A Plain-Language Edition of Quantum from the Ground Up

Einstein called it spooky action at a distance. He meant quantum entanglement, and
he thought it couldn't be real. He was wrong!

I published a plain-language edition of Quantum from the Ground Up today, built for that reader.

The original book is written for people with a technical background who haven't taken a graduate course in quantum mechanics. It works well for that reader. It does not work for someone who has never had a reason to think about qubits, cryogenics, or post-quantum cryptography and just wants to know what the technology actually does.

The two audiences want different things. Someone building a career in the field wants precision: exact fidelity numbers, fabrication steps, the difference between a physical qubit and a logical one. Someone reading a headline about a new quantum computing milestone wants to know what it means and whether it matters to them. The plain-language edition is built for the second person.

It covers the same 19 chapters as the technical edition, at a fifth of the length: 13 pages instead of 73. It leaves out the math, the fabrication chemistry, and the dense statistics tables. It keeps the facts that matter: what quantum computers actually do right now, why companies are spending billions of dollars building them, why your bank account and your medical records are already part of this story, and where the actual jobs are for people without a physics degree.

Both editions are free. Both get updated quarterly as new research and hardware announcements come out. The next technical edition publishes tomorrow.

You can find current and older versions of each in this folder.

If you have been putting off understanding quantum computing because every explanation you found assumed a physics background you don't have, this edition removes that excuse.

Brookhaven and Stony Brook Just Linked a Quantum Network Through Open Air

In my last post, I laid out the fiber problem: quantum networks can't use classical repeaters because the no-cloning theorem forbids copying an unknown quantum state, so every photon has to survive, unassisted, from source to destination. Fiber compounds that: it loses about 0.2 dB/km, and its only efficient band (1550 nm) doesn't match the wavelengths most atomic memories emit, forcing lossy conversion steps at each end.

Commercial telecom has been married to one wavelength band for as long as I've been in the field, going back to writing the telecommunications curriculum for Verizon's NextStep program in 1995 and later directing NSF-funded Centers of Excellence in telecommunications and optics and photonics. My dad worked the generation before that, retiring in 1984 back when the network still ran on copper and fiber hadn't arrived yet. Brookhaven National Laboratory and Stony Brook University just did something a little different. 

Photons travel 13 miles through open air from Stony Brook's Quantum Watchtower to Brookhaven's Quantum Lighthouse, using adaptive optics instead of fiber. Link connects to existing 161 mile fiber network, with a 30 mile extension to Yale planned next.

On August 21, researchers sent single photons and entangled photon pairs 13 miles through open air, from Stony Brook's Quantum Watchtower to Brookhaven's Quantum Lighthouse in Upton, New York, no fiber involved for that leg of the trip. The daytime event was a formal demonstration for DOE and state officials. The actual first detection happened two days earlier, in the dark: at 12:26 a.m. on August 19, the Lighthouse recorded entangled photon pairs arriving from the Watchtower, confirmed through phase folded photon analysis comparing on-phase detection counts against background noise rates.

Both facilities exist to solve one problem: Brookhaven and Stony Brook need an unbroken line of sight to each other, which is why the Lighthouse sits on the only Brookhaven building with that sightline, a seven story rooftop installation, while the Watchtower sits atop Stony Brook's Health Sciences Center. Fog blocks the link outright. Bright daylight is a subtler problem. It raises atmospheric turbulence and buries the faint photon signal in background light, a limitation the Brookhaven team has compared to trying to spot a flashlight beam from a rooftop in broad daylight.

The optics came out of Brookhaven's Instrumentation Department, built on adaptive optics designs borrowed from the Vera C. Rubin Observatory. Photons leave the Watchtower through a fiber core five microns wide, about a tenth the width of a human hair. A telescope expands that pinprick of light into a 25 inch, 0.6 meter beam to match the primary mirror, while deformable mirrors correct for atmospheric turbulence in real time at kilohertz frequencies. At the Lighthouse, the process runs in reverse: the beam narrows back down and threads into a matching five micron fiber core for detection.

Commercial fiber networks are locked into wavelengths near 1550 nanometers because that band travels through glass with the least loss over distance. A free-space link carries no such requirement. Researchers can transmit infrared wavelengths native to the atomic systems and quantum processors themselves, opening a direct channel to entangle remote atomic memories without a wavelength conversion step.

The new link folds into an existing fiber network spanning 161 miles and eight nodes across Long Island and the New York City area, the longest metropolitan quantum network in the country. DOE Under Secretary for Science Dario Gil cut the ribbon on the receiving aperture at the August 21 event, framing the connection as a step toward linking individual quantum computers into something larger.

A third facility, functionally identical to the Lighthouse and Watchtower, is already built at Yale University in New Haven, Connecticut, with a 30 mile free-space link across Long Island Sound planned to connect Stony Brook and Yale directly. Past that, the team plans to repurpose the same rooftop telescope infrastructure to track low-earth-orbit satellites, laying groundwork for satellite-based quantum key distribution and a longer-term global quantum network. Funding comes from DOE's Office of Science, the National Science Foundation, and $300 million in New York Empire State Development money tied to Stony Brook's Quantum Innovation initiative.

