Quantum Software Gets a Faster Compiler and AI/SI-Generated Circuits

Chapter 12 of Quantum from the Ground Up covers NVIDIA Ising, two machine learning models that calibrate quantum hardware and decode its errors. Chapter 19 names Qiskit, Cirq, and PennyLane as the standard programming frameworks and moves on, which is about the attention most programmers give a compiler until it breaks. The software that turns a quantum idea into something a chip can run gets little space in the book. Two announcements from the past three months change that: IBM’s Qiskit v2.5 release in July, and a generative AI method from Oak Ridge National Laboratory, IonQ, NVIDIA, and the University of Tennessee, Knoxville in September.

A quantum program moves from algorithm to circuit to compiler to hardware. DQAOA-GPT generates circuits in simulation, and Qiskit v2.5 speeds up the compile step. Figures are reported by IBM and IonQ. Source: gordostuff.com

What a Quantum Program Is

Start with what a quantum program is. A quantum program is a circuit: a list of gates applied to qubits, followed by measurements. A gate is a basic operation on one or two qubits. You write the circuit in a framework such as Qiskit, an open-source Python toolkit maintained by IBM. The circuit you write cannot run as written. Each chip supports only a small set of native gates, and on many chips a qubit can interact only with its neighbors. A compiler, which Qiskit calls a transpiler, rewrites your circuit using the gates the chip supports. It assigns the qubits in your circuit to physical qubits and adds extra operations to carry information between distant ones. It also trims the circuit where it can. Every added gate adds a chance of error, so a better compiler produces more reliable answers from the same hardware.

A Faster Compiler in Qiskit v2.5

Qiskit v2.5 improves this layer in several ways. IBM reports typical transpilation speedups of two to three times on its benchmark circuits, and a 115-qubit circuit now routes in about two thirds of the earlier time. Routing is the step that moves information between distant qubits. A new default optimization pass chooses gate decompositions with higher predicted fidelity, the estimated chance a circuit runs correctly. IBM notes that the pass can add gates or depth when that raises the prediction.

Compiling for Fault-Tolerant Machines

The larger change targets fault-tolerant machines, the error-corrected systems that Chapter 6 and Chapter 12 describe. Qiskit now ships two preset compilation pipelines for them. The first compiles to Clifford+T, a standard gate set for error-corrected hardware. Clifford gates are relatively inexpensive to run on such a machine. T gates require magic states, the costly resource behind the Quantinuum work in Chapter 6, so compilers try to use as few T gates as possible. Qiskit lets you adjust synthesis accuracy to trade T-gate count against approximation error. The second pipeline compiles to Pauli-based computation, which expresses a program as a sequence of rotations and measurements built from the three basic Pauli operators, X, Y, and Z. In IBM’s example, the LitinskiTransformation optimization pass cut a phase estimation circuit from 1,354 Pauli rotations to 443. Phase estimation is a common subroutine in quantum algorithms, including the factoring method behind the encryption threat in Chapter 13. Fewer rotations mean fewer expensive operations on a future error-corrected machine.

A New Framework and a New Name

Version 2.5 also adds a framework for compiling through several intermediate representations, which are program formats that sit between what you write and what the chip runs. IBM calls it an early step. The release also renames Qiskit Runtime Service to IBM Quantum Compute Service. The change is naming only, so current documentation may use either name for the same software.

AI/SI-Generated Circuits

The second announcement uses AI/SI to generate circuits. Many useful problems are optimization problems, which ask for the best choice among a huge number of combinations. DQAOA, the distributed quantum approximate optimization algorithm, splits a large problem into smaller subproblems, solves each with a small quantum circuit, and combines the results. Larger subproblems tend to give better answers. Each circuit has adjustable settings called parameters, and the standard method finds good values by looping: run the circuit, measure the result, adjust, and repeat, sometimes hundreds of times. That tuning time limits how large the subproblems can grow.

