So, how do we get rid of these residues? 𝗧𝗵𝗲 𝗼𝗯𝘃𝗶𝗼𝘂𝘀 𝗮𝗻𝘀𝘄𝗲𝗿 𝗶𝘀 𝘁𝗼 𝘀𝘁𝗼𝗽 𝘂𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 𝘁𝗵𝗮𝘁 𝗰𝗿𝗲𝗮𝘁𝗲 𝘁𝗵𝗲𝗺. This means rethinking our reliance on organic resists and the classic Dolan bridge or Manhattan technique, which, besides requiring liftoff, inevitably also creates parasitic shadow junctions. This is precisely the challenge I tackled in the second part of my PhD. My thesis proposed a radical alternative: a fully mask-based, in-situ process using hard masks made of silicon. The concept is to pattern the entire qubit circuit, including the Josephson junction, using a 𝘁𝗵𝗶𝗰𝗸 silicon stencil mask. This approach has the potential to: • 𝗘𝗹𝗶𝗺𝗶𝗻𝗮𝘁𝗲 𝗢𝗿𝗴𝗮𝗻𝗶𝗰 𝗥𝗲𝘀𝗶𝗱𝘂𝗲𝘀: By removing the need for resists, we can sidestep the primary source of the lossy defects identified in the McDermott paper.    • 𝗣𝗿𝗲𝘃𝗲𝗻𝘁 𝗦𝗵𝗮𝗱𝗼𝘄 𝗝𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀: A hard mask (>2um thickness) enables a selective deposition process without the "shadows" inherent to Dolan-style evaporation.    • 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗙𝗮𝗯𝗿𝗶𝗰𝗮𝘁𝗶𝗼𝗻: The entire qubit circuit could be fabricated in just two (U)HV evaporation steps with an in-situ oxidation in between, all without breaking vacuum.    Let's be clear: this technique is hard. Fabricating nanoscale stencil masks with the required precision is a significant challenge, as is the critical task of aligning them in-situ with the qubit chip. But the building blocks are there. Why are we not pursuing this or similar approaches more aggressively as a community? The potential upside - pristine interfaces and a dramatic simplification of the entire fabrication chain - is immense. 📸 Michaela Eichinger, 𝘗𝘩𝘋 𝘵𝘩𝘦𝘴𝘪𝘴 (𝘕𝘰𝘷𝘦𝘭 𝘮𝘦𝘵𝘩𝘰𝘥𝘴 𝘢𝘯𝘥 𝘮𝘢𝘵𝘦𝘳𝘪𝘢𝘭𝘴 𝘧𝘰𝘳 𝘴𝘶𝘱𝘦𝘳𝘤𝘰𝘯𝘥𝘶𝘤𝘵𝘪𝘯𝘨 𝘲𝘶𝘣𝘪𝘵𝘴 𝘢𝘯𝘥 𝘤𝘪𝘳𝘤𝘶𝘪𝘵𝘴)

I've been tackling the "barren plateaus" problem in QML, where training stalls inside vast search spaces. My latest experiment in fraud detection revealed a fascinating, counterintuitive solution. I discovered that increasing my quantum circuit's entanglement didn't smooth the path to a solution, but it created a more complex and rugged loss landscape (using a dressed quantum circuit scheme). Taking advantage of the hyvis library, I visualized this effect (thanks to the colleagues of JoS QUANTUM for putting this together), as shown in the first image of the post. The landscape evolves from a simple valley to a rich, expressive terrain (but potentially more complex for an optimizer). But did this complexity hurt performance? Usually that should be the case, but the exact opposite happened. The image shows the model with the most complex landscape (8 CNOTs by layer) not only learned faster (lower loss) but also achieved the highest accuracy (AUC) on the validation set and later in the test set. There is no free lunch on this. We can't generalize from these examples. This added complexity, or "expressivity," is precisely what allowed the model to find a superior solution in this case and avoid getting stuck, but it is not the norm. My biggest conclusion here It seems that for QML, the key to real-world performance isn't avoiding complexity, but leveraging it. To be able to extract permanent benefits, we should follow approaches like what Dra. Eva Andres Nuñez is researching by finding the way to use the extra complexity of entanglement to be able to find the global minima and not get stuck in our quantum optimization procedures using the theory behind SNNs. Here details about the hyvis library in GitHub: https://lnkd.in/dzqcFvDE An insightful paper from Eva about mixing SNNs and quantum: https://lnkd.in/dXDiuCBH Same subject from Jiechen Chen: https://lnkd.in/d-Uyngef #quantumcomputing #machinelearning #ai #datascience #frauddetection #ml #qml

