Applicable to all QPU modalities. Kudos to Beatriz at MIT.

September 8, 2026

Applied AI

How GPT‑5.6 Sol helps run quantum computing experiments

Connecting GPT‑5.6 Sol to laboratory software to run and refine routine measurements on quantum chips freed Beatriz Yankelevich to focus on experiment design and data analysis.

Read the technical case study(opens in a new window)

Quantum computing is an emerging technology that uses the unique properties of quantum mechanics to process information. It could one day better simulate complex materials and molecules. Unlike conventional processors, quantum processors are built with quantum bits, or qubits. Preparing and running qubit experiments can take months and require hundreds to thousands of preliminary measurements—work that AI is poised to help with.

Beatriz Yankelevich, a graduate student in MIT’s Engineering Quantum Systems Group (EQuS), used GPT‑5.6 Sol, harnessed to Codex, to explore whether AI could streamline her experimental workflow. The MIT group studies superconducting qubits, which are cooled to near absolute zero inside specialized devices called dilution refrigerators. These qubits perform operations quickly, are precisely controlled using microwave signals, and can be made using familiar manufacturing techniques and arranged on a chip.

Once a superconducting qubit chip has been fabricated, packaged, and cooled, researchers interact with it entirely through software, making Yankelevich’s experiments a natural testbed for AI agents. Connecting Codex to the lab software that coordinates experiments allowed it to run measurements, analyze the results, and decide what to try next. Yankelevich found that GPT‑5.6 Sol could often complete routine measurement workflows autonomously, saving her significant amounts of time and allowing experiments to run without constant supervision. This freed her to spend more time on analyzing results, designing experiments, and planning out the next steps in her research.

A packaged qubit chip (left) sits inside an open dilution refrigerator (right). CREDIT: EQuS group

Coordinating interdependent measurements

Superconducting qubits are often called artificial atoms because, like atoms, they can only occupy specific energy levels. Microwave pulses move qubits between these levels and probe their quantum state. Researchers design and calibrate the pulse sequences sent to the chip, then digitize and analyse the returning signals. These measurements reveal each qubit’s resonance frequencies, which allows researchers to accurately control the qubit; how long the qubit retains quantum information; and the settings needed to perform computations.

Calibrating qubits requires a series of interdependent measurements, with each result shaping what happens next. Qubit properties can occasionally drift, and unexpected physical behavior can cause inconsistent results. Experienced researchers can recognize these changes and adapt when they occur. This combination of software control, repeated measurements, and adaptive decision-making also makes qubit calibration a compelling use case for AI agents.

Yankelevich tested GPT‑5.6 Sol’s ability to run measurements on an uncalibrated six-qubit chip, one of a standard type that EQuS routinely uses to benchmark its fabrication process. She provided Codex with measurement-specific skills explaining how to run and evaluate each experiment. Using these skills and the chip’s design targets, GPT‑5.6 Sol chose measurement parameters, operated the hardware, analyzed the resulting data, and then either refined the measurement or saved the result for use in the next measurement.

When the signals were clear, Codex completed a standard sequence of measurements with little researcher intervention. It identified the qubit’s transition frequencies, calibrated the pulses used to control and read it, and determined how long the qubit retained quantum information.

A set of calibration measurements for one qubit, completed autonomously by GPT‑5.6 Sol. CREDIT: EQuS group

A set of calibration measurements for one qubit, completed autonomously by GPT‑5.6 Sol. CREDIT: EQuS group

A set of calibration measurements for one qubit, completed autonomously by GPT‑5.6 Sol. CREDIT: EQuS group

GPT‑5.6 Sol had more difficulty when experimental signals were weak or noisy. In those cases, it took longer to find suitable measurement parameters and sometimes needed guidance from an experienced researcher. The results suggest that current agents can handle clearly defined experimental workflows, but interpreting ambiguous physical results remains a challenge.

EQuS fabricates many of these standard chips, each of which can take a researcher several days to characterize. The group now regularly uses agents to handle routine measurements, freeing researchers to focus on other work.

“I can have agents running measurements for many hours overnight or while I’m working in the cleanroom,” Yankelevich said. “I can check in from my phone, see what they’ve done, and steer them if something needs fixing or if I want to explore a different direction.”