This ties directly to Chapter 1 of Quantum from the Ground Up, which opens on the fiber problem: how to move quantum information over distance without destroying the fragile state that makes it quantum in the first place. Fiber solved part of that by forcing everything into a wavelength band it can carry efficiently. This link solves a different part by removing the requirement to force anything at all. This post will roll into the next quarterly edition of the book, out September 1. You can read the current edition here.

The Fiber Problem: Why Distance Breaks Quantum Networks

Every quantum network built so far runs into the same wall: how do you move a quantum state across distance without destroying the property that makes it quantum in the first place. That's the fiber problem. 

Diagram shows the photon's path: native wavelength out, up-converted into the fiber band, lossy fiber crossing, down-converted back, with the no-cloning constraint called out separately underneath since it's the reason none of that loss can be patched mid-flight.

Classical data degrades over distance too, but repeaters fix it. A signal gets weak, a repeater reads it, copies it, and sends a fresh copy further down the line. Quantum states can't do that. The no-cloning theorem, a hard result in quantum mechanics, says you cannot create an identical copy of an unknown quantum state. There's no way to read a photon's quantum state, copy it, and pass along a clean version. Doing so destroys the very state you were trying to preserve. Whatever leaves the source has to survive, unassisted, all the way to the destination.

Fiber makes survival harder the farther a photon travels. Standard telecom fiber loses roughly 0.2 decibels of signal per kilometer at the wavelength commercial networks use, a small number that compounds fast over distance. A classical signal can be boosted past that loss. A single photon carrying a quantum state can't be boosted, only lost. Every kilometer of fiber is another chance for that photon to get absorbed or scattered before it arrives.

There's a second problem. Commercial fiber carries light efficiently only in narrow bands, mainly around 1550 nanometers, because that's where glass loses the least light over distance. The atomic systems used to store and process quantum information, trapped ions, neutral atoms, certain solid-state qubits, usually don't emit or absorb light at 1550 nanometers. A photon leaving an atomic memory typically needs a wavelength conversion step just to enter the fiber network, and another one on the far end to be absorbed by the receiving memory. Each conversion is one more place to lose or degrade the state.

You can't clone a quantum state to fix loss along the way, and fiber adds distance-dependent loss plus a wavelength mismatch on top of photons that are already fragile.

In my next post I'll describe how Brookhaven National Labs and Stony Brook University have worked around the fiber "problem."

This is the problem Chapter 1 of Quantum from the Ground Up opens on. You can read the current edition of the book here.

IBM Links Two Quantum Cryostats Toward Fault Tolerance

Chapter 3 of Quantum from the Ground Up makes one argument: the physics works, and the engineering catches up slowly, one specialized system at a time. The chapter leans on the dilution refrigerator as its example, a device that cools a quantum chip down to a few thousandths of a degree above absolute zero. A separate post here, The Chandelier, walked through why that cooling matters. Below a certain temperature, the metal on a quantum chip becomes a superconductor, meaning electricity flows through it with no resistance. That property is what lets a qubit hold information as a quantum state instead of losing it to heat and vibration almost instantly. IBM's announcement on August 19 is the next chapter in that same argument. Instead of one isolated refrigerator, two of them, joined.

What IBM Actually Did

On August 19, IBM announced it had physically connected two cryogenic modules—each its own standalone refrigerator—into a single integrated cold environment. Crucially, this initial milestone served as a structural, thermal, vacuum, and EMI-shielding validation of the empty joined cells, rather than a live multi-QPU execution. IBM plans to install its next-generation Nighthawk quantum processors into these coupled modules later in 2026 to execute live inter-module quantum gate operations.

A cryogenic module in this context is a sealed, vacuum-insulated box that removes heat in stages as you go deeper inside it, ending at a chamber cold enough to keep a chip in its quantum state. IBM reached a base temperature more than 180 times colder than deep space, which works out to below 15 millikelvin, or 15 thousandths of a degree above absolute zero. Getting there took five days at 4 Kelvin, the temperature of liquid helium, before a final drop to that base temperature shortly after. Standalone, each module stands about 8 feet tall and 8 feet wide, closer in size to an industrial appliance or plant assembly than a benchtop instrument.

The two modules IBM cooled down were empty, no chips installed. IBM plans to install Nighthawk quantum processors into the modules later this year, then test whether chips connected across the module boundary can run operations reliably enough to be useful. IBM itself has said that reliability across the L-coupler connection is still active engineering work, not a finished result. This week's announcement is the refrigerator working, not chips talking to each other across it.

Each module is a self contained casing that steps a chip down through three stages: room temperature electronics at the top, a 4 kelvin stage in the middle, then the chip itself below 15 millikelvin at the bottom. The line between the two chip stages is the L-coupler, the connection that lets Module A and Module B behave as one cooled environment instead of two separate refrigerators.

Why Wiring Is the Bottleneck

A quantum chip does not run itself. Every qubit on it needs a wire carrying a control signal in and a readout signal out, and those wires have to pass through every cooling stage without carrying stray heat down with them. That is the actual scaling problem, not the chip. IBM's new module design gives each vacuum enclosure up to 12 times more wiring space than today's most common IBM systems. More wiring space is what will let more chips be wired up and connected, both inside one module and across two joined modules, once IBM installs chips and runs them through the connector it calls the L-coupler. IBM describes the goal as processors reliably working on the same problem together, not just sitting side by side, which is a different requirement than just packing more qubits onto one chip.

