˰

Researchers “Hide” Ions to Reduce Quantum Errors

A new technique developed by Honeywell researchers reduced the amount of measured “crosstalk” errors by an order of magnitude

November 29, 2021

Cambridge Researchers at Honeywell Quantum Solutions have turned problematic micromotion that jostles trapped ion qubits out of position into a plus.

The team recently demonstrated a technique that uses micromotion to shield nearby ions from stray photons released during mid-circuit measurement, a procedure in which lasers are used to check the quantum state of certain qubits and then reset them.

Mid-circuit measurement is a key capability in today’s early-stage quantum computers. Because the qubit’s state can be checked and then re-used, researchers can run more complex algorithms – such as the holoQUADS algorithm – with fewer qubits.

By “hiding” ions behind micromotion, Honeywell researchers significantly reduced the amount of “crosstalk” – errors caused by photons hitting neighboring qubits – that occurred when measuring qubits during an operation. (Details are available in a pre-print publication available on the arXiv.)

“We were able to reduce crosstalk by an order of magnitude,” said Dr. John Gaebler, Chief Scientist of Commercial Products at Honeywell Quantum Solutions, and lead author of the paper. “It is a significant reduction in crosstalk errors. Much more so than other methods we’ve used.”

The new technique represents another step toward reducing errors that occur in today’s trapped-ion quantum computers, which is necessary if the technology is to solve problems too complex for classical systems.

“For quantum computers to scale, we need to reduce errors throughout the system,” said Tony Uttley, President of Honeywell Quantum Solutions. “The new technique the Honeywell team developed will help us get there.”

Eliminating errors

Today’s quantum computing technologies are still in the early stage and are prone to “noise” - or interference - caused by qubits interacting with their environment and one another.

This noise causes errors to accumulate, corrupts information stored in and between physical qubits, and disrupts the quantum state in which qubits must exist to run calculations. (Scientists call this decoherence.)

Researchers are trying to eliminate or suppress as many of these errors as possible while also creating logical qubits, a collection of entangled physical qubits on which quantum information is distributed, stored, and protected.

By creating logical qubits, scientists can apply mathematical codes to detect and correct errors and eliminate noise as calculations are running. This multi-step process is known as quantum error correction (QEC). Honeywell researchers recently demonstrated they can detect and correct errors in real-time by applying multiple rounds of full cycles of quantum error correction.

Logical qubits and QEC are important elements to improving the accuracy and precision of quantum computers. But, Gaebler said, those methods are not enough on their own.

“Everything has to be working at a certain level before QEC can take you the rest of the way,” he said. “The more we can suppress or eliminate errors in the overall system, the more effective QEC will be and the fewer qubits we need to run complex calculations.”

Cutting out crosstalk

In classical computing, bit flip errors occur when a binary digit, or bit, inadvertently switches from a zero to one or vice versa. Quantum computers experience a similar bit flip error as well as phase flip errors. Both errors cause qubits to lose their quantum state – or to decohere. In trapped ion quantum computing, one source of errors comes from the lasers used to implement gate operations and qubit measurements.

Though these lasers are highly controlled, unruly photons (small packets of light) still escape and bounce into neighboring ions causing “crosstalk” and decoherence.

Researchers use a variety of methods to protect these ions from crosstalk, especially during mid-circuit measurement where only a single qubit or a small subset of qubits is meant to be measured. With its quantum charged-coupled device (QCCD) architecture, the Honeywell team takes the approach of moving neighboring ions away from the qubit being fluoresced by a laser. But there is limited space along the device, which becomes even more compact as more qubits are added.

“Even when we move them more than 100 microns away, we still get more crosstalk than we prefer,” said Dr. Charlie Baldwin, a senior advanced physicist and co-author of the paper. “There is still some scattered light from the detection laser.”

The team hit on hiding neighboring ions from stray photons using micromotion potentials, which are caused by the oscillating electric fields used to “trap” these charged atoms. Micromotion is typically thought of as a nuisance with ion trapping, causing the ions to rapidly oscillate back and forth, and occurs when the ions are pushed out of the center of the trap by additional electric fields.

“Usually, we are trying to eliminate micromotion but in this case, we were able to use it to our benefit,” said Dr. Patty Lee, chief scientist at Honeywell Quantum Solutions.

The team’s goal is to reduce by 10 million the probability of a neighboring ion absorbing photons at 110 microns away. By moving neighboring ions and hiding them behind micromotion the Honeywell team is approaching that mark.

How and why the technique works

In their paper, Honeywell researchers delved into how and why hiding ions with micromotion works, including the ideal frequency of the oscillations. They also identified and characterized errors. (The basic physics behind the concept of hiding ions was first explored by the ion storage group at the National Institute of Standards and Technology.)

“Mid-circuit operations are a new feature in commercial quantum computing hardware, so we had to invent a new way to validate that the micromotion hiding technique was achieving the low level of crosstalk errors that we predicted,” said Dr. Charlie Baldwin.