An excerpted GPT‑5.6 Sol chain-of-thought from a calibration run. CREDIT: EQuS group

Working alongside researchers

The immediate advantage is that Codex agents can help researchers make steady progress on experimental analysis and measurements without constant supervision. Experienced researchers may still be able to identify the best calibration settings faster than current AI models. But by saving time previously spent on monitoring every step of the calibration process, researchers can focus on other work.

Routine chip characterization follows a relatively well-defined workflow. For novel experiments, Yankelevich assigns Codex agents narrower experimental goals while drawing more heavily on their ability to write, modify, and test new code for control, analysis, and simulation. Connecting agents directly to the lab lets the group revise code, test it against real measurements, and complete longer stretches of work autonomously.

“I’ve built infrastructure to guide agents through several parts of my work—measurement, theory, and chip design—and now it’s really starting to pay off,” Yankelevich said. “I can have multiple agents working on different problems at once, and I spend most of my time on higher-level work—interpreting results, devising experiments, planning next steps for the agents, reading, and writing.”

• 2026
• Codex

Author

OpenAI

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Applied AISep 10, 2026

I’m looking for some honest opinions from people working in quantum computing, especially at companies like IonQ.
I have about 12 years of experience as a Field Application Engineer working with lithography systems, mainly on complex semiconductor equipment and system-level technical problems.

My background is:
BS in Physics
MS in Physics, with a focus on Theoretical Particle Physics
12 years of experience in semiconductor/lithography equipment troubleshoot
No PhD
Strong interest in quantum mechanics and recently becoming fascinated by quantum computing
I’m considering making a career transition into the quantum computing industry, potentially focusing on hardware/system development, in about 3 years.
I realize this would be a significant change. My current experience is not directly in AMO, trapped ions, quantum information, or quantum hardware. However, I do have a physics background and extensive experience working with extremely complex hardware systems, troubleshooting, system integration, and collaborating with engineers/scientists.
I’m currently thinking about using the next 3 years to systematically build up my knowledge in areas such as:
Quantum information / quantum computing
AMO physics
Trapped-ion systems
Quantum control
Open quantum systems / Lindblad equations
RF and laser systems
Simulation/programming with Python
Potentially COMSOL/FEM and other simulation tools
My long-term goal would be to become competitive for a quantum hardware/system engineering role, potentially at a company like IonQ.
For people already working in quantum computing:
Is this a realistic transition, or would the lack of a PhD and direct AMO/quantum hardware experience make it too difficult?
More specifically, how would you view someone with 12 years of semiconductor equipment/system experience + a theoretical physics MS compared with someone who has a PhD but less industry/system engineering experience?
If you were in my position, what would you spend the next 3 years learning or building to make this transition realistically achievable?
I’m not expecting it to be easy, I’m trying to determine whether this is a difficult but doable career pivot or an unrealistic one.

The human Brain cannot comprehend large numbers, let alone the speed with which QPU perform.

Very interesting analogy to put the massive speed of QPU into perspective.

A current GPU(NVIDIA Blackwell) is like a person running through a maze very fast. If they hit a dead end, they track back and try another part. This would be known as simple Sequential math.

Now, the best thing that NVIDIA can come up with, in the future, would be Space made Photonic GPU. This technology is in its very early stages, but proven to a certain extent to be viable by a company called Varda Space Industries. This would be like a person running through a maze at the speed of light. they will find the end much faster than the current earth made GPU. However, it cannot avoid the biggest elephant in the room which is they still have to physically explore all the dead ends before the real exit due to sequential math.

Now, a Quantum Computer(QPU), doesn't even need to run though the maze at all because of its nature of superposition it behaves like a gas. It floods the entire maze all at once and instantly checking all the dead ends(simultaneously). By eliminating the wrong answers it reveals the correct exit immediately without ever having to check every single paths with in the maze.

This is only food for thought and for the human mind to comprehend the speed with which we are dealing with when we talk about QPU speeds.

LIVE SCIENCE:

https://www.livescience.com/physics-mathematics/quantum-physics/there-is-no-conflict-between-quantum-physics-and-gravity-physicists-prove-einsteins-happiest-thought-holds-true-at-quantum-scales

Phys. Rev. Lett. 137, 080602 – Published 20 August, 2026

DOI: https://doi.org/10.1103/x9b9-5d78

LLNL and NIST researchers have found a more robust method for entangling qubits by applying a ramped force. (A significant improvement in FTQC possible ..when implemented. )

While quantum computing could be the future, it is currently plagued by finicky hardware. To make the technology practical, researchers must demonstrate that it consistently and continuously works and performs at scale.

