The Pools at the Bottom

Pic courtesy of fws.gov

Tim and I were friends in elementary school and through high school. We fished and we hiked all over the Western Massachusetts  Berkshires. We specialized in finding waterfalls and the pools at the bottom that held brook trout. Small but beautiful. Tim always caught more than me. We almost always let them go, careful with how we handled them. He’s also the one who turned me on to Hemingway, including Big Two-Hearted River, both parts. A man goes fishing alone, and the whole story is what he doesn’t say.

High school ended. We went to different colleges. Jobs came, then families, and life pulled us in different directions. Forty years went by before we found our way back to each other.

This kind of drift happens to almost everyone. Nobody decides to lose a friend. It happens in small pieces: you mean to call, you don’t call, the next week comes and you don’t call then either. The years pass like that, fast, faster than you expect, until you realize you don’t actually know how someone you once knew well is doing anymore.

Judy, Tim’s wife, died this past April. Since then there have been a couple of text messages, nothing more. I haven’t picked up the phone. I never met his two sons. Forty years apart is enough time to miss entire chapters of somebody’s life.

What I do remember is Bub’s BBQ in Sunderland, where we started getting together about eight years ago, every fall: Tim and Judy, Diane and me. A picnic table, plates of barbecue, a few beers, old stories, a lot of laughs. You could see how much Tim and Judy still loved each other just in how they sat together. Bub’s closed this year after almost fifty years in business. That’s the kind of detail that tells you how much time had actually gone by without me noticing.

Time moves faster than the people living in it ever plan for. Forty years can pass between two friends who never had a falling out, never had one bad conversation, and just stopped calling. If there’s someone you’ve been meaning to call, calling closes the gap. Waiting only grows it.

Diane and I will be at the celebration of Judy’s life next month. After that, I’m hoping we get back out fishing. He’ll probably still catch more brookies than me. I don’t think I’ve got forty years left to find out.

Authentication, Not Encryption, Is Now The Big Quantum Worry

In the chapter on the threat to encryption in Quantum from the Ground Up, I cited a 2025 estimate from Craig Gidney at Google Quantum AI that RSA-2048 could be factored by a quantum computer with roughly one million noisy physical qubits, and noted that most experts placed Q-Day, the point at which a quantum computer can break current encryption, at around 2035. That estimate did not survive the spring.

In late March and early April 2026, two independent research results arrived within weeks of each other. Google published a major improvement to the quantum algorithm used to break elliptic curve cryptography, the math behind most of the internet's key exchange. Then Oratomic, a quantum computing startup, published a resource estimate suggesting RSA-2048 and P-256 could be broken with as few as 10,000 qubits on a neutral atom machine. That is roughly two orders of magnitude below the prior million-qubit estimate.

Why 10,000 Qubits Instead of a Million

The efficiency gain comes from error correction overhead, not from a change in the underlying mathematics. Shor's algorithm, the quantum algorithm that breaks RSA and ECC, has not changed. What changed is how many physical qubits it takes to build one reliable logical qubit on a neutral atom platform. Reporting on the Oratomic estimate put the ratio at roughly three to four physical qubits per logical qubit, a dramatically smaller overhead than superconducting platforms have required.

That ratio matters because it is the number that determines whether an attack is a research curiosity or an engineering project with a budget and a timeline. A million-qubit machine is not something any organization is building in this decade. A 10,000-qubit machine is in the range that QuEra, Atom Computing, and Pasqal have all stated as roadmap targets for 2026 to 2028 in the neutral atom chapters of this book. The number did not just get smaller. It got small enough to be plausible on hardware roadmaps that already exist.

How the Industry Responded

Cloudflare's response is the clearest signal of how seriously the industry is taking this. Cloudflare secures a significant fraction of global internet traffic, and the company had already completed most of its post-quantum encryption rollout, protecting against harvest-now-decrypt-later attacks where adversaries collect encrypted traffic today and decrypt it once a quantum computer is available. That work was largely done. The Google and Oratomic results changed what Cloudflare is worried about next.

Cloudflare's senior product director told reporters that authentication, not data confidentiality, is now the bigger concern. The distinction is worth sitting with. If a quantum computer can forge access credentials, an attacker does not need to decrypt anything. They can log into systems they are not supposed to have access to, or compromise a software update channel directly. That is a more immediate and more damaging failure mode than data theft, and it requires a different and more complex migration than swapping out an encryption algorithm.