How the Model Works and What It Showed

The researchers ran the standard method on many sample problems and kept only the near-optimal circuits. They trained a transformer on those circuits. A transformer is the model type behind large language models. This one trained on circuits. At solve time it generates ten candidate circuits per subproblem, simulation scores all ten, and the best one updates the overall solution. The model skips the tuning loop. Circuit-finding time for the standard method rose from about 34 seconds at 4 qubits to over 11 minutes at 12 qubits. The AI/SI method stayed near 28 seconds at every size tested. IonQ reports that answer quality roughly doubled as subproblem size grew on a dense 100-variable Higher-Order Unconstrained Binary Optimization (HUBO) benchmark. The team presented the work at IEEE Quantum Week 2026 in Toronto, and reports say it won a best paper award.

Limits of the Results

Every test ran in simulation. The circuits ran on a single NVIDIA H200 GPU at Oak Ridge using CUDA-Q and cuQuantum, NVIDIA software that simulates quantum circuits on GPUs. Both circuit-generation methods used the same setup, so the comparison covers two ways of producing circuits, with no quantum hardware involved. The authors describe the results as benchmark-scale validation, and hardware runs remain a stated future goal. Both sets of figures in this post come from the organizations that built the tools.

What This Changes in the Book

In the book, Chapter 12 describes the NVIDIA Ising calibration agent as a vision-language model and its decoder as a 3D convolutional neural network, both machine learning tools applied to quantum data. DQAOA-GPT adds a transformer that generates circuits, which extends the same pattern to the programming layer. Chapter 18 lists machine learning model fine-tuning among the skills a candidate can bring without a physics PhD, and training a transformer on a library of good circuits fits that description. The Chapter 18 list also gains an entry: compiler and software tooling development. The Qiskit 2.5 work involves multithreading, a C interface, and compiler pipelines, all conventional software engineering. Chapter 19 names the computer science bachelor’s degree as the main path into quantum software, and the compiler work above is computer science.

This post will be folded into the next quarterly edition of Quantum from the Ground Up. Every edition of the book is available as a free download.

Frank Snyder, December 1939

This is the second part of a story. It is best to read Uncle Al's Knife first, and then this post. That post tells how I found my great-uncle Al's Swiss Army knife. Al's brother Frank was my grandfather. I wrote about Frank's son, my father, in Lessons From My Father's Life.

Frank Snyder was 34. He was a machinist at the Tryco Products Company in Westfield, Massachusetts. He lived on Eldridge Avenue with his wife, Emily, and their two sons. Vernon was 10. My father, Gordon, was eight.

My father remembered Frank's voice from a day in traffic, when he was five or six. A big car came roaring past every car, in the wrong lane. Frank said something like, 'Look at that crazy driver go. Oh my God, it's Al.'

I recall seeing paperwork that said Frank was born in Germany. I do not know when he came to the United States, or how. His parents, Julius and his wife, lived on Park Street, near the river. Frank had five brothers and three sisters.

On the eleventh of December, 1939, Frank spent most of the afternoon on the Westfield River in a boat with his brother August, who was 22. My family called him Augie. They gathered driftwood and new lumber that floated down from the dam at Woronoco, past their parents' home. My family said they gathered it to burn, to keep warm.

Rain had swollen the river. Near the Great River Bridge the water poured over the remains of the old Horton dam and spun into whirlpools among blocks of cement and granite. The brothers turned for shore by the Lynch Lumber Company yard. August stepped out. As Frank was about to follow, his foot slipped and the boat rolled over. The paper said he apparently did not cry for help. In a second or two he was gone. The river threw the boat against the rocks and shattered it.

August ran for help, and no one he met could give any. Many families had no telephone at home in 1939. He kept running, across the bridge and down the south bank, nearly a mile, to Park Street. His brother-in-law, Daniel Reader, drove him to police headquarters. August was so shaken he could barely tell the police what happened. My father, who was eight that day, later spent 33 years with the telephone company.

