Physics PhD analyzing quantum technology for investors & curious minds. Follow if you want to understand quantum tech without the hype. Not financial advice.
Joined May 2026
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What is quantum sensing?
It can share technology with quantum computing, but serves a different purpose.
Both use quantum physics. While computing gets attention, sensing has decades of real-world use, with $INFQ building a sensing business and $IONQ entering through Vector Atomic.
The field spans very different levels of maturity. The first atomic clock was demonstrated in 1949, and quantum magnetometers followed in the 1950s. Some other technologies are only now moving from laboratories toward real-world deployment.
That uneven maturity is what makes the field interesting from an investor perspective.
So for this deep dive, I want to understand the technology, separate what is established from what is emerging, and see where the frontier actually sits today.
$GOOGL and $MSFT both report Wednesday, October 28. On quantum, the bar is low. One measured number from either call would be news.
Alphabet holds its call after the close, at 4:30 PM ET, with results out before it.
Microsoft reports after the close, with a webcast at 5:30 PM ET.
For Alphabet, does Pichai repeat his five year timeline for commercial quantum to investors? For Microsoft, is Magne, its machine with Atom Computing, still due early 2027?
The week after, the pure plays start. $IQMX and $IONQ report November 5, and $QBTS is expected that week too, per calendar estimates. Dates and what to watch next Sunday.
This week we'll see if the megacaps have anything new to say about quantum.
ALT Calendar card titled "Reporting this week", Week of Oct 26, 2026 · times in ET. $GOOGL (Alphabet): Wed Oct 28, call after the close, call 4:30 PM ET. To watch: Any update on the five year quantum timeline? $MSFT (Microsoft): Wed Oct 28, after the close, webcast 2:30 PM PT (5:30 PM ET). To watch: Any update on Magne, its quantum machine due early 2027? Next week: $IQMX and $IONQ report Thursday, Nov 5. $QBTS is expected that week too (est.).
Sources:
Alphabet date: abc.xyz/investor/news/news-d…
Microsoft date: news.microsoft.com/source/20…
IQM date: sec.gov/Archives/edgar/data/…
IonQ date: investors.ionq.com/news/news…
Pichai, five years: sources.news/p/sundar-pichai…
Magne, early 2027: quantum.microsoft.com/en-us/…
You may not need a fully fault tolerant machine for the first useful quantum result.
A new Oxford study says about 5,000 neutral atoms could do it, the platform $INFQ builds on. It's a simulation, and the margin over classical is thin.
Full error correction is expensive because some operations need dozens of protected steps each. The idea here is to protect most of the computation but do those costly operations directly. Far less overhead, at the price of a bit more noise.
The target is a classic physics problem about how electrons behave in materials.
The authors say a version just beyond what classical computers can handle fits in about 5,000 atoms, if each physical operation fails only about once in 1,000 tries.
That points to a first useful result with thousands of atoms, long before a fully fault tolerant machine.
I'd like to watch companies like Infleqtion for a real hardware test.
ALT Illustration from the paper of a neutral atom quantum computer: rows of single atoms, each held in a focused laser beam, with some pairs highlighted for two-atom gates. On the right, a computer screen shows a laser pulse shape being optimized to reduce gate errors. Source: Veroni, Deger, Daley (Oxford), arXiv:2610.26471, Fig. 1(c).
Paper: Veroni, Deger, Daley (Oxford), arXiv:2610.26471 arxiv.org/abs/2610.26471
Image: Fig. 1(c) from the paper.
@M_Decoherence asked a sharp question under my first post on the NIST paper with an $INFQ researcher: if the ytterbium line moves, how do you know what moved it?
The paper doesn't answer it.
It pitches the line as a clock, a voltage reference or a thermometer, and it has a plan for each job on its own.
The thermometer would be calibrated inside a temperature-controlled shield in their clock lab. The voltage reference would get a known field between two metal plates. For the laser's own push on the line, they point to three published ways to cancel it.
What's missing is the case where the pushes mix.
Digging deeper revealed something interesting. Stashed in the paper's own reference list, a 2011 proposal notes that a warm wall moves all the atom's high energy lines by about the same amount, while a stray electric field pushes the higher ones much harder.
The reason is lovely. An electron that far from the nucleus feels heat almost like a free electron, so the wall pushes them all alike, by an amount set by nature's constants rather than by the atom.
That means measuring two lines can solve for both.
Think of two thermometers that read heat the same way, but one also picks up stray charge about four times more strongly. The gap between their readings is the stray field. What's left is the temperature.