Cloudflare set 2029 as its target for full post-quantum security across its platform, including authentication, and has accelerated its existing roadmap to hit that date. Google made a similar 2029 commitment. Coverage of the shift described the timeline change as a direct response to the two breakthroughs, and one report quoted the view that a coordinated attack against a high-value target, what the article called a moonshot attack, could plausibly arrive by 2030.

What This Changes in the Book

Chapter 13 of the first edition gave a 2029 to 2033 range as the accelerated estimate, driven by IonQ's October 2025 fidelity result. That range still holds, but the reasoning behind it has shifted. The IonQ result was a hardware fidelity improvement on an existing approach. The Google and Oratomic results are an algorithmic and architectural efficiency gain that reduces the qubit count needed for the same outcome. Both push in the same direction, but they are different kinds of progress, and the second edition will need to explain both rather than treating the timeline as a single number that moves around.

The Oratomic estimate specifically relies on high-rate quantum error-correction codes to achieve its 3:1 physical-to-logical overhead ratio. It is worth emphasizing that we are talking about 10,000 physical qubits here. Because the cybersecurity industry has spent a decade hearing that breaking RSA requires millions of physical qubits, highlighting that this breakthrough brings the physical hardware requirements down to the low thousands underscores just how radical this architectural shift is. Furthermore, this timeline collapse wasn't driven by hardware breakthroughs alone; as noted in the landmark paper co-published by Caltech and Oratomic, the team heavily leveraged AI-assisted algorithmic design and reconfigurable arrays to optimize these error-correcting codes, proving that classical AI is actively accelerating the software running on quantum roadmaps. Meanwhile, Google Quantum AI's joint research demonstrated that the logical qubit threshold to break elliptic curve cryptography could be slashed by 20x, creating a compounding effect where hardware requirements dropped while algorithm efficiencies spiked.

The organizational guidance from Chapter 13 does not change, it gets more urgent. Inventory your cryptographic dependencies. Specify quantum-resistant encryption in procurement. Re-encrypt long-term sensitive data. Build crypto agility into your architecture so algorithms can be swapped without a system rebuild.

The new addition, following Cloudflare's formal roadmap acceleration announcement, is that authentication systems deserve the same attention as encrypted data. As industry infrastructure leaders begin locking down their platforms against these newly compressed timelines, a compromised root certificate or access token is recognized as a far more catastrophic failure than a decrypted file, and it is the exact vulnerability the industry is now scrambling to patch.

This post will be incorporated into the second edition of Quantum from the Ground Up, out September 1. The full book, updated quarterly, is free at gordostuff.com/p/quantum-from-ground-up-hardware.html

The Classical Engine Inside Every Quantum Computer

Before writing this, I realized NVIDIA Ising already showed up in Chapter 12 of the book, the one on AI-assisted calibration and error correction. That chapter only had NVIDIA's side of it. A few days after that post went up, IEEE Spectrum ran a piece by Edd Gent that talked to Q-CTRL, Riverlane, IBM, and Google about the same problem, and the picture got more interesting. This post is what Chapter 12 looks like with that reporting folded in.

I have spent the past several months writing about quantum computing. Topological qubits, neutral atoms, silicon spin, photonics. Each one has its own physics, its own failure modes, its own argument for why it will win. Putting the book together, I noticed something missing from every chapter: what runs the thing once you turn it on.

The answer is classical computers. A lot of them. A recent IEEE Spectrum piece by Edd Gent lays out why, and it changes how I think about the supply chain side of this industry too.

Qubits are not reliable the way transistors are. A transistor comes off the line and works, billions of times, with no babysitting. A qubit drifts. It decoheres. It needs constant tuning just to stay usable, and that tuning happens in two stages. The first, called “bring up,” determines the frequency each qubit resonates at, how long it holds its quantum state, how sensitive it is to control pulses, and how strongly it interacts with its neighbors. Every one of those numbers affects error rates and how the qubit responds to control signals.