The acting police chief took charge. The firemen supplied spotlights. The police dragged the river for nearly two hours while three or four hundred people lined the bridge and the bank behind the lumber yard. August and an officer rowed downstream and found the boat a quarter mile from the bridge. They towed it to shore near the end of Fowler Street. They never found Frank.

The search went on the next morning. The police told residents to stay off the river, where the current was swift, and to report any clothing they saw. The headlines said 'Westfield Man Believed Lost in High River' and then 'Snyder Body Unrecovered.' Police, Boy Scouts, and volunteers combed the banks for several miles below the dam. In June, a badly decomposed body was found in the Connecticut River near Glastonbury, Connecticut. The police checked it against Frank's description. Other bodies came out of the river over the years. None was ever identified as Frank. Three years after the drowning, a newspaper wrote that the river still held the secret of where he was.

Emily was a widow with two small sons. The Depression was still on, and then came the war. They had a small chicken farm. It gave them just enough to survive. I do not know whether Emily received any public aid.

When I was a kid I saw a photograph of Frank on my grandmother's bureau, in her bedroom. I do not know where it is now.

Frank's brothers Al and Pooley, who owned the junkyard in Pottstown, never talked about it. Augie never talked about it either, and he was the one in the boat. I wish I had asked.

One day in December 1978 I talked with my grandmother. She told me that time of year brought back memories. When I got home I wrote down what she said. I no longer have the notes. Then I sat at a microfiche reader at UMass and found the newspaper articles. I gave them to my father. He saved them. Save everything was the plan in our family. It came from the Depression. I found them in a folder in my parents' house. The clippings were laid out together on one page.

Uncle Al's knife is in my safe. I have a note with it that says whose it was. The river kept Frank. My father kept the articles. Someone kept the knife sharp for years. Now I have to keep all of it.

Uncle Al’s Knife

I found it in a box in my parents’ house. We were clearing the house out. The box had no label. Inside were old keys, a flashlight, and a roll of electrical tape. The Swiss Army knife lay at the bottom.

The case was worn. Someone had scratched “AL” into it. The blades were sharp. Someone had sharpened them often.

It was Uncle Al’s. Al was my father’s father’s brother. His given name was Adam. He and his brother Pooley owned a junkyard in Pottstown, Pennsylvania. After they “retired” they still lived at the junkyard. In winter they kept a trailer in New Port Richey, Florida. In college I drove over one day on spring break to see them. We grilled hamburgers outside the trailer. I think he had the knife in his pocket then.

Twice a year Al and Pooley drove up to Massachusetts to fish with my father, two brothers and me on the Cape. At first they drove together. Pooley died around 1980. After that Al drove alone from Pennsylvania in an old Dodge Dart. A bar ran across the back seat and his clothes hung from it. I looked at them and thought he lived in the car. His hands were stiff with arthritis. He said he drove with two fingers.

We fished from my father’s 16-foot Starcraft. We caught bluefish and striped bass. The bluefish ran hard and the drag sang. The striped bass pulled heavy and steady. In a boat that size you felt every fish through the hull. At night they told stories.

I was young and I did not listen well. I watched the rods and the water and waited for the next fish. Al and Pooley talked and I heard some of it. Now I wish I had heard all of it. Maybe he told more than I remember. Maybe he told everything and I was somewhere else.

My father listened. He read a eulogy at Al’s memorial service, and the stories were in it.

Al got his chauffeur’s license in 1914, at 16. In 1918 he drove to Cape Cod on dirt roads. The trip took 26 hours. He had 12 flat tires and fixed every one himself, on the road. He raced cars. In World War I the government wanted race car drivers to train as pilots. Al volunteered, and so did a friend. The doctors found a hernia. They told him to have it fixed and they would take him. Al said no way. He never had it fixed. His friend was killed.

My father wrote that Al could talk to fish. Al caught the most and the largest, and it upset Pooley. In his last years Al could not stand to cast. He sat in one of the rear-facing seats and told my father where to cast, then took the rod to reel in. A moment later you heard “Fish on.” Everyone else got skunked. I think he had the knife in his pocket then too.