The paper's own calculations suggest this could work for ytterbium, but the authors call those numbers a first try they can't yet check against experiment.
To be fair, this is a "first steps" paper. It picks the line, calculates its properties and shows it can be seen.
If you're a researcher, this looks like a topic worth pursuing. I'll be watching for developments in this field and will post if I find any meaningful advancement.
Part of my deep dive into quantum sensing:
What is quantum sensing?
It can share technology with quantum computing, but serves a different purpose.
Both use quantum physics. While computing gets attention, sensing has decades of real-world use, with $INFQ building a sensing business and $IONQ entering through Vector Atomic.
The field spans very different levels of maturity. The first atomic clock was demonstrated in 1949, and quantum magnetometers followed in the 1950s. Some other technologies are only now moving from laboratories toward real-world deployment.
That uneven maturity is what makes the field interesting from an investor perspective.
So for this deep dive, I want to understand the technology, separate what is established from what is emerging, and see where the frontier actually sits today.
Paper, Hunt et al. (NIST, CU Boulder): arxiv.org/abs/2610.22574
2011 proposal, Ovsiannikov, Derevianko and Gibble: arxiv.org/abs/1107.3169
Yesterday's post: xitter.cf/QInvestorNotes/status/…
Co-author Bothwell's present address: Infleqtion.
One line in ytterbium's spectrum could become a clock, a voltmeter and a thermometer. That's the case a new NIST preprint with an $INFQ co-author makes.
A Rydberg atom has one electron lifted far from the nucleus. That loosely held electron reacts strongly to electric fields and to heat radiation, which is what makes it a good sensor.
Ytterbium has a lucky ladder. A middle energy level sits at about 49.7% of the energy needed to free the electron. So two photons of the same violet color, 399 nm, can lift the atom from its ground state to a Rydberg state without much laser power.
Send those two photons from opposite directions and the atom's motion cancels out. A moving atom sees one beam shifted up in frequency and the other shifted down by the same amount, so their sum stays fixed.
One beam gives a blurry line. Two opposing beams make it 14 times sharper. The authors say it's the sharpest line of its kind measured so far.
And sharp is what makes it useful.
The line barely moves when magnetic fields change, so it can tick a clock. It does move when an electric field changes, so it can measure voltage. And it moves with heat in the room, so it can measure temperature.
One line, three sensors.
This was done in a big lab setup with super-cold atoms, not in the small, simple box a real product would need.
In the field of quantum sensing, size matters.
Part of my deep dive into quantum sensing:
The US vs. China fight comes for quantum next?
If you own US quantum stocks like $IONQ, $RGTI, $QBTS or $INFQ, Washington's new quantum headline changes less than it sounds.
On October 22, the Treasury proposed the rule that puts the COINS Act into practice. It limits American investment in Chinese companies working on chips, AI, supercomputers, biotech, hypersonic weapons and quantum.
Chips get a middle lane, where some deals are allowed as long as you report them.
Quantum gets no such lane, and neither does AI, which the rule now bans outright.
Quantum computers would be banned outright, and the list goes deep. It covers not only the machines, but the dilution refrigerators and cryocoolers that keep them cold and the software that designs and controls them.
Quantum sensing is banned when it serves the military, intelligence or mass surveillance. Quantum networks are banned more broadly, including any built to link quantum computers or for secure communications such as quantum key distribution.
That sounds dramatic, but quantum was already in the banned bucket under the rules in force since January 2025. The new rule keeps it there and widens the list.
Congress wrote the program into law last December, and this rule fills in the details.
Comments close December 7.
I'm watching whether the final rule keeps quantum entirely in the banned bucket, and whether China answers by tightening its own side of the supply chain.
ALT Excerpt from page 37 of the US Treasury's COINS Act proposed rule, October 22, 2026. Highlighted phrases: "dilution refrigerator," "two-stage pulse tube cryocooler," "software for the purpose of designing, simulating, testing, or controlling a quantum computer," and "quantum sensing platform," all listed as prohibited technology for US investment in countries of concern.
Treasury press release: home.treasury.gov/news/press…
Proposed rule (quantum section, p. 37): home.treasury.gov/system/fil…
$NVDA committed at least $6.5 billion to photonics this spring, to move AI data, not to compute.
It made me wonder about another NVIDIA job: decoding quantum errors for $QNT.
Quantinuum's Helios decodes its errors on NVIDIA GPUs in real time. I suspect that decoder won't stay electronic.
In superconducting machines, the next limit may be getting error data out of the fridge. A 2025 lab demo read out one qubit using only light.
But what if decoding becomes neural network math, the kind optical chips are built for, and data leaves the fridge as light? Does the decoder go photonic?