Physical Qubits Versus Logical Qubits

This is also a good place to separate two terms I’ve used a little loosely. A physical qubit is one actual quantum circuit on a chip. A logical qubit is a group of many physical qubits, wired together and error corrected as a unit, that behaves like one reliable qubit for the purpose of running a calculation. Fault tolerant means a system can keep correcting its own errors fast enough to finish a long calculation before the errors pile up and ruin the answer.

IBM's roadmap calls for at least 1,000 programmable qubits by 2027 using L-couplers, feeding into IBM Quantum Starling, the system the company expects to deliver in 2029 as the first fault-tolerant quantum computer, running 100 million quantum gates across 200 logical qubits. Two hundred logical qubits sounds small next to 1,000 physical qubits, and that gap is the whole point. Error correction is expensive. However, while standard 2D surface codes typically require an overhead of 1,000+ physical qubits per logical qubit, IBM relies on quantum Low-Density Parity Check (qLDPC) codes. This reduces the overhead to roughly 50 physical qubits per logical qubit (~10,000 physical qubits for 200 logical qubits), making fault tolerance achievable on a substantially smaller hardware scale.

None of that works if the chips cannot be wired together and kept at the same ultra low temperature at the same time. The cryogenic housing announced this week is the part of the plan nobody puts on the cover of a press release, even though Live Science frames it as one of the field's biggest infrastructure bottlenecks. Without a way to link cryostats, a 200 logical qubit machine stays a slide in a roadmap deck.

What This Changes in the Book

Chapter 5 covers IBM's Condor chip: 1,121 physical qubits inside one standard cylindrical cryostat. That chapter treats the cryostat as fixed, one chip, one refrigerator. This announcement breaks that assumption. The box shaped modular design, not the cylinder, is now IBM's stated path past a single chip's qubit ceiling. Chapter 3's engineering argument gets a dated, measured example: five days to 4 Kelvin, then to base temperature, in a system built to add modules rather than grow one tank.

This post rolls into the next edition of the book, due September 1. The current edition is available at gordostuff.com/p/quantum-from-ground-up-hardware.html

A Gas Swap Fixes a Manufacturing Problem in Superconducting Qubits

Chapter 5 of Quantum From The Ground Up covers superconducting qubits: Josephson junctions, the Dolan bridge fabrication technique, and IBM's 1,121-qubit Condor chip running at 99.0 to 99.5 percent two-qubit fidelity. That chapter never asks how the metal underneath those junctions gets onto the chip in the first place. A new paper out of Cornell fixes a problem in that step, and it matters more than another fidelity number would.

A superconducting qubit is a tiny circuit built from metal that, once cooled near absolute zero, behaves like a single quantum object instead of an ordinary wire. The part that makes it a qubit rather than just a very cold wire is the Josephson junction: two superconducting metal layers separated by an insulating gap so thin that electrons tunnel straight through it. Tantalum has become a favorite metal for those layers because tantalum-based qubits hold their quantum state longer than most alternatives. But tantalum only works if its atoms land on the chip in one specific crystal arrangement, called the alpha phase. Getting that arrangement has required heating the substrate past 400°C during deposition. Most semiconductor factories run their fabrication lines with a hard ceiling near that same 400°C, so there was almost no margin between what tantalum needed and what a foundry could tolerate, as Cornell's team describes the manufacturing squeeze.

Cornell's group, led by Assistant Professor Valla Fatemi, builds these tantalum layers by sputtering: a process gas is ionized and fired at a block of tantalum, knocking atoms loose so they land on a silicon wafer and build up a thin film. The standard process gas is argon. Fatemi's team swapped in krypton, a heavier noble gas, and found that the heavier atoms push the tantalum into the alpha phase at temperatures as low as 200°C, half of what argon requires, according to the published results in Nature Materials on August 18, 2026. The resulting films also carried noticeably higher electronic conductivity than tantalum deposited the old way.

Krypton ions hit the tantalum target at the top, knock atoms loose, and those atoms travel down to build the alpha-tantalum film on the heated silicon substrate. Note 200°C, half the temperature argon sputtering needs.

The work builds on an earlier study from Fatemi's lab that sputtered niobium films with argon and mapped out how surface chemistry during deposition shapes final qubit performance. Swapping the metal to tantalum and the gas to krypton let the team apply what that niobium work taught them about controlling film quality. Qubits built from the new tantalum films performed, in Fatemi's own assessment, at the leading edge for the field.

None of this sets a new fidelity record. It removes a bottleneck that sits underneath every fidelity number in the chapter: a qubit design cannot scale to production if the fabrication step it needs falls outside what an ordinary chip factory can actually run.

This result covers one sample film deposited at one lab. Scaling it to production is a separate engineering problem, not a physics problem, and it is the harder one. A foundry running krypton sputtering needs the crystal phase, conductivity, and thickness to stay uniform across every chip on a wafer and across every wafer in a batch, not just in the sample that made it into the paper. It needs the process to hold up next to every other step already running on that line, since real chips stack tantalum with silicon oxide, aluminum wiring, and the Josephson junction itself, each with its own temperature limits and contamination risks. And it needs the equipment: krypton sputtering targets, gas handling, and chamber tuning are not yet standard equipment at most semiconductor fabs the way argon sputtering is. Lowering the temperature ceiling was the physics half of the problem. Building a repeatable, monitored, high-yield process around that lower ceiling, at the volume a real qubit chip production line runs, is the engineering half, yet to be figured out.