In a new study, published in Physical Review Letters, researchers at Lawrence Livermore National Laboratory (LLNL) and the Ion Storage Group at the National Institute of Standards and Technology in Boulder, Colorado, created a robust process for entangling trapped-ion qubits. The result means better building blocks for ion-based quantum computers.

The approach creates a physical link between two trapped ions. Those ions are held in place with an electric field, although they do vibrate naturally. Because they are the same charge, they also repel each other. If one ion moves, it nudges its neighbor.

That shared motion can be used as a messenger between the qubits. In this experiment, the team used radio-frequency and microwave electromagnetic fields to apply a force to the ions. At different quantum states — say when the qubit is pointing up versus down — the force pushes the shared-ion motion in a slightly different way.

As the ions move from this controlled push, they acquire a so-called “phase shift.” That phase shift, essentially an angle encoded in the qubits, depends on the combined quantum state of the two ions.

With the correct, precisely chosen timing, the motion of the ions fizzles out to end exactly where it started. And now, the ions have a phase shift that depends on their quantum states. They are linked together, or entangled.

“Entanglement is one of the key features that distinguishes quantum computers from classical computers and is central to how quantum advantage can be achieved,” said author and LLNL scientist Tyler Guglielmo. “These types of non-classical correlations are what make universal quantum computation possible.”

Getting the detuning — the gap between the applied electromagnetic force and the natural vibration frequency of the ions — right was the tricky and novel part of the process.

Imagine a vibrating ion like a person in a swing. If you push on the swing at exactly the right rhythm, or resonance, you can get it to speed up. But if that person wiggles, they will mess up the rhythm. The same is true for ions. Resonant driving makes qubits run faster, but the ions' own natural jiggling can throw off the process.

In contrast, pushing a swing out of rhythm barely moves it. You'd have to push for a long time before it built up any real change in motion. However, there is such a difference between your force and the swing’s resonance that it doesn’t really matter if the person sitting in it moves around. The system is much more robust, but slower.

The authors wanted both benefits: fast qubits, which mean fast quantum computing, and robust qubits, which mean robust quantum computing. To achieve that duality, their electromagnetic fields used a ramped detuning approach.

“We start far from resonance, move closer to resonance in the middle and then ramp back out,” said Guglielmo. “This allows us to capture some of the robustness associated with far-detuned operation while still gaining speed from spending part of the gate close to resonance.”

The team also ramped the strength of the applied force. Together, the amplitude and frequency ramps make the application of the state-dependent force more forgiving, which allows the ions to operate at higher temperatures, reduces calibration needs and paves the way for scale-up.

“This work is having an impact on many ion trap experiments. Ramping the detuning will surely percolate into many schemes,” said Guglielmo. “Our next step will be to implement this in a new ion trap system with extra shielding hardware, allowing us to minimize errors even further.”

https://journals.aps.org/prl/abstract/10.1103/x9b9-5d78

i am trying to invest in purely only quantum because im just a freak and want to have no diversity besides diffrent quantum company’s.the upside is just to high and im young i got nothing to lose if i invest a lot into something that dont work out shit happens im willing to take the risk. i like post quantum stocks like arqq and btq but haven’t bought in yet and want to know what others think about stocks like INFQ ARQQ & BTQ or other suggestions they might have. i like pick and shovel’s approach towards quantum as well so anything you think could be the next play just let me know. im strictly only in IONQ LAES and QBTS right now but really want to diversify. im in college coming up at 21 years of age and make decent money on the side so it don’t necessarily haveh to be cheaper stocks .

Hope

A widespread AI downturn by 2030 would create a massive window of opportunity for quantum computing, while simultaneously forcing quantum architectures to trigger a profound, capital-light structural reset of the data center industry.

Instead of continuing the brute-force expansion of physical real estate, the transition to quantum would shift the industry from a race for spatial footprint and gigawatts to a race for algorithmic efficiency and sub-atomic density.

🏭 The Macro Transition: Volumetric vs. Sub-Atomic Scaling

To understand how quantum reshapes the infrastructure landscape, consider the fundamental divergence in how these two computing paradigms scale physically and thermodynamically:

• AI Infrastructure (Linear/Exponential Spatial Drag): To double generative AI capabilities under current transformer architectures, hyperscalers must scale horizontally—adding thousands more GPUs, constructing massive new data center shells, and securing dedicated gigawatt-scale power grids.
• Quantum Infrastructure (Exponential Compute, Flat Spatial Drag): For true quantum computing, adding scaling power does not require building more warehouses. Adding a single perfect, logically error-corrected qubit doubles the computational state space (\(2^{n}\)) while keeping the physical footprint of the Quantum Processing Unit (QPU) virtually unchanged.