This hypothesis has a weak spot: today's decoders, like matching, make step-by-step choices, and light is bad at that. It's built for doing one big calculation all at once.
I'd watch for the first decoder that computes with light, not just carries data.
ALT Bar chart from NVIDIA comparing decoding time per error-correction round for a distance-13 surface code at a 0.3% physical error rate. A classic matching decoder on a CPU alone takes about 5.2 microseconds per round. Adding NVIDIA's Ising neural network pre-decoder on a GPU cuts it to about 2.1 microseconds with the fast model and about 2.3 microseconds with the accurate model, with the GPU doing most of the remaining work.
Sources:
NVIDIA QEC 0.6: nvidia.github.io/cuda-quantu…
Helios on NVIDIA GPUs: nvidianews.nvidia.com/news/s…
Fridge I/O limit: arxiv.org/abs/2208.08547
All-optical readout: nature.com/articles/s41567-0…
NVIDIA photonics bets: cnbc.com/2026/05/29/nvidia-p…
Image: NVIDIA, QEC 0.6 blog
A private startup now says RSA-2048 could be broken with 72,000 physical qubits in 3.7 days.
That's about 12x fewer qubits than $GOOGL's 2025 estimate at a similar runtime. It's only a simulation for now, but the Q-Day math keeps getting cheaper.
Iceberg Quantum (Sydney) built a faster decoder, Crevasse, and ran a trillion samples per code. On its main code at a 0.1% physical error rate it saw 3 logical errors, about 2 per 10 trillion logical qubit cycles. RSA needs under 1 per trillion.
The fine print: 0.1% error on every operation, a 1 µs code cycle, and qubits that connect beyond their neighbors.
No machine has all three at this scale.
Crevasse averages 4 µs per round, so it doesn't yet keep pace with that 1 µs cycle. At a 1 ms cycle, typical of atoms, the same table says 2.1 million qubits and 32 days.
And there's no peer-reviewed paper yet, not even a preprint to check.
As an investor, I now see decoding speed and connectivity as the likely bottlenecks, so that's what I'd track in hardware roadmaps.
We’ve run trillions of simulations to test our fault-tolerant quantum architectures, thanks to our new decoder, Crevasse. We think it’s the first QLDPC decoder with both the speed and accuracy needed for utility-scale quantum computing.
Release: iceberg-quantum.com/blog/rel…
Sources:
Iceberg's Pinnacle 1.2 release (Oct 22): iceberg-quantum.com/blog/rel…
Pinnacle 1.0 paper, Feb 2026: arxiv.org/abs/2602.11457
Gidney 2025, under 1M qubits in about a week: arxiv.org/abs/2505.15917
$GOOGL is placing a bet on AI-made quantum software.
Google Quantum AI opened a Kaggle competition this week, paying $50,000 for a smaller quantum circuit.
What if the next leap needs fewer qubits, not more?
Entrants, expected to use AI agents, compress three costly building blocks of future algorithms, for fusion, optimization and chemistry. Every entry must pass Google's verifier and is scored on qubits and gates.
The trigger was ecdsa.fail. Over 100 people, most with no quantum background, used AI agents to cut the cost of elliptic curve attack circuits roughly in half. Elliptic curves protect Bitcoin's signatures.
That is not a new Q-Day date. It is a reminder that the attack can get cheaper with no new hardware.
The chemistry task centers on a quantum lookup table, the same step $XNDU said this month it cut by nearly 4x.
My guess is that the algorithm race is starting to matter as much as the qubit race.
As an investor, I wonder what it means for pure-play quantum software companies.
To bring quantum applications within reach sooner, we must compress the circuits that power them. Join our @Kaggle competition to build efficient, verifiable circuits using our new open source kickmix library: goo.gle/4BqF2QX
ALT Graphic reading "Verifiable Quantum Circuit Compilation Competition with Google Quantum AI and Kaggle" featuring a close-up image of a leaf. At the bottom, text reads "Enter the competition on Kaggle."
Sources:
Google's announcement (Oct 21): opensource.googleblog.com/20…
Companion paper, ecdsa.fail result on p. 2: arxiv.org/abs/2610.23832
Craig Gidney's blog, $50K first prize: algassert.com/post/2605
Xanadu's QROM paper, nearly 4x cheaper: arxiv.org/abs/2610.02321
One line in ytterbium's spectrum could become a clock, a voltmeter and a thermometer. That's the case a new NIST preprint with an $INFQ co-author makes.
