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Token Talk 22: Q-Day Is Coming. Why Doesn’t Venture Care?

June 18, 2025

By: Thomas Stahura & Nate Bek

A few weeks back, we began asking around about quantum. 

We spoke with investors, folks inside big tech, and a handful of startup founders. Everyone agreed: something's happening. Washington is pouring in money. The cloud giants are positioning to host quantum platforms like it’s the next GPU boom. CVCs are getting early exposure to hardware and software bets. 

And yet, when you talk to most valley VCs, you hear the same refrain: interesting, but not investable.

That tension seems to be the story. Quantum computing has become a strange mix of alarm and apathy. Quantum computing lives in a weird limbo between existential urgency and total indifference. Federal agencies and global rivals are treating it like a strategic emergency. Meanwhile, most VCs treat it like a curiosity. 

So why is DC moving fast while venture capital sits back? And what do we think?

For some reason, no matter where the conversation starts, it always ends up at the same place: Q-Day.

Q-Day, for the uninitiated, is the hypothetical day a quantum computer becomes powerful enough to break RSA encryption by running Shor’s algorithm incredibly quickly. RSA is the math that secures the internet. Break it, and you can access bank records, government secrets, private messages, and maybe even your neighbor’s crypto wallet. The risk is serious enough that adversaries are already staging "harvest now, decrypt later" attacks — stockpiling encrypted data today, hoping they can decrypt it when the tech catches up.

Like AGI, Q-Day is equal parts hype and legitimate concern. No one knows when it’s coming. Estimates range from five years to never. But governments aren’t waiting around. In 2016, the National Institute of Standards and Technology (NIST) kicked off a public competition to select cryptographic algorithms that could survive a quantum attack. 

By 2022, it announced its first picks: Kyber for public-key encryption. Dilithium, FALCON, and SPHINCS+ for digital signatures.

This effort, known as post-quantum cryptography (PQC), is a race to replace the locks before quantum finds the keys.

***

Suppose you have a diamond you want to send to a friend through the mail. But the postal service is corrupt and anything not in a locked box gets stolen. You have an unlimited supply of unbreakable boxes and unique locks. How do you send the diamond to your friend without it getting stolen?

Simple, you put the diamond in a box, lock it, and mail it. But your friend doesn’t have the key. So you send the key to the first box in a second locked box, but now they’ve got two boxes they can’t open. 

Ok reset, once again, you put the diamond in a box, lock it, and mail it. But when your friend receives it, instead of trying to open it, they put their lock on the box and send it back. When you receive it, remove your lock and ship back once more. Finally, your friend removes their lock and gets the diamond.

This is how RSA works. Your public key is the locked box. Your private key is the box unlocker. The encryption math relies on multiplying two big prime numbers, p and q, and publishing their product, n. Anyone can use n to encrypt messages, but only someone with the original primes (your private key) can decrypt them. It’s easy to go from p and q to n. It’s hard, today, to go the other way around.

Unless you have a quantum computer.

***

Instead of prime numbers, it uses math problems that quantum computers can’t easily solve. One of the most important is Kyber, which relies on lattices and is difficult to solve because of something called the Module Learning With Errors (MLWE) problem. Think of it like hiding your diamond in a mind-bending, multi-dimensional maze. No locks, no boxes, just direction. Only someone with the right pattern can navigate it.

Less than 1% of real-world systems use PQC today. But the urgency is growing. The scramble is on to upgrade infrastructure before Q-Day arrives.

That timeline — fuzzy, indefinite, yet somehow looming — is one of the core reasons venture capital has stayed away. Funds have a shelf life. LPs expect distributions. GPs want returns. The average check wants product-market fit in 18 months and a real business in 5 years. 

Ascend chatted with Chris Moran, vice president and general manager at Lockheed Martin Ventures, during a panel earlier this year. The corporate venture investor says quantum is mostly strategic for Lockheed; it's important for cybersecurity, simulation, and long-term design capabilities. The firm invested in Atom Computing, which uses optically trapped neutral atoms to build scalable, gate-based systems. It also backed IonQ, the name most people point to when they talk about quantum going public.

