Managing Performance Trade-Offs in Quantum Code Design

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

New preprint out today with my PhD student Marvin Richter, together with Ingrid Strandberg and Simone Gasparinetti of the 202Q-lab, all at WACQT - Wallenberg Centre for Quantum Technology at Chalmers tekniska högskola: ”Overhead in quantum circuits with time-multiplexed qubit control” https://lnkd.in/d8tVVxjA We analyse an important scaling challenge for quantum computers. It would be good to reduce the number of control lines going into the fridge hosting superconducting qubits, to reduce cooling requirements and the amount of electronics. But doing so risks quantum algorithms taking longer to execute and thus becoming more affected by noise, since fewer qubits can be controlled in parallel with fewer control lines. We quantify this trade-off and find it to be surprisingly benign. We show that couplers for two-qubit gates can be grouped on common drive lines without any overhead up to a limit set by the connectivity of the qubits. For single-qubit gates, we find that the serialization overhead generally scales only logarithmically in the number of qubits sharing a drive line. We are able to explain this finding using queueing theory. These results are promising for the continued progress towards large-scale quantum computers. The number of control lines in a quantum computer can be significantly reduced without introducing much overhead in execution time for quantum algorithms.

⚛️ Illustration of Barren Plateaus in Quantum Computing 📜 Variational Quantum Circuits (VQCs) have emerged as a promising paradigm for quantum machine learning in the NISQ era. While parameter sharing in VQCs can reduce the parameter space dimensionality and potentially mitigate the barren plateau phenomenon, it introduces a complex trade-off that has been largely overlooked. This paper investigates how parameter sharing, despite creating better global optima with fewer parameters, fundamentally alters the optimization landscape through deceptive gradients—regions where gradient information exists but systematically misleads optimizers away from global optima. Through systematic experimental analysis, we demonstrate that increasing degrees of parameter sharing generate more complex solution landscapes with heightened gradient magnitudes and measurably higher deceptiveness ratios. Our findings reveal that traditional gradient-based optimizers (Adam, SGD) show progressively degraded convergence as parameter sharing increases, with performance heavily dependent on hyperparameter selection. We introduce a novel gradient deceptiveness detection algorithm and a quantitative framework for measuring optimization difficulty in quantum circuits, establishing that while parameter sharing can improve circuit expressivity by orders of magnitude, this comes at the cost of significantly increased landscape deceptiveness. These insights provide important considerations for quantum circuit design in practical applications, highlighting the fundamental mismatch between classical optimization strategies and quantum parameter landscapes shaped by parameter sharing. ℹ️ Stenzel et al - LMU Munich, Germany - 2026

Meta-optimization of resources on quantum computers “combination of classical and quantum approaches leads to many interesting challenges. The accuracy and reliability of the final output is inevitably limited by a combination of different factors. First, the classical optimization algorithm is not guaranteed to converge to the true minimum. This is because many optimization algorithms are stochastic by nature and they use randomness at least during initialization to choose the starting point. In many scenarios, randomness is required in each iteration. This randomness ensures that the optimization procedures can deal with a large family of different functions without getting stuck in a local minimum, but naturally also leads to stochasticity of the whole calculation, i.e. the algorithms are not guaranteed to succeed. Thus for obtaining a useful result with high probability, the procedure needs to be repeated several times.” “… even in the noiseless quantum processor scenario the outcome of any useful quantum computation is stochastic. Typically, the outcome is a non-computational basis state (otherwise the computation would be classically efficiently simulable) and is characterized by frequencies of outcomes for different measurement settings. A single run of a quantum computer only provides a single snapshot of the state for one measurement setting. Any optimization procedure therefore inevitably comes with a trade-off between a more precise measurement on a single position in the parameter-space and less precise measurements of many positions. It is non-trivial to decide which of these two strategies leads to better results.” By Ijaz Ahamed Mohammad, Matej Pivoluska & Martin Plesch Link https://lnkd.in/d4Rs7KAh

New work from a Harvard team highlights a major bottleneck in fault-tolerant quantum computing: the classical decoder used in quantum error correction. Quick primer on QEC: 1. Encode: A logical qubit is spread across many physical qubits, so no single error destroys the information. 2. Detect: Stabilizer measurements run repeatedly. They do not reveal the quantum state, but they do flag when something has gone wrong. The pattern of those flags is called the syndrome. 3. Decode: A classical computer reads the syndrome and infers which error most likely occurred. 4. Correct: The correction is applied, and the logical qubit survives. Step 3 is where things get hard. For quantum LDPC codes, one of the most promising routes to efficient fault tolerance, practical decoders have usually forced a tradeoff between speed and accuracy: the fast ones are too weak, and the accurate ones are too slow for real-time use. This paper introduces Cascade, a geometry-aware convolutional neural decoder. The key idea is not just “use a neural network,” but to build the structure of the code directly into the model: locality, translation equivariance, and anisotropy. That makes this feel less like generic ML and more like architecture co-design. Some of the headline results: - On the [[144, 12, 12]] Gross code, Cascade achieves logical error rates up to 17x lower than prior practical decoders, with 3–5 orders of magnitude higher throughput - It reveals a “waterfall” regime in which logical errors fall much faster than standard distance-based formulas would suggest, largely because earlier decoders were not strong enough to expose it - In one surface code example, that translates to roughly 40% fewer physical qubits to reach a target logical error rate of 10^-9 - Its confidence estimates are well calibrated, which enables post-selection. In one setting on the [[72, 12, 6]] code, that implies roughly 20x fewer retries for repeat-until-success protocols such as magic state distillation - Current GPU latencies already fit the timing budgets for trapped-ion and neutral-atom platforms. Superconducting qubits still require a tighter ~1 microsecond budget, with FPGA and ASIC paths supported by the hardware estimates in the supplement The broader takeaway: decoder quality is not just an implementation detail. It directly shapes how many qubits and how much time fault-tolerant quantum computing actually requires, and those costs may be meaningfully lower than standard estimates assume. Paper: https://lnkd.in/g9D82Ry8