Done by hand, bring up still requires someone with a PhD and can take days or weeks, according to Jay Guilmart, lead product manager at Q-CTRL. The process resists scripting because each step depends on the result of the one before it. So Q-CTRL built software that examines each measurement, diagnoses what failed, and decides whether to proceed, repeat, or back up, rather than running a fixed sequence. Calibration does not end once a machine is running either. Parameters drift over time, so the system needs “runtime recalibration” to nudge things back into spec. But every cycle spent recalibrating is a cycle not spent running a circuit. As Guilmart puts it, if recalibration eats your uptime, the high fidelity you maintained is worthless.

Then there is error correction. Quantum information gets spread across many physical qubits to form a “logical qubit,” so that errors in individual qubits can be caught and fixed without destroying the encoded information. Measuring a qubit directly collapses its state, so error detection works indirectly through “parity checks,” which compare pairs of qubits to see if they agree. The pattern of agreements and disagreements is called a syndrome, and classical algorithms called decoders read that syndrome to locate errors.

This has to happen fast. Superconducting and silicon spin qubits hold their state for only microseconds to milliseconds, so decoding has to finish inside that window or the algorithm stalls. That rules out general-purpose processors. Decoders run on FPGAs or ASICs, chips built specifically for speed, according to Jerry Chow, CTO of quantum-centric supercomputing at IBM.

This is where the AI argument gets interesting, and where the field splits. In April, Nvidia released two models aimed at this problem. One uses a vision-language model to read calibration plots and feed an AI agent that decides how to adjust the processor. The other uses a convolutional neural network to catch the simple, localized errors that make up most faults, passing only the harder cases to a traditional decoder, for roughly a 2x speedup. Sam Stanwyck, Nvidia's director of quantum product, makes the case for AI on inference speed: models take time to train, but once trained they run fast and parallelize across chips as qubit counts grow.

Marco Ghibaudi, VP of engineering at Riverlane, pushes back on a different axis. Running anything on a GPU adds latency, even with massive throughput. His framing: you can have a very fat pipe, but if it is also a very long pipe, the data still arrives late. Riverlane's approach has been to shorten the pipe itself and make every stage faster, rather than chase raw throughput.

IBM's Chow lands in a similar place on calibration. He thinks AI shows real promise for understanding new architectures or unfamiliar circuit types, where you do not yet know what you are looking for. But for a well-characterized device where you are hunting small deviations from a known baseline, simpler physics-informed methods are cheaper and faster. Google's Adam Zalcman frames the two approaches as complementary rather than competing: neural networks are good at finding hidden patterns in syndrome data that handwritten decoders miss, so Google is building architectures that can run both, including its AlphaQubit 2 model.

Andi Gu, a Harvard PhD student working on AI decoders, takes the long view. He expects the “bitter lesson” to apply here eventually, the same pattern that played out in other parts of AI: general learning methods, given enough data and a large enough model, outperform hand-built algorithms over time. The obstacle right now is latency. Gu's group is working on shrinking AI decoders enough to fit on an FPGA, though smaller models trade off some accuracy, and finding that balance is still unresolved.

What stands out reading this against the QSCA work is how much of the “quantum workforce” conversation undersells this layer. When people talk about quantum jobs, they tend to mean physicists and people who understand qubits. But calibration software, decoder design, FPGA and ASIC engineering, and the AI models being built to assist both are squarely classical computing jobs. Guilmart's own warning, that calibration overhead will “blow up” past a thousand qubits and that current techniques will not scale, is as much a hiring problem as a technical one. Nobody in the article claims to have this solved, and Guilmart says plainly that no one is winning this battle today.

Every qubit type I have written about, no matter how exotic, sits on top of a rack of classical silicon doing the unglamorous work of keeping it alive. 

*****

Download Quantum from the Ground Up, a free PDF now available at gordostuff.com. This first edition covers posts through June 2026 and runs 50 pages across 19 chapters. It is written for someone entering the quantum workforce, or seriously considering it, who has a technical background but has not taken a graduate course in quantum mechanics.

Quantum from the Ground Up: First Edition Now Available

I have been learning and writing about quantum computing on gordostuff.com since October 2025. The posts started as a way to make sense of announcements as they happened: IonQ hitting 99.99% two-qubit gate fidelity, Microsoft unveiling Majorana 2, IBM running a 12,635-atom protein simulation, Apple releasing a 50,000-step formal proof of its post-quantum cryptographic library. Each one went up as a standalone post. After eight months and seventeen posts, it made more sense to put them together in one place.