That tends to be the profile. It seems the most active investors in quantum right now aren’t traditional venture firms. It’s Lockheed. It’s IBM Ventures. It’s Microsoft, Google, Amazon. If quantum hits, they want to be the ones selling the picks and shovels.

IonQ is the most well-known quantum hardware company. It trades at a $10 billion market cap. In Q1 2025, it reported just $7.6 million in revenue. Most of that came from government contracts and academic partnerships. That’s more than 300x sales.

IonQ’s latest 10-Q spells it out: “anticipated future revenues from the U.S. government result from contracts awarded under various U.S. government programs.” These deals come with long timelines, risk-sharing, and complex functionality requirements. They’re good signals for deep tech maturity, but they don’t translate to enterprise adoption.

The 2024 year-end report backs it up. IonQ closed a $54.5 million contract with the U.S. Air Force Research Lab — its biggest deal to date! A big win, but again, not commercial pull.

Even so, most analysts have a buy rating on the stock (look it up). The bull case rests on scarcity. If quantum does work, IonQ owns the cloud rails, the IP, and a defensible moat. It’s priced for dominance, not for today’s numbers.

Jon Chu at Khosla Ventures framed the dynamic well in a X post: “DC sees the risk if it does work, but doesn’t know how to judge the probability or investability by looking at the technical thesis.”

That’s the divide. Washington sees a global race to crack encryption, simulate materials, and optimize weapons systems. VCs see a science project with no clear buyer and no path to $100 million ARR.

Still, we see movement in the market, especially at the application (software) layer. A new wave of pre-quantum startups is building tools for use cases that don’t require fault-tolerant machines. Think drug discovery, molecular modeling, cybersecurity, faster search, and optimization for manufacturing and aerospace. These companies are translating theoretical algorithms into usable software. They want to be ready when the hardware matures. 

(If you’re building in this space, we’d love to speak with you). 

This work is important. It’s the bridge between research and commercialization. But it’s not the end state. Until quantum has a breakout use case or a commercial buyer base beyond federal labs, it will remain in the hands of governments, defense primes, and cloud hyperscalers who can afford to think in decades. And no, you probably won’t ever be getting one in your home or pocket.

As David Ulevitch at Andreessen Horowitz put it in a post: “In Silicon Valley, almost nobody talks about quantum computing. In Washington DC, it comes up all the time.”

Tags Token Talk, Quantum Computing

Token Talk 21: We Built the Chips. Now Build the Apps

June 11, 2025

By: Thomas Stahura

Last December, Google unveiled Willow, its new quantum chip. The media dubbed it mind boggling when, with only 105 qubits, it was able to solve a problem in five minutes that would take a classical computer ten septillion years to complete. That problem is called Random Circuit Averaging (or RCA) which I’ll explain in a bit.

On the news, Google’s stock jumped, as did its competitors: Microsoft, Rigetti, D-Wave, and IONQ. For a moment, it seemed, quantum hype dethroned AI to become the talk of the town. Two months later, Microsoft responded by announcing its own quantum chip called Majorana 1, causing another stock bump. However, at only 8 qubit, it's still early stage, and the tech giant  has yet to publish its RCA results.

RCA is a benchmark, not a problem, as such is designed to gauge quantum computer performance. The whole test is basically "can you sample from this crazy quantum distribution faster than classical computers can even calculate what that distribution should be?" 

To do this, researchers must:

  • Pick a number of qubits (like 105 for Google's chip)

  • Generate a random sequence of 20+ random quantum gates (like Hadamard or Pauli-X mentioned last week)

  • Run information through the more than 20 gate layers a million times or so

  • Collect each runs generated bitstring output (something like "01101001...")

