#QNT# Quantinuum surged more than 27% today, and I believe the biggest catalyst was its deal with Oracle. Here is how I view the deal. Personally, I sincerely hope this partnership succeeds and becomes the first clear example of meaningful synergy between quantum computing and HPC. If it does, it could become an important proof point for the entire industry and help drive the quantum ecosystem forward with much greater momentum. Having said that, there are still clear technical and commercial challenges that should not be overlooked. Helios can be offered to real customers. That has already been demonstrated. It is an existing cloud-accessible quantum system. But the next question is very different: Why should customers actually use Helios through OCI? So if the question is, “Can Quantinuum technically offer Helios as a service through OCI?” my answer is yes. But if the question is, “Will Oracle customers now have access to 48 fault-tolerant logical qubits operating at 99.999% fidelity?” my answer is much closer to no. At least, that is not what the currently published technical results demonstrate. In the 48-LQ QEC-cycle experiment, 5,000 shots were submitted for each basis. After the various selection stages, the final accepted samples were: X basis: 708 / 5,000 ≈ 14.2% Z basis: 958 / 5,000 ≈ 19.2% (attached pre-print link and the screenshot below) That is why I think we need to distinguish between validation of Quantinuum’s technology and commercial validation of its economic usefulness. Oracle putting Helios inside its data center → meaningful validation. Oracle putting Helios inside its data center → proof that Helios already has production workloads that outperform AI/HPC → not demonstrated yet. What matters next will be what happens after the OCI preview begins: paying customers, QPU utilization, bookings and revenue, repeat usage, and most importantly, which real workloads actually benefit from combining QPUs with GPUs/HPC. Once those numbers begin to emerge, we will have a much better idea whether the Oracle deal is primarily a strategic option on quantum computing, or a genuine commercialization inflection point that could take Quantinuum beyond the current AWS Braket/Azure Quantum model. There is another factor worth considering when looking at today's 27% move: QNT's market structure. Quantinuum has only recently gone public. It has roughly 261 million shares outstanding, but a float of only around 31 million shares. That distinction matters. The contrast with IonQ is striking. IonQ generated $80.1M of Q2 revenue versus Quantinuum's $8.0M — roughly 10× as much. The midpoint of their FY2026 revenue guidance is similarly about $285M vs. $30M, or roughly 9.5×. Yet their market capitalizations are now in roughly the same range, with Quantinuum at times valued even higher. On a very simple forward P/S basis, that works out to approximately: IonQ: ~55× Quantinuum: ~600× In other words, Quantinuum is currently receiving a revenue-multiple premium of roughly 10× over IonQ. Liquidity is also very different. QNT's normal daily trading volume has been only around 1.35–1.46 million shares, while IonQ trades with substantially greater liquidity. With such a small float, a combination of Oracle + earnings + QEC headlines can produce a very large price response. So I would be cautious about concluding that “the market has decided Quantinuum is worth more than IonQ.” With this kind of float and liquidity, the price can potentially be moved disproportionately by a relatively small portion of market participants. The numbers are rather extreme: IonQ generates roughly 10× Quantinuum's revenue, yet the two companies are trading at roughly comparable valuations. That does not necessarily mean the market is wrong about Quantinuum's technological potential. It does mean that I would be very careful about interpreting the current stock price as a clean measure of broad market consensus. I have a hard enough time predicting IonQ's sharp moves up and down. With a stock this lightly traded, I have even less confidence trying to predict QNT's price action — especially when so much of its valuation still depends on outcomes that remain well into the future.

The Uncomfortable Truth Behind Quantinuum’s Earnings and Its “Near Five-Nines” Claim. Disclaimer: This post is not intended to discredit Quantinuum or favor any particular company. My concern is simply that many investors make investment decisions—or repeat claims in discussions—without understanding the underlying facts and technical context. The purpose here is to look beyond the headlines and clarify what was actually demonstrated. Quantinuum has been at essentially the same physical-gate fidelity level for years. In 2023, its H-Series was already around 99.997% 1Q fidelity and roughly 99.8% 2Q fidelity. By late 2025, Helios reported 99.9975% 1Q and 99.921% 2Q. The improvement is real, but incremental. Helios’ published hardware benchmark reports those latter figures directly. The bigger headline has been logical qubits: Quantinuum has promoted 94 error-detected LQs (it doesn't mean the error is corrected) and 48 error-corrected LQs from just 98 physical qubits. Now it is advertising “near five-nines” logical fidelity. But the underlying result is about 99.996% logical QEC-cycle fidelity, and the experiment still uses post-selection—detected bad outcomes are excluded, and the fidelity is evaluated on the accepted shots. That is still a meaningful QEC result. But it is not the same thing as demonstrating 99.999% universal logical-gate fidelity, nor is it equivalent to 48 utility-scale fault-tolerant logical qubits. The headline sounds much stronger than what was actually demonstrated.