My father also read that he had lost an uncle who treated him like a son. He treated Al like a father. Al died at 93.

I never met my grandfather. His name was Frank Snyder. He was Al’s brother, and Pooley’s, and August’s.

Next: Frank Snyder, December 1939

Spectrum Still Needs a Network

In 2007, I tracked the 700 MHz auction on this blog. Dish Network appeared on the bidder list, and a Credit Suisse analyst questioned whether the band suited Dish. The FCC closed Auction 73 in March 2008 with about $19.6 billion in winning bids. Verizon and AT&T spent $16.3 billion of that. Verizon took the largest nationwide block with a $4.74 billion bid, which topped Google's $4.71 billion by $30 million. A Dish Network subsidiary won 168 licenses in the E Block for $711.9 million. Dish is now part of EchoStar.

On October 9, the same day I published "Walls Are the Hard Part of Phone Service From Space," John Saw, T-Mobile's chief technology officer, published a LinkedIn article titled "Spectrum Alone Doesn't Make a Network." He wrote it after SpaceX agreed to acquire Grain Management's nationwide 800 MHz portfolio, pending FCC approval. The companies did not disclose terms. Grain reportedly sought about $6 billion in August, and the Wall Street Journal valued the deal at about $8 billion. T-Mobile sold those licenses to Grain, and T-Mobile now competes with Starlink Mobile. Read the article with that in mind. The engineering claims are checkable, so they follow after some background.

T-Mobile and SpaceX have worked together. T-Mobile's T-Satellite service runs on Starlink, and T-Mobile's one-year exclusive agreement is ending. On its August 5 earnings call, SpaceX said it will build out terrestrial networks with its EchoStar spectrum and expects Starlink Mobile to win customers from Verizon, AT&T, and T-Mobile. T-Mobile executives have told analysts that satellite service carries about 0.0002% of network usage. T-Mobile, Verizon, and AT&T have since announced a satellite joint venture, and T-Mobile removed the Starlink name from its T-Satellite webpage. The carriers say existing satellite agreements stay in place, so the two companies are partners and rivals at once.

According to Wccftech's report, Saw makes three technical points. First, the Grain block is about 14 MHz of paired spectrum. Starlink Mobile would hold roughly 80 MHz in total, some of it uplink only, while T-Mobile holds nearly 400 MHz of low-band and mid-band spectrum. Second, a satellite hundreds of kilometers up faces about 50 dB more path loss than a cell site 1 km away, and moving from 2 GHz to 800 MHz recovers only a few dB. Third, one satellite beam covers an area that hundreds of terrestrial sectors serve. Millions of simultaneous users streaming video and making calls need that density.

The path loss figure holds up. Free-space loss at 2 GHz is about 153 dB at 550 km and about 98.5 dB at 1 km, a gap of roughly 55 dB. The next-generation satellites the FCC approved would orbit at 326 to 335 km, which narrows the gap to about 50 dB at 330 km. The same formula gives about 8 dB between 2 GHz and 800 MHz for equal antenna gain. The bigger benefit of the lower band is less loss through walls and foliage, which free-space math omits. That benefit explains why SpaceX wants the block. Yesterday's post covered the loss at the wall. This post covers the loss before the signal reaches the wall, and the two add together. Saw addresses link budget and capacity. SpaceX addresses indoor coverage. Both can be correct.

Free-space path loss for a cell site and for satellites at 550 km and 330 km, at 2 GHz and 800 MHz. Calculated at equal antenna gain.

Open items remain. SpaceX says most existing phones already support the 800 MHz band. Starlink's current service uses standard LTE signaling from satellites that carry LTE base station payloads, with Doppler and timing compensation applied on the satellite, so unmodified phones connect as they would to a terrestrial tower. Chipset support matters in two cases. The first is the 2 GHz EchoStar spectrum, which overlaps the 5G NR NTN band n252 that commercial phone chipsets do not yet include. The second is a move to the native 3GPP NR NTN standard, which Starlink's current system does not follow. On October 6, the FCC approved 15,000 next-generation satellites for Starlink Mobile in a partial grant. The approval adds capacity to the system Saw describes.