What This Changes in the Book

Chapter 5 lists IBM's Condor at 1,121 qubits and 99.0 to 99.5 percent two-qubit fidelity, built with Dolan-bridge Josephson junctions. This post adds a manufacturing footnote to the process. A lower-temperature tantalum deposition route now exists that fits inside standard semiconductor foundry limits. The path to building chips at that quality, at scale, gets wider.

This post will fold into the next edition of Quantum from the Ground Up, due September 1. The current edition is available on the book page.

What Mrs. Anderson’s High School Chemistry Class Taught Me About Units

I found a bunch of posts I started writing years ago but never finished. Here’s one of them.... just finished.

I remember Mrs. Anderson at the blackboard in my high school chemistry class, writing out a conversion problem - something like converting a volume in liters to milliliters to moles. She worked it by stacking fractions, one after another, each one arranged so a unit in the numerator of one fraction matched the unit in the denominator of the next. Then she crossed them out in pairs until only the answer's units remained. "Learn a few equations," she said, "and you can solve just about anything." She was not talking about memorizing formulas. She was talking about what she called factor labeling.

Factor labeling - today more commonly called a more fancy dimensional analysis - treats units as algebraic objects. You multiply and divide them the same way you multiply and divide numbers. A conversion factor like 1,000 milliliters per liter is really just the number one, with some units, so multiplying by it changes the label without changing the quantity. Chain enough of these factors together and the units in between cancel, leaving you with exactly the unit you wanted.

The value of the method is not speed. It is error detection. In high school it helped in chemistry (not my favorite subject). In college physics (loved it) it became something I depended on, once problems started combining velocity, acceleration, force, and energy in the same calculation and a single wrong exponent could hide inside an otherwise reasonable looking number..

Here’s a simple example. In circuit analysis, say a resistor carries 25 milliamps (I) at 12 kilohms ® and you need to figure the power (P) dissipated in watts. Run the raw numbers straight through P = I²R without tracking units and you get 7,500 - off by a factor of a thousand from the real answer. Run it through factor labeling instead: convert 25 milliamps to 0.025 amps and 12 kilohms to 12,000 ohms before multiplying, and the units confirm the answer lands in watts: 7.5 watts.

I have used this same check in electrical engineering courses, in circuit analysis, and in grading student calculations for capstone projects. A wrong answer with clean unit cancellation is rare. A wrong setup almost always leaves a stray unit sitting where it should not be.

Fifty plus years after high school, I always check my units before I trust my numbers. Thanks, Mrs. Anderson!

What Nobody Tells You About AI and Your Water Supply

Last week during a workshop on AI tools in the classroom at Pace University, a faculty member asked about AI and water use. I expected the question. The workshop was about classroom applications, not infrastructure, so I kept the answer short and moved on. Here’s a longer answer.

Data centers consume water directly, through cooling towers that evaporate it to remove heat. US data centers directly consume an estimated 17 to 19 billion gallons a year, a figure projected to climb toward 60 to 110 billion gallons by 2030. The bigger draw is indirect. Generating the electricity these facilities use consumed roughly 211 billion gallons of water in 2023, mostly through cooling at power plants. A data center's water footprint depends as much on how its power is generated as on how its servers are cooled.

Location compounds the problem. Two thirds of new data center construction since 2022 has landed in areas already under water stress. Municipal systems supply nearly all of that water, and the infrastructure upgrades to meet rising demand often fall on residential ratepayers rather than the companies building the facilities. Transparency has not kept pace either. Most operators disclose only partial or aggregate figures, so the public rarely sees facility level numbers.

The fixes already exist - sort of. Closed loop and liquid cooling systems cut water draw at the facility to near zero. Siting near hydro, nuclear, or wind power removes most of the indirect draw tied to electricity generation. The Massachusetts Green High Performance Computing Center in Holyoke, built in 2012 on hydro power from the Connecticut River, has been proving this model for over a decade. But.... scale matters here. MGHPCC runs on a 15 to 19 megawatt connection with about 10 megawatts dedicated to computing. A single hyperscale AI campus now under construction can draw a gigawatt or more, one hundred times that load. Holyoke proves the cooling and power sourcing approach works. It does not prove that approach scales directly to a facility built for AI though.

Regulation is starting to catch up. State legislatures introduced more than 200 data center bills in 2025 and enacted over 40 of them across 21 states. Most of what passed addressed ratepayer protection and siting incentives rather than water directly. Roughly 30 of the proposed bills targeted water consumption specifically, and only a handful of those were enacted. South Carolina and Kansas are considering mandates requiring closed loop cooling. California, Iowa, and Michigan are pushing water use disclosure requirements. Virginia is taking a different route, tying grant funding to the use of reclaimed wastewater instead of potable water in cooling systems. None of this is settled yet. Water legislation is still catching up to where energy legislation was two years ago, and disclosure requirements are starting to follow the path carbon reporting took a decade earlier.