A Rydberg atom has one electron lifted far from the nucleus. That loosely held electron reacts strongly to electric fields and to heat radiation, which is what makes it a good sensor.
Ytterbium has a lucky ladder. A middle energy level sits at about 49.7% of the energy needed to free the electron. So two photons of the same violet color, 399 nm, can lift the atom from its ground state to a Rydberg state without much laser power.
Send those two photons from opposite directions and the atom's motion cancels out. A moving atom sees one beam shifted up in frequency and the other shifted down by the same amount, so their sum stays fixed.
One beam gives a blurry line. Two opposing beams make it 14 times sharper. The authors say it's the sharpest line of its kind measured so far.
And sharp is what makes it useful.
The line barely moves when magnetic fields change, so it can tick a clock. It does move when an electric field changes, so it can measure voltage. And it moves with heat in the room, so it can measure temperature.
One line, three sensors.
This was done in a big lab setup with super-cold atoms, not in the small, simple box a real product would need.
In the field of quantum sensing, size matters.
Part of my deep dive into quantum sensing:
What is quantum sensing?
It can share technology with quantum computing, but serves a different purpose.
Both use quantum physics. While computing gets attention, sensing has decades of real-world use, with $INFQ building a sensing business and $IONQ entering through Vector Atomic.
The field spans very different levels of maturity. The first atomic clock was demonstrated in 1949, and quantum magnetometers followed in the 1950s. Some other technologies are only now moving from laboratories toward real-world deployment.
That uneven maturity is what makes the field interesting from an investor perspective.
So for this deep dive, I want to understand the technology, separate what is established from what is emerging, and see where the frontier actually sits today.
The paper:
arxiv.org/abs/2610.22574
Funded by NIST, ONR and NSF. Co-author Tobias Bothwell's listed present address is Infleqtion.
Image: Fig. 4(a), Hunt et al., arXiv:2610.22574, CC BY 4.0.
$QNT's Helios is now inside $ORCL's cloud, but only as a beta, and Oracle picks who gets in.
Oracle opened its OCI Quantum AI Compute Beta this month. Approved organizations get cloud access to Helios alongside Oracle's GPUs and HPC.
For investors, distribution through a big cloud is a real plus. The open question is who owns the customer.
No pricing, customers or revenue were disclosed. Helios itself is still "advancing toward installation" at Oracle.
I'll watch whether any of this shows up as revenue.
Big milestone in our partnership with @Oracle. Organizations can now apply for early access to Quantinuum's Helios quantum computer through Oracle's Quantum AI Compute program.
This marks an important step forward in our collaboration and in advancing hybrid quantum-AI computing at enterprise scale, giving organizations new ways to explore how quantum computing, AI, and HPC can be combined to address some of the world's most computationally demanding challenges.
Next week at Oracle AI World, our Chief Commercial Officer Larry Stack joins Oracle Cloud Infrastructure President Mahesh Thiagarajan on the main stage to discuss what this progress means for customers and how these capabilities can accelerate discovery, simulation, optimization, and other high-value enterprise applications.
We're excited about what's ahead.
Read more here: quantinuum.com/blog/quantinu…
Continuing my quantum-sensing deep dive with ytterbium.
A new NIST preprint shows how one spectral line, reached with a single laser color, could anchor a clock, a voltmeter and a thermometer.
arxiv.org/abs/2610.22574
An interesting observation from $HON's Q3 filing. $HON carries its 48% stake in $QNT at $7.1 billion. At today's price, the market says it's worth about $4.6 billion.
Why does it matter if you're a Quantinuum holder?
The lock-up ends November 30. That gap probably makes a near-term Honeywell sale less likely, since selling now would lock in a loss against book value.
The numbers come from Honeywell's 10-Q, filed today. On September 30 the stake's book value was $7.1 billion and its market value $6.2 billion. Since then $QNT has fallen about another 25%.
Under equity accounting, Honeywell doesn't mark the stake to market each quarter. It writes it down only if it judges the drop to be more than temporary.
I will watch for an impairment. Once the stake is written down, selling no longer books a new loss.
ALT Bar chart of Honeywell's 48% stake in Quantinuum. Value at the June 2026 IPO: $7.5 billion. Honeywell's book value on September 30: $7.1 billion. Market value on September 30: $6.2 billion. Estimated market value at the October 22 price of $35.87: $4.6 billion. Source: Honeywell 10-Q, October 22, 2026.
Sources:
Honeywell 10-Q, Q3 2026 (Oct 22, 2026), Quantinuum note and fair value table: sec.gov/Archives/edgar/data/…
$QNT price history:
nasdaq.com/market-activity/s…