$IONQ EC (2) IonQ recognized $4.3M(Q2) and $11.3M(1H) from Chicago Uni. The 256-qubit system has now moved beyond the design stage and reached the fully integrated QPU prototype stage. The 10,000-qubit chip is already being developed in parallel, with tape-out work underway. SkyWater will remain a merchant foundry serving other quantum companies, rather than becoming a fab dedicated exclusively to IonQ. An upgrade pipeline is beginning to emerge in the field, with Tempo customers potentially progressing to 256-qubit and eventually 10,000-qubit systems.

My biggest takeaway from this announcement: Golden Dome’s $185 billion opportunity is not going to be a party reserved for a handful of traditional defense primes. The U.S. is explicitly building an open defense ecosystem designed to bring in commercial technology, startups, private capital, and nontraditional players — with multi-vendor competition and the prevention of vendor lock-in built into the strategy.

Of course, IonQ’s impact cannot be directly compared with the sheer scale and reach of NVIDIA’s GPU sales today. But within the quantum ecosystem, the picture looks very different. Classical computing evolved over many decades—from processors to memory, interconnects, networking, security, sensing, satellite infrastructure, and semiconductor foundries. The quantum industry is now compressing much of that long history into roughly a decade. That means building a great QPU alone is not enough to establish a durable competitive advantage. Computing, memory, photonic interconnects, networking, security, sensing and PNT, space infrastructure, semiconductor manufacturing, and advanced packaging must all advance together. That is why I describe IonQ as NVIDIA—with Mellanox—combined with Cisco, Palo Alto Networks, Honeywell, Maxar, and TSMC. More precisely, IonQ is attempting to combine an Intel-style IDM model—in-house chip design and manufacturing—with a TSMC-style external supply model. It intends to build its own quantum systems while also supplying foundry services, advanced packaging, photonics, networking, security, and sensing capabilities to the broader quantum ecosystem. That is what IonQ means by becoming a “merchant supplier.”

Wondering what IonQ’s recently completed acquisitions really mean? Think of it this way: imagine NVIDIA owning TSMC, Cisco, Palo Alto Networks, Honeywell, and Maxar in addition to Mellanox all under one roof. But this is about more than vertical integration. In this hypothetical, NVIDIA would own TSMC, yet TSMC would continue manufacturing chips designed by AMD and potentially other NVIDIA competitors. That is the essence of the merchant supplier model: IonQ owns the manufacturing and technology stack but does not intend to keep those capabilities captive for its own products. But do not compare this directly with today’s mature semiconductor industry. To fully appreciate what it could mean, rewind to the early 1990s—when the industry’s architecture was still taking shape, dominant platforms had yet to emerge, and the eventual winners were far from obvious. IonQ is positioning itself to build its own full-stack systems while supplying foundry services, advanced packaging, photonics, interconnects, networking, security, sensing, and space infrastructure to the broader quantum ecosystem including potentially other quantum platforms. IonQ does not simply want to compete in the quantum industry. It wants to supply the critical infrastructure on which the entire industry is built.