The 2008 bidder list showed who had money. In 2025, after the FCC questioned its spectrum use and SpaceX called the holdings underused, EchoStar agreed to sell its AWS-4 and H-block licenses to SpaceX for about $17 billion and its 600 MHz and 3.45 GHz licenses to AT&T for about $23 billion. Saw has the title right. EchoStar held the licenses, and the buyers owned the networks.

SpaceX once let its boosters fall into the ocean after a single flight. It now lands and reuses them. Saw lists real obstacles: path loss, capacity, and chipsets. I expect SpaceX to work on those the way it worked on boosters.

Correction: Yesterday's post listed two Grain prices without explaining them. Grain reportedly sought about $6 billion, and the Wall Street Journal valued the deal at about $8 billion.

Walls Are the Hard Part of Phone Service From Space

This post covers frequency, walls and satellites. The companies appear because they hold the licenses, and the physics works the same for every owner.

Key Summary Takeaways

·      Low Band Passes Through Walls: Lower-frequency signals lose less energy in drywall, brick, and glass than higher bands.

·      Distance Weakens Satellite Signals: A satellite signal travels roughly 480 to 550 kilometers to reach your phone, and a wall takes more of it.

·      Spectrum Deals Show the Stakes: SpaceX agreed to pay about $17 billion for EchoStar's licenses, and Grain Management reportedly wants about $6 billion for its 800 MHz licenses.

·      Expect Coverage Gains First: Rural homes, trails, and open water gain the most, with text and voice arriving before fast data.

In August 2007, I wrote that the FCC's 700 MHz spectrum drew heavy interest because it travels long distances and passes through walls. The auction closed in March 2008 with $19.6 billion in bids. Dish Network won 168 licenses for $712 million, and a Credit Suisse analyst questioned whether the band suited Dish at all. Nineteen years later, the same property is why a rocket company is shopping for spectrum.

Start with how your phone service works today. A cell tower has antennas that send and receive radio signals. Your phone connects to the closest tower, and the tower connects to the carrier network over fiber. Each carrier holds FCC licenses for specific radio frequencies, and that set of frequencies is the spectrum. A tower covers a limited area, so carriers build thousands of them. Where no tower exists, or where a storm knocks out its power, your service stops.

Frequency decides how a signal behaves. Low-band signals, generally below 1 GHz, travel farther and pass through drywall, brick, and glass with less loss. Mid-band and high-band signals carry more data but cover less distance and struggle with walls. A low-band signal can reach the middle of your house. A high-band signal can drop at the front door.

Phone service from satellites has a distance problem. A Starlink satellite orbits roughly 480 to 550 kilometers up and crosses your sky in minutes. Its signal arrives weak, and your phone answers with a tiny antenna and little power. One satellite also covers a large area, so many phones share its capacity. Satellite texting on current phones already works this way, and it asks you to point the phone at open sky. A wall takes the rest of the signal.

That is why 800 MHz matters. T-Mobile sold its 800 MHz licenses to Grain Management in a swap that closed in August 2026. On October 8, 2026, SpaceX announced a definitive agreement to acquire Grain Management's nationwide 800 MHz spectrum portfolio. The deal still needs FCC approval, and SpaceX did not disclose terms. The Wall Street Journal reported a price of $8 billion. In 2025, SpaceX agreed to pay about $17 billion for EchoStar's licenses, and Boost Mobile customers get access to Starlink service as part of that deal. AT&T, Verizon, and T-Mobile shares fell 4 to 5 percent that day.

