Quantum Computing Performance Considerations for Engineers

Fault-tolerant quantum computing depends on finding QEC codes that minimize overhead while maximizing logical performance, but the design space for codes is too large to explore exhaustively and solely through analytical means. What this work from IBM Research shows is that code discovery can be turned into a scalable, data-driven search problem. Using an LLM-guided evolutionary loop, candidate constructions are generated as programs and iteratively refined using feedback from decoding performance and exact verification. This allows systematic exploration of qLDPC code families that were previously only sparsely sampled, with 465 new candidates identified across different trade-off regimes. The below flow chart shows the different stages of the evolutionary framework, with each step narrowing the pool of candidate codes being considered. This can have practical impact on quantum error correction. First, it increases the probability of finding codes that reduce the physical qubit overhead required per logical qubit, which is a primary scaling bottleneck for fault tolerance. Second, it enables discovering codes that may be better matched to specific hardware constraints, such as connectivity and decoder latency, that may be difficult to satisfy using general-purpose constructions. Third, it accelerates evaluation of new architectures by providing a denser map of viable code options and their performance envelopes. Error correction is an evolving layer in the quantum stack. Codes, instructions, decoders, and hardware are co-optimized together, iterating quickly as device characteristics improve. The broader implication of this work is that we can hasten this evolution by effectively searching and validating codes in this space. Blog: https://lnkd.in/eN8Hs7tU Paper: https://lnkd.in/ebegYdMJ

Years ago, I had my first encounter with the QCage—back when it was just an alpha version. I was helping measure transmission values and testing its performance, and it was immediately clear: this wasn’t just another prototype. Up until then, I’d been working with in-house manufactured sample holders. They were simple and did an okay job but far from ideal—definitely not engineered for high qubit performance. But what is it that is required from a sample holder these days? For qubits to achieve high performance, every aspect of their environment needs to be optimized. A modern sample holder must: ➡️ Minimize signal loss: Efficient transmission lines and connections are essential to preserve microwave signal integrity, ensuring the qubit receives clean and precise control pulses. ➡️ Suppress electromagnetic interference (EMI): Proper shielding is critical to block external noise, which can significantly degrade qubit coherence times. ➡️ Provide thermal stability: A sample holder must minimize thermal gradients and maintain robust thermal anchoring since qubits are operated at millikelvin temperatures. ➡️ Offer scalability: As QPU sizes grow, sample holders need to handle more complex layouts while maintaining their high-performance characteristics. ➡️ Ensure compatibility with cryogenic environments: Materials and connectors must be carefully selected to withstand extreme cold without compromising electrical or mechanical properties. Fast forward to today, and it’s incredible to see how far we’ve come. The QCage didn’t just demonstrate potential—it sets a new bar for the industry. By addressing each of these requirements, it enables researchers to focus on science rather than struggling with hardware limitations. Labs like Mikko Möttönen’s, with their millisecond coherence times and high average T1, are proof of what’s possible when you meticulously optimize every aspect of qubit environments. Quantum Machines Aalto University Princeton University Søren Andresen Lars Damgaard Løjkner

One of the biggest misconceptions in quantum computing is that scaling is only about qubit count. It isn’t. The biggest bottleneck may ultimately be energy and infrastructure. AI is already forcing a redesign of data centre infrastructure around power and cooling. Quantum computing is heading toward the same reality. At small scale, the processor is the main focus. But once systems scale toward commercially useful workloads, the challenge becomes the total infrastructure required to support useful computation: • Cryogenic cooling requirements • Control electronics • Error correction overhead • Classical orchestration systems • Networking and interconnects • Pre- and post-processing infrastructure As systems scale, power, cooling, and deployability become critical constraints. Different quantum architectures handle those constraints very differently. Some approaches require increasingly large infrastructure footprints as they scale. Others aim to scale more like semiconductor computing historically has, by increasing qubit density on-chip. Ultimately, quantum computing will face the same commercial reality as every advanced computing platform: Can it deliver more value than it costs to operate? That question may ultimately determine which quantum architectures survive. We explore this in more detail in our latest Substack: https://lnkd.in/gGhacsdg

⚛️ Multi-GPU Quantum Circuit Simulation and the Impact of Network Performance 📜 As is intrinsic to the fundamental goal of quantum computing, classical simulation of quantum algorithms is notoriously demanding in resource requirements. Nonetheless, simulation is critical to the success of the field and a requirement for algorithm development and validation, as well as hardware design. GPU-acceleration has become standard practice for simulation, and due to the exponential scaling inherent in classical methods, multi-GPU simulation can be required to achieve representative system sizes. In this case, inter-GPU communications can bottleneck performance. In this work, we present the introduction of MPI into the QED-C Application-Oriented Benchmarks to facilitate benchmarking on HPC systems. We review the advances in interconnect technology and the APIs for multi-GPU communication. We benchmark using a variety of interconnect paths, including the recent NVIDIA Grace Blackwell NVL72 architecture that represents the first product to expand high-bandwidth GPU-specialized interconnects across multiple nodes. We show that while improvements to GPU architecture have led to speedups of over 4.5X across the last few generations of GPUs, advances in interconnect performance have had a larger impact with over 16X performance improvements in time to solution for multi-GPU simulations. ℹ️ Brown et al - 2025