I told that faculty member the honest version this week: the technology to fix this exists, and it works today in Holyoke. Scaling it up is an engineering problem, and engineers solve those. What is missing is the will to require it before the next facility breaks ground.

Post-Quantum Crypto Just Got a Chip to Run On

Chapter 14 of Quantum from the Ground Up covers the hardware side of post-quantum cryptography, meaning new encryption methods designed to survive an attack from a future quantum computer. This week gave that a concrete data point. BTQ Technologies and Taiwan's Industrial Technology Research Institute validated the first phase of a chip architecture built specifically to run the new encryption standards in hardware instead of software.

Why Hardware Matters Here

Encryption, whether old or new, is math. A processor runs that math the same way it runs any other program: by fetching instructions and data from memory, doing the calculation, then writing the result back. Post-quantum encryption methods use larger keys and more complex math than the encryption in use today, so they demand more of that fetch-and-calculate cycle. Running them purely in software, on a general-purpose processor, is slower and draws more power. That is a real problem for a car's onboard computer, a factory sensor, or a battery-powered IoT device that cannot spare the extra milliseconds or milliwatts.

The fix is to build a dedicated piece of hardware that runs only the encryption math, wired directly for that job. That is what a chip architecture like QCIM is.

What QCIM Actually Does

QCIM stands for Quantum Compute-in-Memory. Despite the name, it does not involve quantum computing itself. It refers to where the calculation happens on the chip. In a standard chip layout, memory and the processor are separate blocks connected by a data bus, and every calculation means shuttling data back and forth across that bus. Compute-in-memory design instead performs the calculation inside or immediately next to the memory itself, cutting out most of that back-and-forth. Less movement means lower power draw and faster results, which is exactly what power-constrained devices need if they are going to run demanding post-quantum math.

BTQ built this particular version around three specific encryption standards published by NIST: FIPS 203, 204, and 205. These are the official post-quantum algorithms the US government has approved for general use, covering both encrypting data and verifying digital signatures. Any hardware built to accelerate post-quantum cryptography needs to run these three algorithms specifically, since they are what systems will actually be required to support going forward.

 

What Was Tested, and What It Showed

The test ran inside a TSMC 28-nanometer design environment. TSMC is the world's largest chip manufacturer, and 28-nanometer refers to the size of the transistors in the manufacturing process being simulated, an older and well-proven node rather than the cutting edge, which keeps early testing cheaper and more predictable. Inside that environment, researchers checked two things. First, whether the QCIM core could genuinely speed up FIPS 203, 204, and 205 operations. Second, whether it produced correct results while doing so, since a faster chip that gets the math wrong is worthless for security. Both held up under the demanding conditions BTQ and ITRI put it through. ITRI's Dr. Chih-Cheng Lu called it meaningful progress toward module-level integration, the next phase of the program, where the core gets built into a larger working system rather than tested on its own.

BTQ is not new to this work. The collaboration with ITRI traces back to 2022, and a companion program with South Korea's ICTK is aimed at a fully integrated, commercially deployable chipset built around the same core. BTQ has said it expects to ship QCIM test chips to customers and partners by the end of the year. The target applications read like a list of things you do not want to re-secure one device at a time later: military systems, industrial equipment, automotive platforms, IoT devices, and connected infrastructure generally, all of which stay in service for years and are hard to patch remotely.

What This Changes in the Book

Chapter 14 already covers hardware paths to PQC, citing the SEALSQ QS7001. QCIM adds a second, independently developed example aimed at the same FIPS standards, this time using compute-in-memory design. The next edition adds it alongside QS7001 and tracks the module-level integration phase BTQ and ITRI are moving into next.

QCIM is the third hardware entry in a pattern this blog has been tracking since December: SEALSQ's QS7001, covered in The Quantum Security Race: Software vs. Hardware, and STMicroelectronics' ST54M, covered in Government Sets New Deadline for Quantum-Safe Encryption. All three chips target the same FIPS 203/204/205 standards introduced in Quantum Computers Just Got Much Closer to Breaking Your Passwords, and all three exist because the federal migration deadlines covered in that June post do not leave software-only implementations enough runway.

This post will fold into the next edition of Quantum from the Ground Up, due September 1.

Quantinuum Built a Universal Gate Set With 28 Qubits, Not Hundreds

In my last post I wrote about how I often take my time learning about complex things. This one has been percolating for a while - the first experimental demonstration of switching between two different error-correcting codes to build a universal gate set, using 28 physical qubits instead of the hundreds that brute-force magic state distillation usually needs. Here we go!

Chapter 6 in my Quantum book covers Quantinuum's H2 trapped-ion computer through its gate fidelity, meaning how often a single operation on a qubit comes out correct. That number sits at 99.0 to 99.5 percent, close to IonQ's 99.99 percent result also cited in that chapter. A single clean operation is not the same thing as a working quantum computer. You need to run thousands or millions of operations in sequence, and you need every type of operation a quantum algorithm requires, not just the easy ones. A team from Quantinuum and UC Davis showed a way to get every type of operation working together reliably, using only 28 qubits where earlier approaches needed hundreds.

Let's start with some basics. A classical computer builds everything out of simple logic gates, AND, OR, NOT. A quantum computer builds everything out of quantum gates instead, operations that rotate or combine qubits in specific ways. Some quantum gates are considered easy to protect with error correction. Others are considered hard. That split matters more in quantum computing than it does in classical computing, and it is the whole reason this experiment is worth explaining.