The result is Quantum from the Ground Up, a free PDF now available at gordostuff.com. This first edition covers posts through June 2026 and runs 50 pages across 19 chapters. It is written for someone entering the quantum workforce, or seriously considering it, who has a technical background but has not taken a graduate course in quantum mechanics.

Download: Download PDF

The book covers six qubit fabrication platforms in sequence: superconducting circuits, trapped ions, photonics, neutral atoms, silicon spin qubits, and topological qubits. Each chapter describes the physical mechanism, the fabrication or trapping method, current performance numbers, and the specific engineering problems that remain unsolved. The qubit chapters are followed by sections on hybrid classical-quantum protein simulation, AI-assisted hardware calibration, post-quantum cryptography, and the hardware landscape as it stands in mid-2026.

Two chapters address the workforce directly. The first maps the adjacent skill sets the quantum supply chain actually needs: semiconductor process engineering, cryogenic systems operation, RF electronics, machine learning for hardware calibration, and cryptographic implementation. None of those require a physics PhD. The second chapter maps the full degree pathway from a two-year associate degree through a PhD, with specific programs, what each credential prepares you to do, and salary ranges at each level. The field has a real workforce shortage. The shortage is not only at the PhD tier.

The plan is to update the book quarterly as new posts are published. The field moves fast enough that a quarterly cycle makes sense: slow enough to let any given announcement settle, fast enough to stay current with actual engineering progress. This edition covers October 2025 through June 2026. The next update will incorporate posts from Q3 2026.

Seventeen posts, fifty pages, fifteen images, one dark navy cover. The posts were worth writing individually. They are more useful together. Download free at Download PDF.

Support This Work

The book is free and will stay free. If you find it useful and want to support future editions, you can contribute at ko-fi.com/gordostuff.

Topological Qubits: A Different Way to Build a Quantum Computer

Earlier posts here covered five qubit platforms: superconducting, trapped ion, photonic, neutral atom, and silicon spin. Each one answers the same question differently: how do you isolate a quantum system long enough to do useful computation? Topological qubits are the sixth approach, and the most contested. They exist at the intersection of materials science, condensed matter physics, and a twenty-year bet by Microsoft that the rest of the field is solving the wrong problem.

Every qubit in a quantum computer is fragile. Superconducting qubits, the kind Google and IBM use, operate near absolute zero and still lose coherence in microseconds. Gate error rates run between 0.1% and 1%, which sounds small until you consider that a useful fault-tolerant quantum computer may need error rates below one in a million. The industry has spent years stacking error correction on top of error correction, adding physical qubits to protect logical ones.

Topological qubits take a different approach. Instead of fighting noise with redundancy, they aim to make the qubit itself resistant to local disturbances. The physics relies on Majorana zero modes, exotic quasiparticles that store quantum information non-locally across two spatially separated points. Because the information is spread out, a local disturbance at one point cannot corrupt the qubit on its own. Topology, the branch of math that describes properties preserved under continuous deformation, gives the qubit its protection. You would have to disturb both ends of the system simultaneously to flip the state, which is far less likely than a single-point noise event.

Microsoft has pursued this path for nearly two decades through its Station Q research group. In February 2025, the company announced Majorana 1, described as the world's first quantum processor built on a topological core architecture. The chip used indium arsenide and aluminum, a hybrid superconductor-semiconductor platform. The research appeared in Nature in February 2025. The announcement drew immediate scrutiny. Independent researchers questioned the Topological Gap Protocol Microsoft uses to confirm the presence of Majorana modes, a University of St Andrews physicist published a challenge to its validity in March 2025, and Scientific American noted that Microsoft had previously retracted a high-profile Nature paper in 2021 after outside experts found the data could have come from material imperfections rather than topological qubits.

On June 2, 2026, Microsoft announced Majorana 2 at its Build 2026 conference. The new chip replaces aluminum with lead in the superconducting material stack and redesigns the semiconductor structure. Microsoft reports a mean qubit lifetime of 20 seconds, with some measurements exceeding one minute. That is a claimed 1,000-fold improvement over Majorana 1. Gate operations run at one microsecond, and the qubit footprint is 1/100th of a millimeter. The company now targets a commercially useful scalable quantum computer by 2029, moved up from a prior estimate of 2033. The materials iteration was accelerated with Microsoft Discovery, the company's agentic AI platform for scientific research.