Is Quantum-Selected Configuration Interaction (QSCI) really the near-term bridge between chemistry and quantum hardware—or just an expensive detour? QSCI’s promise 1 Prepare an approximate wavefunction on a quantum device. 2 Sample its amplitudes to identify the “important” Slater determinants. 3 Build a CI Hamiltonian from those determinants and diagonalise it classically. In principle the hybrid split sounds perfect for NISQ hardware: shallow circuits for sampling, heavy linear-algebra left to CPUs or GPUs. What the latest evidence says A new study, “Critical Limitations in Quantum-Selected Configuration Interaction Methods” (arXiv 2501.07231, June 2025), puts that optimism to the test. Using N₂ and a 2-Fe/2-S cluster, the authors show: • Sampling stalls— the quantum routine keeps drawing determinants already seen, so the CI space grows painfully slowly. • When sampling does add new determinants, the resulting expansion is typically larger than what classical selected-CI heuristics produce. • The final, dense CI matrix still has to be diagonalised on a classical machine, so any quantum speed-up must come entirely from step 2—yet that is where the inefficiency lives. A deeper question If you can guarantee that only a polynomial number of determinants matter in the chosen basis, classical selected-CI (CIPSI, HCI, SHCI, etc.) can already enumerate and diagonalise that set in polynomial–ish time and memory. The quantum sampler adds value only when the relevant determinant space is exponentially large and the sampler can discover its structure faster than classical importance-sampling schemes. Today’s evidence suggests we are not there yet. Implication for “Transform to Quantum” Before wiring a chemistry problem into qubits, we must ask whether the mapping actually off-loads a classically hard step. QSCI may still mature, but right now its quantum portion solves a problem that classical algorithms handle just as well—or better—once the determinant count is known to be modest. Take-away Transform-to-Quantum in our ITBQ framework isn’t about forcing every workflow to touch a QPU; it’s about reserving qubits for the steps that are truly intractable on classical hardware. QSCI, at least in its current form, might not clear that bar. Here the paper I have cited https://lnkd.in/e8acadAk

⚛️⁺ How do you individually control ions that are only a few microns apart without fighting alignment drift, bulky optics, or scalability limits? Our answer: 𝐋𝐞𝐭 𝐭𝐡𝐞 𝐩𝐡𝐨𝐭𝐨𝐧𝐢𝐜 𝐜𝐡𝐢𝐩 𝐝𝐨 𝐭𝐡𝐞 𝐛𝐞𝐚𝐦 𝐬𝐡𝐚𝐩𝐢𝐧𝐠 We propose a multimode, adjoint-optimized photonic circuit that enables reconfigurable, individual addressing of closely spaced trapped ions without relying on free-space optics. Key points: • Multimode (TE₀₀/TE₁₀) interference for programmable beam shaping • Diffraction-limited focusing at the ion plane (~2–4 μm spots at sub-100-μm height) • Crosstalk suppression down to -30 dB for single-ion addressing and -60 dB for dual-ion configurations • A scalable, foundry-compatible SiN platform integrated directly with surface-electrode ion traps Beyond addressing, higher-order modes open intriguing possibilities for spin–motion coupling, sideband control, and alternative gate schemes, pointing toward more compact and stable trapped-ion architectures as systems scale. Huge thanks to an outstanding collaboration across UC Irvine and the University of California, Berkeley, and especially to Melika Momenzadeh and other students who pushed the inverse design and multimode photonics to work in a very non-trivial regime. 📄 Paper: Individual trapped-ion addressing with adjoint-optimized multimode photonic circuits 👉 https://lnkd.in/gbtweCZd #QuantumComputing #TrappedIons #IntegratedPhotonics #Nanophotonics #InverseDesign #QuantumHardware