The easy gates are called Clifford gates. Hadamard, CNOT, and the phase gate all fall into this group. Error correcting codes, like the Steane code already described in Chapter 6, were designed around these gates. Run a Clifford gate on a protected, encoded qubit and the code keeps working the way it is supposed to. The catch is that Clifford gates by themselves are not enough. A computer that only ever runs Clifford gates can be copied and simulated by an ordinary laptop. Nothing quantum about the result. To get an actual advantage over classical computing, you need one more type of gate, called the T gate, and it does not play by the same rules. Try to run a T gate directly inside most error correcting codes and the protection breaks down. If you want the fuller story on how trapped-ion qubits and error correction work together on this hardware, Chapter 6's background on QCCD architecture and logical qubits covers that ground.

Rather than force the T gate to behave, researchers build it a workaround. They prepare a separate, specially crafted qubit ahead of time, called a magic state, and use a process similar to quantum teleportation to transfer its effect onto the qubit that needs the T gate. The protected data qubit never runs the risky operation directly. The magic state absorbs the risk instead. The problem shifts from protecting a difficult gate to manufacturing a clean enough magic state in the first place.

Building a magic state clean enough to trust normally means distillation: take several noisy magic states, run a check on them, and keep only the ones that pass. Repeat that over several rounds and the result gets cleaner, but each round eats more qubits. Estimates for reaching Quantinuum's target fidelity through this brute-force approach ran into the hundreds of physical qubits, well beyond what most trapped-ion computers carry today.

Estimated qubit cost of brute-force distillation versus the demonstrated cost of code switching.

Quantinuum's team used a different trick called code switching. Instead of distilling one magic state over and over inside a single code, they built the state inside a 15-qubit code called the quantum Reed-Muller code, chosen because the T gate happens to work cleanly inside it with no extra steps. They then moved that already-clean state into the 7-qubit Steane code, the same code already in Chapter 6, which handles the rest of the gate set. The move works like a teleportation with a built-in check: if the check fails, that attempt gets thrown out rather than trusted. About 17 percent of attempts failed and were discarded, leaving a usable magic state in the remaining 83 percent.

The code-switching pipeline: build the state where the T gate is easy, check it, then move it to the code that runs everything else.

The number that matters most: the finished magic state had an infidelity of about 5.1 times ten to the negative fourth, roughly 2.7 times lower than the error rate of the physical qubits used to build it. In plain terms, the finished, protected result was cleaner than the raw hardware that made it. That is the entire promise of error correction, and no one had shown it for this particular gate before. The total qubit cost was 28: 22 data qubits and 6 more used just to run the checks. The full technical writeup, published in Physical Review X, is worth a look if you want the underlying math.

Quantinuum's newer Helios system has since carried the same idea further. In November 2025 it produced 48 logical qubits from 98 physical qubits, close to a 2 to 1 ratio, using a different checking scheme called the Iceberg code. None of this means a large, code-breaking quantum computer is close. It means the qubit cost of turning noisy hardware into something trustworthy has started to come down, on the same H-series machines Chapter 6 already covers. This is also the same territory last month's post on Quantinuum's topological-qubit workaround explored, using the H2 processor to test a different route to fault tolerance entirely.

What This Changes in the Book

Chapter 6 will get a new section on code-switching magic states alongside the existing H2 fidelity numbers, with the 28-qubit figure and the 5.1x10 to the negative 4 logical infidelity placed next to the 99.0 to 99.5 percent physical gate fidelity already cited, so readers can see the difference between a clean single operation and a clean chain of protected ones. Chapter 12, which covers the overhead problem in error correction, will note code switching as a second qubit-efficient alternative to brute-force distillation, alongside the NVIDIA AI-assisted calibration work already in that chapter.

This post will be folded into the next quarterly edition of Quantum from the Ground Up, due September 1. The current edition is available at the link above.

The Retirement Advantage

This morning I picked up a paper on topological qubits. Read a section, wrote some notes, set it down. Went out on the boat and did a chart plotter software update, thought about Microsoft's error correction claims, saw a dolphin and a manatee, checked my pinfish bait trap, talked to a neighbor, jumped in the pool, came back inside and read another section. No deadline, no meeting after, no student waiting on feedback. Twenty years ago I was directing NSF Centers of Excellence, first at STCC and later at UCF, and that kind of stretched-out thinking didn't exist. Staff, faculty, grant reports, site visits, advisory boards, proposal writing, college administration, students, airplanes, NSF program officers, audits. Fifteen minutes between fires, and quantum mechanics doesn't yield to fifteen minutes.

Growing older costs you some things, but not the ones people assume. I'm at the gym five days a week now and feel as strong as I have in years, so it isn't a story about decline. It's smaller than that: hearing aid glasses now, some unfortunate hair loss, a night on the boat that costs me more the next morning than it used to (I still love it.) 

At close to 70, semi-retired, I get to choose what earns my attention, and I've learned I do my best thinking in chunks now. Read something, write something, set it aside, let it sit while I'm doing something else entirely. Some mornings that means a quantum computing paper worked through in pieces over a week. It happens with my writing too - for example - I’ve been working on this post for a couple of weeks. Other mornings it means running the boat out of Clearwater before the wind picks up, no agenda beyond fish and water. Last week it meant France with the kids and it was spectacular! 