  • Use classical computers to simulate what the "perfect" quantum computer would output

  • Measure how close your actual results are to the ideal

Each additional gate creates more quantum entanglement between qubits. More layers = more complex quantum correlations = harder for classical computers to track. More than 20 layers is where classical simulation becomes practically impossible. If a quantum computer finishes in minutes but classical takes years → quantum advantage. At least, that's how the thinking goes.

RCA is cool but not practical. It's like saying your AI passed the MENSA iq test. So where are the real world quantum applications?

Enter the wonderful world of optimization and quantum annealing!

Classically, annealing is an algorithm inspired by metallurgy: you heat up a material and then cool it slowly so atoms settle into a low-energy (optimal) state. In the math world of optimization, you randomly explore solutions, occasionally accepting worse ones to escape local minima, and gradually “cool” to settle into the best solution.

Imagine you’re standing in a vast, foggy landscape of rolling hills and valleys. Each point in this landscape represents a possible solution to your optimization problem. The height at each point is the “energy” of that solution — the lower the energy, the better. Classical annealing is like wandering this landscape with a lantern. At first, you’re allowed to take big, random steps, even uphill, so you don’t get stuck in a small valley (local minimum). As time goes on, you “cool down,” and your steps get smaller and more cautious, focusing on moving downhill. The hope is that, by the end, you’ve found the deepest valley, the global minimum. The catch? Sometimes, no matter how clever you are at wandering, you can still get stuck in a valley that isn’t the lowest one (not optimal). The fog is thick, and you can’t see the whole landscape at once.

Quantum annealing replaces random steps with quantum tunneling, allowing the system to “tunnel” through energy barriers rather than climb over them. In our example, instead of just walking over the hills, you can tunnel through them to a lower valley on the other side, even if it looks impossible from a classical perspective. Essentially, thanks to quantum mechanics, quantum tunneling can help escape local minima that would trap a classical algorithm.

Without getting too technical, quantum annealing does not use any quantum logic gates! Instead, an optimization problem is encoded as a Hamiltonian (fancy math representing the system's total energy). This sets up the energy landscape so that the lowest energy state (the ground state) represents the best solution to the problem. Then (thanks to quantum physics), the system naturally wants to stay in the lowest energy state and naturally “relaxes” into the answer.

Companies like D-Wave, founded in 1999, are leading the charge in quantum annealing. D-Wave’s Advantage system, accessible via its Leap cloud platform, has been used by the likes of Volkswagen to optimize traffic flow and by SavantX to streamline port operations, reducing costs and improving efficiency. D-Wave charges a subscription for cloud access and consulting services. In 2024, D-Wave reported contracts with major firms, contributing to its growing commercial traction.

Similarly, IonQ, which runs a quantum computing manufacturing facility up in Bothell, operates primarily as a Quantum-as-a-Service model, providing access to its quantum computers via major cloud platforms like AWS, Azure, and GCP. The company was founded in 2015 and became the first quantum company to IPO back in 2021.

Beyond optimization and the cloud, quantum computing is making inroads in drug discovery and materials science. For example, Algorithmiq’s collaboration with IBM’s Quantum Network focuses on quantum chemistry simulations to identify promising drug candidates, potentially shaving years off development timelines. Generating revenue through partnerships and licensing their software platforms. Algorithmiq secured €13.7 million in funding to scale its offerings. 

Quantinuum is also working with firms like Samsung to apply quantum algorithms in materials design, optimizing material properties for semiconductors and batteries. These early applications, still in the prototyping phase, are driving real revenue through research contracts and pilot projects.

Quantum applications are hitting the market and making money. We now have enough qubits to do cool things! It feels like the bottleneck is shifting from hardware to software. The industry needs more quantum developers to build the next generation of algorithms and apps. Or maybe develop an AI that can program in Q# or the other quantum languages. On the hardware side, things are starting to get crowded: Xanadu, Alice & Bob, Atom Computing, PsiQuantum, Rigetti, NVIDIA, QuEra Computing, and Intel, just to name a few, are all developing their own quantum computers. 