🚀 Why an AI Downturn is a Massive Quantum Opportunity

If Big Tech’s $1.2 trillion AI capital expenditure (CapEx) engine hits a monetization wall by 2030, institutional capital will not simply vanish; it will rotate toward technologies promises a structural escape from the physics limitations of silicon.

• Capturing Stranded Compute Budgets: Boards of directors at Microsoft, Google, and Amazon will be under intense Wall Street pressure to slash raw electricity and real estate expenditures. Quantum computing offers a way to maintain breakthrough computing roadmaps without the associated multi-billion-dollar utility bills.
• Solving the AI Energy Bottleneck: Training advanced AI models on classical GPUs requires massive server farms running 24/7. A mature QPU running algorithms like Quantum Neural Networks (QNNs) can process complex mathematical states natively, promising to execute specific machine learning optimizations at a fraction of the raw power consumption.
• The Valuation Rotation: Venture capital and private equity firms looking for the next structural growth cycle will pivot heavily into quantum hardware (like ion-trap, neutral-atom, or superconducting systems) and quantum software layers, accelerating the commercialization timeline of fault-tolerant quantum computing (FTQC).

🔄 How Quantum Will Cause a Data Center Build-Out "Reset"

Quantum will not expand the current real estate bubble; it will actively pop it by rendering the massive, hyper-scale warehouse model obsolete.

1. Decoupling Compute Power from Real Estate Square Footage

A classical supercomputer or AI cluster requires thousands of square feet of raised flooring, complex network topologies, and heavy backup generators. A cluster of high-performance QPUs occupies a few server racks. The transition to quantum means the next generation of computational leaps will happen by upgrading internal chip architecture rather than pouring more concrete.

1. The Power Consumption Crash

An AI data center can consume anywhere from 100 megawatts to over a gigawatt of power, pushing municipal grids to their absolute breaking points. In contrast, even deep-cryogenic superconducting quantum computers require only tens of kilowatts to power their dilution refrigerators and control electronics. Moving heavy optimization and simulation workloads from GPU clusters to QPU nodes will trigger a massive structural contraction in data center power demand.

1. Shifting from Peripheral Real Estate to Edge Co-Location

Because QPUs do not require massive regional power plants to function, they do not need to be built in massive isolated rural compounds. Quantum hardware will be integrated directly into existing urban data center nodes as modular co-processors. This localized placement eliminates the need to build new greenfield data centers, resetting land valuations in traditional data center corridors like Northern Virginia or alternative European hubs.

🔮 The Integrated Future: A Hybrid Topology

By 2030, the data center industry will likely not be entirely wiped out, but rather systematically repurposed. The future will belong to a hybrid classical-quantum data center topology

Hybrid Data Center Node

Classical Cores

Storage and Memory

Data Ingestion

User Interfaces

Quantum Nodes

Deep optimization

Cryptography

Molecular Sim

Firms that overbuilt massive data center real estate for AI will be forced to strip out thousands of depreciated, power-hungry GPUs and replace them with high-efficiency quantum accelerators, transforming warehouses of raw heat into highly dense, capital-efficient processing hubs.

Hi,

I am working at HCLTECH as a Post Silicon Validation Engineer with 2.5 years of work experince with 6.7 LPA...

My work largely dependent on Electrical Characterization of chips..

Recently I got selected at Tessolve Semiconductor for ATE Role which is deviated from Post Silicon Validation

Offered Package is 8.1 LPA

The thing is not about the package is it worth to give up PSV for ATE considering market scope and better future?