I've also had time to reconnect with people I let drift over four decades of building a career, old friends from K-12 and early consulting years, conversations that don't happen when every hour has a client attached to it.

My career has taught me plenty of technical things. It didn't teach me to work this way though, small pieces, long pauses, letting a hard idea sit until it loosens up on its own. The job never allowed for it. Semi-retirement has.

I’ll finish the topological qubit paper eventually. Not because I'm slower now. Because I finally have the room to work the way my mind actually wants to work, and the time to take my time.

IonQ and EPB Are Testing Quantum Memory on a Live Network

On August 4, IonQ and EPB announced plans for a Chattanooga research center built around quantum memory embedded in a live network. IonQ is committing $15 million over five years. EPB, which built the country's first citywide gigabit fiber network, is supplying the operational fiber for testing rather than a lab bench. The stated goal is to run what the partners describe as the first commercial quantum memory unit operating inside a real telecommunications network, not a simulated one.

Why You Can't Just Amplify a Quantum Signal

A classical repeater works because you can read a signal, clean it up, and retransmit a fresh copy. You cannot do that with a quantum signal. The no-cloning theorem rules out making an exact copy of an unknown quantum state, so you cannot just measure a photon carrying quantum information partway down a fiber and regenerate it downstream. Whatever fragile superposition or entanglement the photon was carrying gets destroyed the moment you try to read it directly. That single restriction is why quantum networking has stayed a laboratory subject for two decades while classical fiber scaled to terabits per second.

Entanglement Swapping: How Quantum Memory Solves It

The workaround is entanglement swapping, and it is where quantum memory earns its name. Instead of relaying one signal over the full distance, you break the link into shorter segments and generate entanglement independently across each one. At teh node joining two segments, a Bell-state measurement on the two local qubits swaps the entanglement outward, so the two endpoints end up entangled with each other even though no photon ever traveled the full path directly. Doing that at scale requires something to hold each segment's entangled state steady while the neighboring segment catches up, since the segments rarely finish at the same instant. That holding function is quantum memory. Without it, the whole chain have to succeed simultaneously across every link, which becomes exponentially unlikely as you add distance. Two further techniques usually ride along with this scheme in a real design. Entanglement purification takes several noisy entangled pairs and consumes them to distill a smaller number of higher-fidelity pairs, trading rate for quality. Multiplexing runs many memory qubits or many frequency and time slots in parallel at each node so that a failed attempt on one channel doesn't stall the whole link while it waits for the next try. Both exist specifically because the base success probability per attempt, discussed below, is low enough that a single-channel, single-shot repeater would be too slow to be useful.

Entanglement swapping through a memory node. Each segment generates its own entangled pair, the memory node performs a Bell-state measurement on its two stored qubits, and the entanglement swaps outward to link the endpoints directly.

Why IonQ Is Betting on Trapped Ions

IonQ's version of this problem runs through trapped ions rather than crystals or atomic vapor cells, which is one of a few competing physical approaches to quantum memory. A trapped ytterbium or barium ion stores its qubit state in hyperfine or Zeeman sublevels of the ground state, energy levels with transition frequencies in the microwave range that are largely insulated from the electric and magnetic field noise that scrambles other qubit types. The same ion can be made to emit a single photon entangled with its internal state through spontaneous emission on an optical transition, which is the interface that lets a stationary trapped-ion memory talk to a photon traveling down fiber. The catch is that an ion radiates in essentially all directions, so a bare setup collects only a tiny fraction of those photons. The standard fix is to place the ion inside an optical cavity, which enhances emission into one preferred mode through the Purcell effect and can push photon-collection efficiency well above what an open microscope objective achieves. IonQ acquired this specific expertise directly: its 2025 acquisition of the Boston photonic-interconnect startup Lightsynq, founded by former Harvard quantum-networking researchers, brought in more than 20 patents covering quantum memory and multi-processor scaling, and that intellectual property is what the Chattanooga center is built to commercialize.

The Real Bottleneck: Entanglement Generation Rate

The number that actually limits this technology is the remote entanglement generation rate, and it is unglamorous compared to headline fidelity figures. Producing one heralded entangled pair between two distant ion nodes today runs on the order of 1 to 10 events per second under good lab conditions, and each attempt only succeeds a small fraction of the time because photon collection efficiency in most published experiments stays under 1 percent. A useful distributed computation or a real communication session needs thousands of these links established in sequence. At 10 Hz that is roughly seventeen minutes just to build 10,000 entangled pairs, before any of the actual computation or communication happens. The Duke-IonQ demonstration illustrates the same constraint at smaller scale: net end-to-end photon collection efficiency across the three nodes ranged from about 0.74 to 1.45 percent, and the ions needd periodic pauses for Doppler cooling between entanglement attempts because recoil from repeated photon scattering heats them out of the trap's ground state. Raising that collection efficiency, largely through better cavity coupling, is the specific engineering problem Lightsynq's patents target.