I think we'll see much more change in the quantum industry in the next 30 years than the last 30. Again with most of that change coming from innovative software.

Stay tuned next week for the final installment of our quantum series!

P.S. If you have any questions or just want to talk about AI, email me! thomas @ ascend dot vc

Tags Token Talk, Quantum Computing

Token Talk 20: How Quantum Computers Work, Pt. 1

June 4, 2025

By: Thomas Stahura

Editor’s note: This is the first in a three-part Token Talk series on quantum computing. Today’s post covers the fundamentals of how quantum machines work. Next week, we’ll dive into the key players in the field and the startups already building real applications.

You’ve probably heard of quantum computers. 

Invented in 1998, this breed of thinking machines are billed as the quintessential classical computer disrupter. But when asked exactly why or how these machines will change the world, most folks just shrug. 

Over the last 27 years, the field has gone from two qubits per chip to an astounding 1,121 qubits in IBM's latest quantum chip. Still, few have seen, let alone used, a quantum computer. What gives?

Before diving into the new world of quantum computers, let's quickly cover the old world of classical computers.

Classical computers (like the device you're looking at now), store information in binary bits of 1s and 0s. This information flows through a series of logic gates that each perform a certain mathematical operation. These logic gates are the following: NOT, AND, OR, NAND (Not AND), NOR (Not OR), XOR (Exclusive OR), XNOR (Exclusive NOR / Equivalence).

Take the NAND gate. Its function is to output 0 only if both of its inputs are 1; otherwise, it outputs 1.

So,

Input: 1, 1 → Output: 0

Input: 1, 0 → Output: 1

Input: 0, 1 → Output: 1

Input: 0, 0 → Output: 1

The NOR gate, on the other hand, outputs 1 only if both inputs are 0; otherwise, it outputs 0.

So,

Input: 0, 0 → Output: 1

Input: 0, 1 → Output: 0

Input: 1, 0 → Output: 0

Input: 1, 1 → Output: 0

And lastly, the NOT gate (AKA the inverter), flips the input.

So,

Input: 1 → Output: 0

Input: 0 → Output: 1

Logic gates are the LEGO bricks of computation. By chaining them together, you build circuits that can add, subtract, multiply, and more. Ok, now to understand how quantum computers differ from classical computers, you also need to understand the concept of reversibility.

A logic gate is reversible if you can always uniquely recover the input from the output.

For example, you have a NAND gate and it outputs a 1, what was the input? It could be 0,0 or 0,1 or 1,0. Since we cannot uniquely recover the input from the output, we say NAND gates are not reversible. In other words, information (about the input) is lost.

NOT gates, on the other hand, are reversible. For example, if a NOT gate outputs a 0, we know the input must be 1. And if it outputs a 1, its input must be 0.

Now that you get classical gates — NAND, NOR, NOT, etc. — it's time to dive into quantum computers because they are playing a whole different game. Instead of bits, they use qubits. 

Qubits aren’t just 0 or 1; they can be both at the same time (that’s superposition). And quantum gates are the logic gates that manipulate these qubits.

The first rule of quantum math is: Every quantum gate is reversible. Meaning you can always run them backward and recover your original state.

Classical gates (like NAND/NOR) can destroy info (not reversible). Quantum gates never do. They’re always reversible, always unitary (fancy math words for “no info lost”).

As such, because of reversibility, quantum computers have a unique set of quantum logic gates that permit a certain kind of math. Let's go over two of them:

Hadamard (H) Gate is the superposition gate. Input a 0, you get a 50/50 mix of 0 and 1. Imagine flipping a coin, as it's spinning in mid air, it forms a 3d sphere and its probability, at that moment, is 50/50 chance of being heads or tails. Input a 1, same deal — still a 50/50 mix, but with a phase flip. Imagine representing the direction and speed of the coin’s spinning as an arrow in 3d space, this arrow has a direction (phase), and speed (magnitude). Flipping the phase reverses the direction of the coin's spin. The Hadamard gate is how you unlock quantum parallelism: it takes a boring, definite state and turns it into a quantum probabilistic state. In short, it’s the logic gate that turns classical bits into quantum bits.