> Sharing Resource < I like this one, not directly connected to QML, but on hardware effect for the quantum algorithm performance. "Investigation of Hardware Architecture Effects on Quantum Algorithm Performance: A Comparative Hardware Study" by Askar Oralkhan, Temirlan Zhaxalykov The authors compare Bell state preparation, GHZ state generation, Quantum Fourier Transform (QFT), Grover's Search, and the Quantum Approximate Optimization Algorithm (QAOA) on IonQ computers, IQM Quantum Computers, Rigetti Computing quantum computers and a state vector. Abstract: Cloud-accessible quantum processors enable direct execution of quantum algorithms on heterogeneous hardware platforms. Unlike classical systems, however, identical quantum circuits may exhibit substantially different behavior across devices due to architectural variations in qubit connectivity, gate fidelity, and coherence times. In this work, we systematically benchmark five representative quantum algorithms - Bell state preparation, GHZ state generation, Quantum Fourier Transform (QFT), Grover's Search, and the Quantum Approximate Optimization Algorithm (QAOA) - across trapped-ion, superconducting, and simulator backends using Amazon Braket. Performance metrics including fidelity, CHSH violation, success probability, circuit depth, and gate counts are evaluated. Our results demonstrate a strong dependence of algorithmic performance on hardware topology and noise characteristics. For example, 10-qubit GHZ states achieved fidelities above 0.8 on trapped-ion hardware, while superconducting platforms dropped below 0.15 due to routing overhead and accumulated two-qubit gate errors. These findings highlight the importance of hardware-aware algorithm selection and provide practical guidance for benchmarking in the NISQ era. Link: https://lnkd.in/eGjF3Z6D #quantumalgorithms #quantumcomputing #research #paper

Quantum Computing Modalities: Different Paths, One Big Prize 🏆 Quantum computing could revolutionize drug discovery, supply chains, finance, and materials. But quantum “modalities” (superconducting, trapped ions, photonics, and annealing) make it sound overly technical. These are simply different engineering ways to build the same powerful machine. Like rival EVs, battery vs. hydrogen vs. hybrid, each has pros/cons, all chasing the same goal. Main Modalities ⚛️ Superconducting (IBM, Google, Rigetti): Chip circuits at near absolute zero. Fast, scaling fast (IBM Heron 156 qubits, Google Willow 105 w/ error demos) ⚛️ Trapped ions (IonQ, Quantinuum): Atoms controlled by lasers. Top accuracy & coherence ⚛️ Photonics (PsiQuantum, Xanadu, Quandela): Light particles in optical networks. Easier scaling, fiber-tech friendly ⚛️ Annealing (D-Wave): Settles into optimal solutions—strong for routing/scheduling. 4,400+ qubits now Emerging Modalities ⚛️ Silicon spin (Intel, others): Builds on chip tech for easier scaling ⚛️ Topological (Microsoft): Aims for built-in error resistance ⚛️ Diamond defect: Room-temp potential for sensors + computing ⚛️ Neutral atoms (QuEra, Pasqal, Atom—1,000+ qubits), silicon spins Rare mentions include flying-electron qubits, quantum dots, or even hybrid systems combining modalities. Nuclear magnetic resonance (NMR) exists for small educational systems but isn't competitive for large-scale computing. Key Differences & Business Impact ⚛️ Speed vs. stability: Superconducting fast but noisy; ions slower but precise ⚛️ Scaling: Photonics/neutral atoms avoid extreme cooling; annealing delivers big systems today ⚛️ Sweet spots: Annealing wins optimization now (logistics, finance). Others target simulation (pharma, AI) As of 2026: Superconducting & trapped ion lead mid-scale reliability/error correction; photonics/atoms advance scaling. Goal: fault-tolerant systems (auto error correction at scale). Industry is in “fault-tolerant foundation era”, IBM eyes advantage by late 2026, full fault-tolerance by 2029. The Chase All target quantum advantage: solving key problems faster/cheaper/better than classical computers. Examples include faster drugs, optimized portfolios, efficient fleets, and better batteries/carbon capture. There are multiple paths because qubits are really tough. Competition speeds progress and the future will be specialized + hybrid, so not any one winner. Think more modalities = more shots on goal. The one (or combo) that hits fault-tolerant, useful scale first for your industry wins. Many experts predict a future of specialized + hybrid systems rather than one dominant hardware type. Bottom Line Don’t pick a “best” modality. Instead, ask which delivers value for your industry in 3–7 years? Cloud pilots (IBM, IonQ, D-Wave) give first-mover edge. Quantum shifts from hype to ROI, so track it, and test proofs-of-concept. Comment below or DM for deeper dive. #QuantumComputing #KPMGQuantum #Quantum