Here is what it could mean for you. Coverage reaches places towers do not: rural homes, trails, and open water. Service continues when a storm takes down local towers. Expect text and voice first, with data slower than a tower provides, because satellites share capacity across wide areas. You will need a phone that supports the band. Many current phones already support nearby 800 MHz bands, but check yours. You will also need a plan that includes satellite access. Whether competition lowers your bill is unknown. Cities will still run mostly on towers.

In January 2008, I wrote about public safety networks that survive disasters by not relying on a single network. Satellite coverage on an ordinary phone adds another network to your phone without adding another device.

The physics in my 2007 post still holds. The 700 MHz buyers were carriers building towers. Some of the next buyers build rockets.

A Quarterback Weighs In on the Moon

Drake Maye is 24 and starts at quarterback for the New England Patriots. This week he went on a radio show and said he was "not really sure 200 percent if we went there or not", referring to the Moon. He added that he took a college class on conspiracies and feels a little skeptical.

Apollo put twelve astronauts on the Moon across six missions between 1969 and 1972. They brought back 2,196 samples weighing 842 pounds, which researchers still study. Observatories have bounced lasers off reflectors the astronauts left to measure the Earth-Moon distance to about 3 centimeters. In 1977, a Soviet radio telescope picked up the transmitters the astronauts left and found their positions matched NASA's reports. NASA's Lunar Reconnaissance Orbiter has imaged the Apollo 12, 14, and 17 sites, and India's Chandrayaan-2 orbiter photographed the Apollo 11 lander in 2021.

Maye can hold any opinion he likes. A 12-year-old who idolizes an NFL quarterback hears an adult they admire treating settled evidence as an open question. That same kid sits in a science class where a teacher is working to build the habit of checking evidence first.

Our country needs more engineers, technicians, and scientists. Students decide early whether math and science belong to them. Every public figure who shrugs at evidence gives a student one more reason to shrug. Celebrities have reach, and reach carries a duty to look something up before speaking.

Maye's conspiracy class presumably covered how to weigh evidence. The Moon landing makes a good practice problem, and one call to any physics department would settle it in ten minutes. The reflectors are still on the Moon, and the physicists still have the measurements.

Fermilab Study Ties Qubit Performance to Nanometer-Scale Fabrication Details

How Microscopic Manufacturing Features Impact Quantum Computing

Key Summary Takeaways

•       Averages Hide Weak Links: Quantum processors are bounded by their weakest qubits, making consistency across a chip essential.

•       Nanometer Precision Matters: Features as small as 1 nanometer in oxide thickness can dramatically impact qubit performance.

•       3 Microscopic Culprits: Surface oxide thickness, sidewall edge slopes, and substrate trench depth directly dictate energy loss.

•       Faster Screening: Advanced techniques like terahertz nanoimaging allow manufacturers to catch defect issues before cooling chips to absolute zero.

Overview

When tech companies talk about quantum computers, they usually share average performance metrics—such as two-qubit gate fidelities between 99.0 and 99.5 percent. However, averages hide a critical challenge in quantum engineering: on any given quantum chip, some qubits perform exceptionally well while others perform poorly. Because a quantum computer is only as strong as its weakest link, understanding why that variation occurs is essential for scaling up functional processors.

A groundbreaking study conducted at Fermilab's SQMS Center (Superconducting Quantum Materials and Systems Center) examined the spread behind these averages. The researchers traced performance differences directly to microscopic physical features measured in single nanometers—a scale tens of thousands of times smaller than a single strand of human hair. The peer-reviewed findings were published in Applied Physics Reviews based on collaborative research involving Northwestern University, Rigetti Computing, NIST, and Ames National Laboratory.

How a Transmon Qubit Works

The most common type of superconducting quantum bit is called a transmon. You can think of a transmon as a tiny electrical circuit made from a thin metal film (typically niobium) laid out on a base chip made of silicon or sapphire. The circuit consists of two primary components:

•       Capacitor: Stores electrical energy across metal pads patterned on the surface.