So,

Input: |0⟩ → Output: 50% chance of being 1 or 0

Input: |1⟩ → Output: 50% chance of being 1 or 0

Once your qubit is in superposition, you can start doing some wild quantum tricks. The next essential gate is the Pauli-X gate (often just called the X gate). Think of the X gate as the quantum version of the classical NOT gate. It flips the state of a qubit:

Input: |0⟩ → Output: |1⟩

Input: |1⟩ → Output: |0⟩

If your qubit is in superposition (say, α|0⟩ + β|1⟩), the X gate swaps the amplitudes:

Input: α|0⟩ + β|1⟩ → Output: α|1⟩ + β|0⟩

Still reversible, still no info lost.

In quantum computing, amplitudes (like α and β) are complex numbers that represent the arrows in 3d space mentioned earlier. They encode both the phase and magnitude of a qubit with the probability of the qubit given by the squared magnitude of the amplitude. The phase (angle) of the amplitude affects how quantum states interfere, but is not directly observable as a probability.

After many quantum logic gates, when you measure a qubit, its superposition collapses to a definite 0 or 1. So, to get a quantum speedup, your algorithm must:

  • Exploit superposition and entanglement to process many possibilities at once.

  • Be reversible (unitary operations only).

  • Use a technique called interference to amplify the correct probabilities and cancel out the wrong ones.

Most problems don’t fit this mold. If you just naively port classical code, you’ll get no speedup — or worse, a slowdown.

As of today, there are only four algorithms that take advantage of quantum computers' unique properties. They are, Shor’s Algorithm (Factoring Integers), Grover’s Algorithm (Unstructured Search), Quantum Simulation (physics simulations), and Quantum Machine Learning (QML)

  • Shor’s algorithm, using quantum Fourier transform, finds the prime factors of large numbers exponentially faster than the best classical algorithms. This has massive implications in cryptography since it breaks RSA encryption, which relies on prime factoring being difficult, and secures most of the internet today

  • Grover’s algorithm, using amplitude amplification to boost the probability of the correct answer, searches an unsorted database about 99.9% faster for a million items. And the speedup grows as the database gets bigger.

  • Quantum Simulation, using entanglement and superposition, models complex quantum systems — like molecules, proteins, or new materials — that are impossible for classical computers to handle. This unlocks breakthroughs in drug discovery, chemistry, and materials science by letting us “test” new compounds in silico before ever touching a lab.

  • Quantum Machine Learning (QML), using quantum circuits, can turbocharge core tasks like linear algebra and sampling. Quantum computers, in theory, can solve huge systems of equations, invert matrices, and sample from complex probability distributions faster than classical machines. Though this is still very much in the domain of researchers.

A new wave of pre-quantum startups is building the application layer for quantum computing. Just as AI startups turned research into real-world value, these teams are doing the same for quantum by targeting proven algorithmic advantages. They are developing tools for drug discovery, molecular modeling, cybersecurity, faster search, and design optimization in aerospace and manufacturing. These companies are positioning themselves now so they are ready to scale when the hardware becomes readily available.

Ok, that was a crash course in quantum computing! Abstract, but just scratching the surface. And there’s still a whole universe left to explore: More quantum logic gates, quantum error correction (how do you keep qubits from falling apart?), decoherence (why do quantum states vanish so easily?), entanglement (spooky action at a distance, anyone?), and the wild world of quantum hardware (trapped ions, superconducting circuits, photonics, and more). We haven’t even touched on the real-world challenges — scaling up, keeping things cold, and making quantum computers actually useful outside the lab. 

Tags Token Talk, Quantum Computing, Quantum

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