The Duke-IonQ Demonstration

The lab evidence behind this bet is recent and specific. In June 2026, a Duke University and IonQ team entangled three separately trapped barium-138 ions across independent network nodes, producing a GHZ state, the three-particle entangled resource that distributed quantum computing protocols need, at a fidelity between 84.1 and 88.1 percent. Each node held a single ion in its own four-rod Paul trap, separated by about two meters, with a static magnetic field of roughly 4.24 gauss splitting the qubit's Zeeman sublevels by about 11.9 MHz and defining the two logical states. The nodes were linked through a shared photon-collection setup rather than a direct chip-to-chip connection, and the entanglement generation rate came in at about 0.095 events per second, close to one every ten seconds. To confirm the entanglement was genuine rather than an artifact of the measurement, the team ran a Mermin-inequality test and measured a value of 3.203, above the maximum of 2 allowed by any theory built on local hidden variables and closer to the value of 4 predicted by ideal quantum mechanics. That is a small distance and a modest rate next to what a production network needs, but it demonstrated something the field had not shown before: individually controlled, independently addressable qubits generating multipartite entanglement over a photonic link without relying on local two-qubit gates to mediate it. Earlier three-node demonstrations in other qubit platforms depended on exactly that kind of shortcut, which does not scale to independently operated network nodes.

Testing Distance and Traffic

Distance and traffic are the two variables Chattanooga is built to test what a two-meter lab bench cannot. On distance, EPB's live fiber replaces a bench-top loop with a real metro network spanning real thermal drift, real splices, and real fiber aging. On traffic, a separate July 2026 study out of Northwestern's McCormick School of Engineering showed that entangled photons can share existing commercial fiber carrying 1.6 terabits per second of live internet traffic over 24.4 kilometers while holding 94.2 percent fidelity, which matters because it removes the assumption that a quantum network needs its own dedicated dark fiber to function. Put those two results together and the engineering question shifts from whether quantum memory works in principle to whether it holds up once it is buffering real entangled states against a live network's noise floor instead of a quiet lab bench.

What Chattanooga Isn't Yet

None of this makes Chattanooga a working quantum repeater yet. A full repeater chain needs memory nodes with coherence times long enough to wait for neighboring segments, entanglement generation rates fast enough to be useful, and Bell-state measurement hardware reliable enough to swap entanglement without introducing more error than it removes. The Tennessee center is explicitly an R&D lab, not a deployed product, and IonQ's own five-year funding horizon reflects that. What changed this year is that the missing piece, a memory node that can sit inside a live network instead of a shielded lab enclosure, now exists to be tested.

The Local Context

This is not Chattanooga's first move in quantum. EPB and IonQ already run the EPB Quantum Center, and this new center extends that relationship rather than starting cold. The partners project it will generate two to three times its $15 million cost in wider economic impact and support roughly two dozen jobs, mostly research scientists and trainees.

Why This Caught My Attention

My father was a telephone man. That is what we called him back then. He spent his career climbing poles and fixing lines, keeping copper telephone circuits working in all weather. I grew up around that trade before I ever studied it formally, and I ended up spending my own career on the technology that came after his: telecommunications and networking education. From 1997 to 2014 I was Co-Principal Investigator and later Principal Investigator and Executive Director of the National Center for Telecommunications Technologies at Springfield Technical Community College, an NSF-funded National Center of Excellence built around telecommunications and networking curriculum for community colleges nationwide. From 2014 to 2017 I served as  Co-Principal Investigator at OP-TEC, the National Center for Optics and Photonics Education at the University of Central Florida, another NSF National Center of Excellence, this one focused on the optics and photonics side of the field, which is exactly the physical layer that entangled-photon networking runs on. I also spent 1995 to 2016 as telecommunications faculty and New England curriculum development leader for Verizon's NextStep AAS degree program, building the courses that trained the technicians who kept Verizon's own fiber and copper networks running. Chattanooga's roughly two dozen jobs are a small number next to a $15 million investment, but that ratio is familiar from all three roles. A center like this rarely proves out on job count in its first few years. It proves out on whether the local workforce and research infrastructure are ready when the technology does mature, and whether that head start pulls in the next round of investment. That is also why I still do quantum workforce consulting, including on the Quantum Supply Chain Accelerator with the Massachusetts Technology Collaborative. Watching EPB and IonQ run a version of that same playbook, a regional utility and a specific technical niche, on quantum networking instead of manufacturing or biotech, is what held my attention past the headline. My father spent his career keeping a physical line working between two points. I have spent a good part of mine teaching people how to do the same thing with light instead of copper. This story is the next chapter of that same problem.

What This Changes in the Book

Chapter 1 will get a new section describing quantum memory repeaters as a second, complementary path to the fiber problem, distinct from telecom-native photon emission. The chapter will carry the mechanics above, the no-cloning restriction, entanglement swapping, purification, and multiplexing, alongside the entanglement generation rate as the real bottleneck rather than fidelity alone. It will also note that Chattanooga's live-network deployment is the first commercial attempt to test a memory-based repeater node outside a controlled lab environment, with the $15 million, five-year figure and the Duke-IonQ fidelity and rate numbers carried alongside the existing ytterbium-171 result so readers can see both tracks side by side rather than mistaking one for a replacement of the other.

This post will be folded into the next quarterly edition of Quantum from the Ground Up, due September 1. The current edition is available at the link above.