•       Josephson Junction: A razor-thin insulating barrier sandwiched between two superconductors. This junction gives the circuit non-linear, unevenly spaced energy levels, allowing engineers to isolate the two lowest energy states as the 0 and 1 of a qubit.

To function properly, the chip operates inside a dilution refrigerator at millikelvin temperatures (near absolute zero), where the metal loses all electrical resistance. Microwave pulses are then sent into the circuit to move the qubit between state 0 and state 1.

Understanding the T1 Lifetime & Energy Leaks

A fundamental metric of qubit quality is its T1 lifetime (energy relaxation time). This measures how long a qubit can remain in its excited state ('1') before releasing its energy and dropping back to the ground state ('0'). Because every quantum logic operation takes a set amount of time, a longer T1 allows a quantum computer to execute longer, more complex algorithms before stored information disappears.

A transmon's electric field is not completely confined inside the clean metal pads; it extends slightly into the chip surface, the underlying substrate, and the surrounding air. Energy stored in clean, defect-free material returns safely to the circuit. However, energy stored in surface materials with atomic defects gets absorbed and turned into heat or stray vibration. Engineers call this share of field energy sitting in lossy material the Energy Participation Ratio—and lower participation in lossy material means a longer-lasting qubit.

The Three Microscopic Culprits Behind Variance

By testing 22 transmon qubits across 7 multi-institution chips using a rigorous blinded protocol, the study identified three distinct manufacturing features that control how much energy leaks away:

1. Surface 'Rust' (Niobium Oxide Layer Thickness)

When niobium metal is exposed to ambient air during manufacturing, it naturally reacts with oxygen to form a thin surface oxide layer (primarily Nb₂O₅) a few nanometers thick. This oxide layer is naturally disordered and contains atomic defects that trap microwave energy. The study revealed that a variation of just 1 single nanometer in oxide thickness caused a noticeable difference in qubit lifetime, surprising even experienced chip fabricators.

2. Edge Slope (Sidewall Geometry & Footer Angle)

When chemical etching carves the metal film into qubit circuit patterns, it leaves an edge profile. The researchers compared sloped, tapered edges (about 30-degree angles) against near-vertical, sharper edges (10 to 15 degrees). Sloped edges increase the physical surface area where lossy oxide forms, exposing 20 to 30 percent more of the qubit's electric field to defective surface material and significantly lowering performance.

3. Moat Depth (Substrate Trenching Saturation)

To reduce electric field concentration at the chip surface, manufacturers etch shallow trenches into the substrate beside the metal pads. The study found that digging even a shallow trench under 20 nanometers produces an immediate, dramatic boost in qubit quality. However, once trench depth exceeds 70 nanometers, the benefit flattens out and saturates. Beyond 70 nm, deeper trenching no longer helps, and the sidewall edge angle takes over as the dominant factor driving energy loss.

Diagnostic Proxies: Testing Without the Deep Freeze

Cooling a quantum chip down to millikelvin temperatures inside a specialized dilution refrigerator takes days of setup and substantial operational expense. One of the study's key practical breakthroughs was identifying two room-temperature or mild-cold diagnostic tools as effective predictors of qubit quality:

•       Terahertz Near-Field Nanoimaging (s-SNOM): Maps localized dielectric loss and oxide non-uniformities at nanometer resolution.

•       Magneto-Optical Imaging: Screens local superconducting transition properties across the metal film.

By using these proxy techniques as a screening step on the cleanroom floor, fabrication teams can receive rapid feedback on each etch and oxidation step—catching bad chips before spending time and money on full cryogenic testing.

What This Means for Future Quantum Hardware

To scale up quantum processors from dozens of experimental qubits to thousands of fault-tolerant logical qubits, manufacturing consistency is everything. Eliminating nanometer-scale variations during chip fabrication will allow every qubit on a processor to perform like the best qubit on the chip.

An expanded section covering these device-to-device yield limits and nanometer-scale fabrication parameters will be included in Chapter 5 of the December 2026 third edition of Quantum from the Ground Up. You can download current book editions here.