The physics community's response has been consistent with the pattern from Majorana 1. Outside experts say the topological approach still lacks sufficient independent verification. The 20-second coherence time is striking if accurate, since superconducting qubits typically decohere in around 100 microseconds. But coherence time alone does not confirm the topological mechanism Microsoft claims is responsible for it. The company has a history of bold announcements followed by retraction or significant revision, and that history shapes how the community reads each new result.

Compared to the five platforms covered in earlier posts, topological qubits occupy a unique position. Superconducting qubits and silicon spin qubits are fabricated systems with well-characterized error mechanisms. Trapped ions and neutral atoms offer long coherence times but slow gates. Photonic qubits avoid decoherence but struggle with deterministic interactions. Topological qubits, if the physics holds, would offer built-in error protection that reduces the overhead for fault tolerance substantially. IBM's Condor chip reached 1,121 superconducting qubits in 2023. Microsoft is betting that the right number is not more physical qubits but better ones, and that topological protection is how you get there.

Whether Majorana 2 represents genuine progress or another contested milestone, Microsoft is the only major commercial player publishing peer-reviewed claims on topological qubit architecture. The debate in the physics community is real, the skepticism is well-founded, and the potential, if the approach works, remains significant. If you want the background on the other five platforms before going deeper on this one, the earlier posts are linked at the top of this page.

When Quantum Hardware Drifts, NVIDIA’s New AI Steps In

Quantum computers do not fail in the way most people expect. They do not crash or throw errors in any obvious way. Instead, they drift. Qubits lose coherence, coupling parameters shift, and gate fidelities degrade without warning. Catching and correcting that drift is the job of calibration and error correction, traditionally a manual, slow, expert-intensive process. NVIDIA Ising is NVIDIA's attempt to close that gap with AI.

NVIDIA Ising is a model family, training framework, and open cookbook released in April 2026. It targets two specific problems: quantum processor calibration and real-time error correction decoding. Both problems block the path to fault-tolerant quantum computing, and both have resisted scaling by brute force. NVIDIA is betting that AI, specifically vision-language models and 3D convolutional neural networks, can do what hardware iteration alone cannot.

Autonomous calibration via vision-language models

The calibration side uses a vision-language model called Ising Calibration 1, a 35-billion-parameter Mixture-of-Experts (MoE) model based on Qwen3.5-35B-A3B. It reads calibration plots directly, the exact same visual plots a human engineer would study, and operates inside an agentic workflow to automate processor bring-up and retuning.

NVIDIA benchmarked the model against the QCalEval dataset, which contains 243 samples across 87 scenario types covering superconducting qubits and neutral atoms. Evaluating six core question types, Ising Calibration 1 achieved a 74.7% zero-shot average, outperforming general-purpose frontier models like GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro pressed into service. These numbers reflect the power of a model fine-tuned specifically for this domain rather than a general-purpose AI model.

Real-time quantum error correction

The decoding side addresses quantum error correction (QEC), where latency is not a convenience issue; it is a hard barrier. A surface code decoder that cannot keep up with syndrome extraction in real time becomes a fatal bottleneck for the entire processor.

NVIDIA's Ising Decoder SurfaceCode 1 uses a 3D CNN architecture to handle syndrome data across both space and time, a foundational requirement for lattice surgery operations. NVIDIA offers two variants. The Fast variant delivers 2.5x lower latency than PyMatching at d=13, p=0.003. The Accurate variant trades minor latency to deliver up to 3x better logical error rates compared to traditional decoding benchmarks. Both weights and an open training framework are hosted on Hugging Face, allowing hardware teams to fine-tune the decoders to their own noise models.

An open ecosystem for quantum builders

The training framework is fully open. NVIDIA published the QCalEval benchmark and the Ising Decoding training code on GitHub. Quantum hardware teams can adapt both the calibration agent and the decoder to their own QPU noise profiles rather than accepting a rigid, one-size-fits-all model. The Quantum Calibration Agent Blueprint provides an end-to-end starting point using the NVIDIA NeMo Agent Toolkit.

For quantum hardware builders and operators, the operational impact is concrete. Bringing up a new QPU or retuning after drift currently requires skilled engineers reading plots and adjusting parameters manually. Ising Calibration 1 runs that loop autonomously, compressing the time between hardware changes and operational readiness. The decoder result is even more immediately measurable: PyMatching is the current production standard, and the Ising Decoder beats it on both latency and accuracy at d=13. This is not a research result waiting for productization; it is a component you can drop directly into a CUDA-Q QEC pipeline today.