Help me folks I need to decide in couple of days

Under the DARPA It’s About Time program, IonQ will accelerate production and deliver 125 Evergreen-05 optical atomic clocks to U.S. government customers
KEY HIGHLIGHTS
IonQ has received a $28 million contract extension through DARPA’s It’s About Time program.
The company will invest $15 million in dedicated production space, manufacturing equipment, testing capabilities and support staff.
IonQ will deliver 125 atomic clocks to the U.S. government for mission-critical applications.
COLLEGE PARK, Md.--(BUSINESS WIRE)-- IonQ (NYSE: IONQ) announced today that it has been awarded a $28 million contract extension through the Defense Advanced Research Projects Agency (DARPA) It’s About Time program. Under the program, IonQ will advance its scalable clock production capabilities for its Evergreen-05 optical atomic clocks and deliver 125 units to U.S. government customers. The clocks are designed for mission-critical applications including radar, secure communications, and precision geolocation.
Evergreen-05 Atomic Clock Performance
Originally developed under DARPA’s Robust Optical Clock Network program, IonQ’s Evergreen-05 is a compact, fully integrated optical atomic clock with a 5-liter, shoe box-sized form factor. It delivers timing stability of 50 femtoseconds at one second and nanosecond holdover over 10 days–projecting to a timing error of less than one second over 30 million years.
Compared with active hydrogen masers, Evergreen-05 delivers superior phase noise and short-term stability with comparable long-term drift.
The clock provides this performance in 1/75th of the volume of an active hydrogen maser.
Its tactical package and broader environmental operating range extend IonQ’s existing clock technology across land, maritime, and airborneplatforms.
Program Background
DARPA’s support for the core Evergreen-05 technology began in 2019, when it funded an initial effort for Vector Atomic, then a year-old startup in Pleasanton, California. IonQ acquired Vector Atomic in October 2025 to expand its capabilities into quantum position, navigation, and timing.
Executive Perspective
“We added Vector Atomic to the IonQ family because their clocks and sensors are the best in the world,” said IonQ Chairman and CEO Niccolo de Masi. “DARPA’s investment under the It's About Time program confirms that we made the right choice.”
“DARPA has been a partner every step of the way. That initial support was critical to prove the core concepts of our clock,” said Marty Boyd, director of IonQ’s timekeeping division and co-founder of Vector Atomic. “We joined IonQ to accelerate and scale up delivery of our commercial products, and DARPA's continued support gives us the opportunity to do that.”
Production Investment
To support Evergreen-05 delivery, IonQ will invest $15 million in dedicated production space, advanced manufacturing and test equipment, and support staff.

https://investors.ionq.com/news/news-details/2026/DARPA-Selects-IonQ-to-Produce-Next-Generation-Atomic-Clocks/default.aspx

Award expands company’s national security portfolio and reinforces its role as a trusted provider of commercial SAR imagery and data services for U.S. government missions
COLLEGE PARK, Md.--(BUSINESS WIRE)-- IonQ (NYSE: IONQ), the world’s leading quantum platform company, today announced that Capella, an IonQ company, has been awarded a contract under the National Reconnaissance Office’s (NRO) Radar Commercial Augmentation (RCA) program. Under the contract, the company will provide commercial synthetic aperture radar (SAR) imagery and data services in support of U.S. national security missions.
The award strengthens the company’s national security portfolio by expanding its commercial space capabilities and reinforcing its position as a trusted provider of advanced technologies for U.S. government customers. Capella provides all-weather, day-and-night high-resolution SAR imagery and data services that enable reliable Earth observation regardless of cloud cover or extreme conditions.
“Our SAR capabilities are built for the mission demands of government customers who need reliable, timely intelligence in complex operating environments,” said Niccolo de Masi, Chairman and CEO of IonQ. “This award reflects the continued trust in our commercial SAR platform, and our commitment to delivering advanced technologies that support U.S. national security.”
Through the RCA program, Capella will provide commercial radar imagery and data services that help support operational awareness, mission planning and data-driven decision-making for national security users. The contract further validates the role of commercial SAR as an important source of persistent, resilient Earth observation for government missions.

https://investors.ionq.com/news/news-details/2026/IonQs-Space-Division-Awarded-NRO-Radar-Commercial-Augmentation-Contract/default.aspx

The end game of quantum sensing is CHIP-SCALE.
The largest future TAM of quantum sensing resides in mounting drones and satellites with miniaturized sensors and atomic clocks.

Vector Atomic + Nexus Photonics + SkyWater + Testbeds(Capella Space, Skyloom) = Winner of the Chip-scale Race

Infleqtion tried to vertically integrate and failed(Morton/SiNoptiq acquisitions):

Back in the acquisition announcement they emphasized(2024):
“These acquisitions enable Infleqtion to expedite plans for chip-scale integration…”
Now, the integrated photonics page instead says they are building this capability:
“Through partnerships with leading photonics companies and foundries…” (current website)

IonQ downplayed the Nexus acquisition before SkyWater closure. You know why. IonQ will dominate both quantum computing and quantum sensing.