Quantum Networks with Atomic Memories The existing remote entanglement rate is roughly 20–250 events per second, and IonQ/Lightsynq has been working to push that toward 10,000 entanglement events per second(x50). Mihir Bhaskar also said in April that they hoped to show results within this year, so I would not be surprised if we see something around Investor Day or shortly afterward. The reason 10,000/s is such an interesting target is that it begins to approach the time scale of entangling operations performed inside a local trapped-ion QPU. In other words, the goal is not simply to make remote entanglement “faster.” It is to bring the speed of QPU-to-QPU entanglement into roughly the same regime as entanglement inside a QPU. That has a much bigger architectural implication. If local and remote entanglement can eventually operate on comparable time scales, then the physical boundary between “inside the QPU” and “between QPUs” becomes much less important from the perspective of the system architecture. A modular quantum computer starts to look less like a collection of separate processors connected by a slow network and more like a distributed compute fabric. This is also why Monroe keeps emphasizing Photon + Memory. The point connects directly with his argument in the second lecture about the limitations of an all-photonic computer without memory. Photons are excellent flying qubits for moving quantum information, but they are poor at waiting. Once probabilistic processes and feed-forward enter the system, some form of stationary quantum memory becomes essential. In the Lightsynq architecture, memory changes the role of the photonic link. Instead of requiring multiple probabilistic photon events to succeed simultaneously, the first successful event can be stored while the system waits for the next one. The network therefore becomes asynchronous and buffered rather than entirely dependent on simultaneous probabilistic events. This makes the roles of Oxford Ionics and Lightsynq look increasingly complementary. Oxford Ionics addresses local scale-up: how to build larger, high-fidelity trapped-ion QPU modules using microwave control, ion shuttling, QCCD-style architectures, and increasingly sophisticated local control. Lightsynq addresses global scale-out: how to connect those QPU modules through photonic links without allowing probabilistic entanglement generation to become the dominant system bottleneck. The recent three-node GHZ experiment fits naturally between those two layers. It demonstrated that independent quantum-memory nodes can in fact be connected through photons and made to share genuine multipartite entanglement. But Monroe’s lecture also makes clear that directly extending that same (P^3) approach to ever larger numbers of nodes is not the intended scaling path. The next challenge is to make the underlying photonic links fast enough, buffered enough, and reconfigurable enough to support a real modular computing architecture. Put together, Monroe’s three lectures suggest a remarkably coherent picture: Building better atomic qubits is not enough. Within a QPU, scaling comes from better control, shuttling, and local connectivity. Between QPUs, scaling comes from photons. Because those photonic processes are probabilistic, quantum memory is needed to buffer successful events, and optical switching is needed to route entanglement wherever the computation requires it. So, my interpretation after putting the three lectures together is that IonQ → Oxford Ionics → Lightsynq → photonic modular architecture should not be viewed as a collection of unrelated technologies or acquisitions. They increasingly look like different layers of the same modular, scalable quantum-computing architecture that Monroe has been advocating for many years. Oxford Ionics strengthens the local scale-up layer. Lightsynq strengthens the global scale-out layer. Photonic interconnects connect the modules. Quantum memory makes those probabilistic links usable at scale. And if remote entanglement really can be pushed toward the same time scale as local entangling gates, that would be a particularly important transition: the network would stop behaving like a slow peripheral connection between QPUs and start becoming part of the quantum computer itself.

Quantum Computing in Academia and Industry Monroe’s broader thesis is that atoms are already close to ideal quantum hardware, so the remaining challenge is increasingly one of engineering rather than discovering fundamentally new qubit physics. In his view, a large-scale quantum computer is therefore unlikely to emerge simply by making one monolithic processor larger and larger. A more plausible path is to build high-performance atomic QPUs, scale them locally through better control and ion transport, and then connect those modules through photonic links. Seen through that lens, the lecture draws a surprisingly natural line from IonQ’s origins to Oxford Ionics, Lightsynq, and ultimately a photonic modular architecture. Monroe explicitly says that IonQ’s genesis came from a paper on modular quantum computer architecture. In other words, IonQ was not founded merely around the idea of building a better ion trap. From the beginning, the ambition was to commercialize a quantum computing architecture that could continue scaling after any single physical module eventually reached its practical limits. That makes the Oxford Ionics acquisition especially interesting. Monroe describes Oxford’s combination of microwave gates and ion shuttling as a highly compelling architecture. From this perspective, Oxford Ionics does not simply add another trapped-ion technology to IonQ’s portfolio. It appears to fill an important gap in IonQ’s original scaling thesis: how to scale efficiently within a local QPU before moving to inter-module networking. The hierarchy Monroe describes can therefore be viewed in two layers: Local scaling: Qubits → better control → QCCD / ion shuttling → qudits → larger and higher-fidelity QPU modules Global scaling: QPU modules → communication qubits → photonic interconnect → modular quantum data center The second layer is where quantum networking, remote entanglement, and quantum memory become critical. Photons are excellent carriers of quantum information because they can travel long distances through optical networks, but they also present a fundamental architectural problem: photons move extremely fast and do not naturally stay where you need them. If entanglement generation is probabilistic, one successful link may need to wait while another link is retried. Without a memory, there is nowhere to hold that quantum state. That is why Monroe repeatedly comes back to the need for Photon + Memory rather than photonics alone. In this architecture, the photon acts as the flying qubit that connects physically separated QPUs, while a quantum memory stores the successfully generated state, allowing the network to operate asynchronously. This is precisely where Lightsynq fits. Its role is not simply to improve photon collection, but to provide the memory and interface layer needed to make photonic interconnects practical enough for modular quantum computing. So the larger picture is quite coherent: Oxford Ionics helps solve local scale-up. Lightsynq helps solve global scale-out. Photonic interconnects tie the QPU modules together. Put together, Monroe’s vision looks less like a single enormous quantum processor and more like a quantum data center built from high-fidelity atomic QPUs, ion shuttling, communication qubits, quantum memories, and a reconfigurable photonic fabric. What makes the lecture particularly interesting is that these pieces do not feel like disconnected acquisitions or research programs. They can be read as successive layers of the same scaling architecture that Monroe says was already embedded in IonQ’s original founding thesis. Continued in Part 3 of the lecture.