What this means for the quantum workforce

The workforce implications are worth spelling out. The quantum field has long assumed that the talent gap is primarily a physics problem, that you need more quantum physicists. NVIDIA Ising points to a second gap: AI and software engineers who can work at the quantum-classical interface. Ising Calibration 1 is a vision-language model. The decoder is a 3D CNN trained on syndrome data. These are standard deep learning architectures applied to a specific domain. A person who understands noise modeling, real-time inference pipelines, and model fine-tuning can contribute to the quantum software stack without a physics PhD.

For workforce development programs targeting the quantum supply chain, Ising gives you a concrete reference architecture for what the adjacent skill set looks like. The gap is not only physicists. It is technicians and engineers who can work with QPU data, fine-tune models to specific noise profiles, and integrate AI components into hardware pipelines. Community colleges and technical programs can point to this stack, VLMs for calibration, CNNs for decoding, agentic workflows for automation, as a curriculum target that does not require starting from quantum mechanics.

The gap between current NISQ devices and fault-tolerant quantum computers is not primarily a physics problem at this point. Calibration drift and decoder latency are engineering problems, and AI has a track record of closing engineering gaps faster than iterative hardware design does. NVIDIA Ising is a focused, tool-level response to two specific chokepoints. The benchmark numbers are strong. The tooling is open. If the results hold against real QPU data in production, this is a significant piece of the fault-tolerance stack, and a clear signal about where the next wave of quantum workforce demand is headed.

The Ferrari That Jony Ive Helped Build

I drive a Tesla now. Before that I had a Porsche Macan, and one of the things I miss is the cold start. The V6 would fire at a high idle, near 2,200 rpm with the exhaust valves open, hold there for 30 or 40 seconds, then settle into a normal idle. You felt it in your chest a beat before you heard it. Some mornings I started the thing just to listen. I taught 40 miles away at the University of Hartford, about 5 miles to the highway on the way home, and the part I waited for was getting on it at the Route 91 on-ramp. The Macan would dig in and snarl through the acceleration. The Tesla does the same merge faster and says nothing. Most days that's fine. Some days, on an empty road, I want internal combustion back.

Ferrari just showed its first electric car, the Luce, Italian for light. Four motors, one per wheel, 1,050 cv, zero to 100 km/h in 2.5 seconds, a 122 kWh battery on 800 volts, about 530 km of range. It also weighs 2,260 kg, seats five, and has a hatchback. 

What got my attention was the design credit. Ferrari handed the inside and outside to LoveFrom, Jony Ive and Marc Newson's studio. Ive ran design at Apple for twenty years. You can see it in the cabin: clean surfaces, glass buttons, real switches you can feel, a passenger shell that sits almost on its own inside the body. Ferrari has never let an outside firm shape a car like this. That alone is news.

They know about the sound, too. The Luce mounts an accelerometer at the center of the rear axle, reads the real frequencies coming off the spinning parts, then equalizes and amplifies them like a guitar pickup. So it isn't a fake V8 played through the stereo. It's the actual car, turned up. The motors use a Halbach array in the rotor, borrowed from Formula 1, and the front pair spins to 30,000 rpm.

Not everyone agrees the bet is worth making. Lamborghini scrapped its first EV, the Lanzador, in February, with CEO Stephan Winkelmann saying demand for an electric supercar was close to zero and calling EV work an expensive hobby. The Lanzador comes back later as a plug-in hybrid instead. Part of his reasoning was the exact thing I keep talking about: their buyers want engine sound and mechanical feedback, and the battery cars don't deliver it yet. After the Luce reveal, Ferrari's stock dropped and Winkelmann went on CNBC to say canceling was the right call. So one company is amplifying real vibration through a sensor, and the other decided the problem isn't solved and walked away.

So Ferrari looked at the same gap I feel every morning and tried to fill it with a sensor and some signal processing instead of an exhaust pipe. I doubt it gives you the cold start, that high-idle bark before the valves close, or the snarl of jumping on it on a highway on-ramp. You can't fake combustion you don't have. But I'm pretty sure I’d rather hear what the car is really doing than a recording of an engine nobody built. But…. I'll reserve judgment until I sit in one.