Most people learning quantum software start with circuits. That is useful. But for fault-tolerant quantum computing, I think one skill is becoming just as important: Resource Estimation. Because a quantum algorithm is not only a circuit. It is also a set of engineering tradeoffs: - How many logical qubits? - How many physical qubits? - How deep is the computation? - What error-correction assumptions are being made? - Which part of the workflow is actually the bottleneck? This matters because a small algorithm on paper can become a very large system-level problem once you ask what it takes to run reliably. That is the mental model shift. Quantum software is moving from: "Can I write the circuit?" to: "Can I understand what this circuit would cost at a fault-tolerant scale?" That is why tools for circuit design, simulation, and resource analysis matter. They help developers ask better questions before useful hardware is fully here. The future quantum developer may need to know not only gates and algorithms. They may also need to think like a systems engineer: - estimate resources - identify bottlenecks - compare architectures - understand error correction - connect algorithms to real-world constraints Hardware gets the headline. Resource estimation tells you whether the idea has a path to becoming useful. If you are learning quantum software today, do not stop at "how do I build this circuit?" Also ask: "What would it take to run this reliably?" That question is where quantum software starts becoming engineering. #QuantumComputing #QuantumSoftware #FaultTolerantQuantum #DeepTech

MIT researchers made a breakthrough in quantum computing by achieving the highest-ever accuracy (99.998%) for single-qubit operations using a type of qubit called fluxonium. This is a huge step toward making reliable quantum computers. They fixed common errors by using creative techniques like circularly polarized microwave drives and "commensurate pulses," which improved speed and accuracy. These methods reduced the need for error correction, making quantum computing more efficient. Fluxonium qubits are special because they are less affected by noise, thanks to a unique design. While they were slower in the past, the new techniques allowed them to perform faster and more accurate operations. This work shows that fluxonium qubits could be a strong option for building future quantum computers. The results also help reduce errors in quantum systems, bringing practical quantum computing closer to reality. These techniques could be applied to other types of qubits too, making this breakthrough widely useful. In short, this research shows how combining physics and engineering can overcome big challenges in quantum computing, paving the way for more advanced and reliable systems.

cuts quantum computer heat emissions by 10,000 times, offering a breakthrough in cooling and efficiency for next-generation machines. Heat is a major challenge in quantum computing, as excess energy disrupts qubits and causes errors. Reducing emissions is essential for scaling up powerful quantum systems. This device operates at extremely low temperatures, maintaining qubits in stable states while drastically minimizing unwanted thermal noise, allowing longer computations with higher accuracy. It could be launched as early as 2026, potentially revolutionizing how quantum computers are built, cooled, and deployed, making them more practical for real-world applications. Controlling heat at this scale reminds us that engineering solutions, combined with quantum science, are key to unlocking the full potential of quantum computing, enabling faster, more reliable, and energy-efficient machines. Thank YOU — Quantum Cookie The device is a cryogenic traveling-wave parametric amplifier (TWPA) made with specialized "quantum materials." Traditional amplifiers used for reading out qubit signals in superconducting quantum computers generate noticeable heat (even if small in absolute terms), which adds thermal noise, raises the cooling burden on dilution refrigerators, and limits how many qubits can be packed into one cryostat. Qubic's version reportedly cuts thermal output by a factor of 10,000, bringing it down to practically zero (on the order of 1–10 microwatts), while also reducing overall power consumption by about 50%. Why this matters for quantum computing - Heat is a core scaling bottleneck: Qubits (especially superconducting ones) must operate at millikelvin temperatures (~10–50 mK). Even tiny amounts of heat from readout electronics or control lines can cause decoherence, increase error rates, and require more powerful (and expensive) cryogenic systems. - The amplifier's role: It boosts the faint microwave signals from qubits without adding much noise. Conventional semiconductor-based amplifiers at cryogenic stages dissipate more heat; this new TWPA minimizes that, potentially allowing twice as many qubits per dilution refrigerator by easing the thermal load and simplifying cabling. - Potential impact: Lower cooling demands could cut operational costs and energy use significantly, making larger, more practical quantum systems feasible for real-world applications rather than just lab prototypes. Timeline and status The company has received grant funding and aims for commercialization/launch in 2026. As of early 2026 reports, development is ongoing with targets like 20 dB gain over a 4–12 GHz bandwidth. No major contradictions or retractions have appeared in credible coverage.