Quantum Computers for the Future (and for Cocktail Parties) Some of the most interesting remarks from Christopher Monroe’s first lecture: We still do not really know what quantum computers will ultimately be most useful for. - His point was that this is a fundamentally new computing paradigm, and we may need to build and use these machines before we fully understand where their greatest value lies. The real power of a quantum algorithm is not simply having 2^n states in superposition. It is interference. - A quantum computer has to engineer constructive and destructive interference so that unwanted answers cancel out while useful answers are amplified. Industry does not necessarily care whether a heuristic is mathematically proven. It cares whether it works. - Monroe contrasted academia’s preference for rigorous proofs with industry’s much more pragmatic standard: if a heuristic produces a better solution and creates economic value, that can be enough. He was remarkably blunt about Big Tech’s approaches to quantum computing, particularly Microsoft’s topological-qubit program. On topological qubits, he said: “This is the string theory of quantum computing. Topological qubits. It’s beautiful physics. It’s never been conclusively shown that it exists. It’s a great idea. It’s wonderful mathematics, wonderful condensed matter theory. But for a big company to go in that direction is very strange because I don’t think they know what they’re doing.” He was similarly dismissive of some of the other large tech companies. On Amazon’s quantum chip, he joked: “They call it a quantum chip. I have no idea what that is. I don’t think they do either.” And in the Q&A, his criticism of brute-force superconducting scaling was even stronger: “IBM and Google are just throwing them on the chip and declaring victory. They’re not going to scale.” Importantly, these are Monroe’s personal technical views, not an industry consensus. His broader argument is that synthetic solid-state qubits may still require major physics breakthroughs, whereas natural atomic systems already provide highly uniform qubits and shift much of the remaining challenge toward control and engineering. His view of a meaningful quantum-computer metric is effectively closer to Qubit count × Fidelity × Circuit depth, rather than qubit count alone. A machine with thousands of qubits but only a handful of reliable operations is no more compelling than a tiny machine with perfect gates. The qubits must be numerous enough, the gates accurate enough, and the circuit deep enough to create useful large-scale entanglement. Scaling is technically possible, but it is enormously expensive. What can truly unleash that scaling is a commercial use case. Monroe put it very clearly: “When that happens, then the floodgates will open and we will see scalable machines.” Personally(4FIRE), I think those floodgates may already be starting to open. What I found particularly striking is how consistent Monroe’s argument is: the bottleneck is shifting from proving that quantum mechanics works to engineering systems that can scale economically—and once real commercial value appears, capital and industrial capacity can accelerate that transition dramatically. Continued in Part 2 of the lecture. Have a great weekend!

Thanks to a recommendation from a member of our community, I happened to come across Professor Chris Monroe’s MCQST Distinguished Lecture and only today realized that he had been selected as an MCQST Distinguished Lecturer. I went through all three YouTube videos, each over an hour long, and there are some genuinely important points buried in them. I’m putting together the key takeaways now and will share them with everyone. For context, this is a highly prestigious lecture series — previous MCQST Distinguished Lecturers include John Preskill and Mikhail Lukin.

Below is a conceptual diagram of #HydRON# (High thRoughput Optical Network), the ultra-high-speed satellite network led by #ESA#. Its architecture looks remarkably similar to that of Skyloom. For HydRON Element #2#, the Franco-Italian space company Thales Alenia Space is providing the on-board packet router, while Officina Stellare, together with the German Aerospace Center, is developing both fixed and mobile optical ground stations. In 2024, Skyloom Global Corp. announced a collaboration with NEC Corporation. At the time, Skyloom’s CTO and co-founder Santiago Tempone said: “We look forward to working closely with NEC's digital coherent optical communication experts and delivering a discriminating optical communications product to the marketplace.” If you’re wondering what coherent means in this context, I recommend the five-part thread below: On October 1, 2025, Officina Stellare and Skyloom Global Corp. USA announced the signing of a technology and license agreement and a teaming agreement for the Skyloom Europe project. Here is the article shared earlier by @Rick101284 regarding the cooperation between Skyloom and Officina: "Skyloom Europe." This initiative will involve (i) the establishment of a #NewCo# in Italy, entirely owned by Officina Stellare, and (ii) the creation of a high-capacity production facility to meet the growing and evolving global market demand for optical communications, which is estimated to be worth approximately 12 billion euros. Just one month later, in November, IonQ officially announced the acquisition of Skyloom. (The deal was completed two months later in January 2026.) In the same month, IonQ also announced the establishment of IonQ Italy. And since then… silence. Even more interesting, just two days ago, Mynaric announced it had been selected for HydRON Element #3#. But Skyloom (now part of IonQ)? Still quiet. Very quiet. Almost too quiet. Surely it’s not dead. Should we go wake it up? 😄 Feels like it might be time. For now… I’ll keep watching.