Though the new method resulted in a significant reduction of crosstalk errors, the Honeywell team acknowledged there is further to go.

“Crosstalk is one of those scary errors for scaling,” Gaebler said. “It has to be controlled because it becomes more of a problem as you scale and add qubits. This is another tool that will help us scale and help us compact our systems and pack in as many qubits as we can.”

About ˰

˰, the world’s largest integrated quantum company, pioneers powerful quantum computers and advanced software solutions. ˰’s technology drives breakthroughs in materials discovery, cybersecurity, and next-gen quantum AI. With over 500 employees, including 370+ scientists and engineers, ˰ leads the quantum computing revolution across continents. 

Blog
|
technical
July 29, 2026
Scaling the Signal: What a Larger QFT Says About Quantum Progress
  • Mitsui & Co. and Mitsubishi Electric demonstrated one of the world’s largest approximate Quantum Fourier Transforms (QFT) on ˰ ˰, scaling from prior records to 98 physical qubits.
  • The collaboration also implemented a logical QFT using a QEC (Quantum Error Correction) code with up to 12 logical qubits.
  • The work highlights ˰’s accuracy and flexible architecture.

While there is ongoing debate around the pace of quantum computing’s development, a more grounded way to assess progress is through concrete demonstrations of foundational algorithms at meaningful scale. In this context, Mitsui & Co. and Mitsubishi Electric are taking a pragmatic view of quantum progress—focusing on how close the field is to executing core algorithmic primitives that underpin many potential industrial applications, rather than relying on abstract milestones or timelines.

, the industrial giants teamed up with ˰ to measure how close we are to running the Quantum Fourier Transform (QFT), a widely-used algorithmic primitive, at scales necessary for industrial applications. In the process, the team successfully ran one of the largest instances of the approximate QFT ever demonstrated. This achievement matters because the QFT is an essential primitive that underpins many of the quantum algorithms expected to deliver practical advantages.

You may have heard of the (classical) Fourier transform (FT), due to its ubiquity throughout modern computing. The FT is essential in everything from image analysis to data compression, with almost limitless applications in between. The quantum Fourier transform (QFT) is similar; it’s used in everything from chemistry to finance.

Because the QFT is a foundational primitive underpinning many quantum algorithms, demonstrating it at larger scales and higher fidelity is a practical way to measure quantum computing readiness. This is exactly the type of benchmarking that organizations should consider to understand where today’s systems are useful, and to see how fault-tolerant approaches are progressing. Ultimately, algorithm-level benchmarking like this is one of the most useful ways to understand not just where we are, but where we are going.

A Transformative Approach

Primitives like Fourier Transform are so widespread because they simplify problems by transforming them into something that is easier to deal with. At ˰, not only are they crucial for industrial applications but they can also simplify algorithms, making them possible to run now instead of later. This ‘transformational’ approach extends beyond the QFT - other transforms exist, and we have even invented our own quantum-native transforms.

Using our ˰ quantum computer and Guppy language, the joint team explored running the QFT on both physical qubits and on logical qubits, showing that fault tolerance is progressing quickly.  Running the QFT on 98 physical qubits; the paper shows a clear progression from previous results.

Then, using the Steane code, one of the best-studied quantum error correcting codes, the team used ˰’ 98 physical qubits to form 12 logical qubits, successfully running the QFT with the mechanisms of quantum error correction interwoven into the algorithm. This marks a crucial step forward for the field.

Foundational Progress

Taken together, these results provide a more concrete lens through which to view progress in quantum computing: not as abstract projections, but as measurable advances in the execution of foundational algorithms at increasing scale. By benchmarking the Quantum Fourier Transform on both physical and logical qubits, Mitsui & Co. and Mitsubishi Electric are helping to clarify what today’s hardware can already achieve, and where fault-tolerant approaches begin to extend those limits.

More broadly, the organizations best positioned to benefit from quantum computing will be those that focus on these foundational capabilities early, and use them to build a clear, evidence-based understanding of how the technology fits into their business goals.

technical
All
Blog
|
partnership
July 29, 2026
˰ and NVIDIA Validate Generative Quantum AI Framework for Pharmaceutical R&D

It is believed that unlocking answers to some of the most complex scientific and industrial problems will require the seamless integration of high-performance computing (HPC), generative AI (GenAI), and quantum computing. Toward this goal, ˰, NVIDIA, and a major pharmaceutical company have successfully demonstrated the first step in a proof-of-principle framework designed to connect these three distinct computing paradigms for industrially relevant computational chemistry.

This milestone, enabled by three industry leaders and experts in their respective domains, serves as a foundational capability that could support the development of future hybrid quantum-AI workflows to help optimize industrial research and development (R&D).

The potential value is a path toward more automated, repeatable, and scalable workflows for translating chemistry problems into executable quantum programs—capabilities that could eventually make hybrid computing easier to deploy in industrial R&D.

The GenQAI Framework

The framework, termed Generative Quantum AI (GenQAI), involved a quantum computer simulating a pharmaceutical compound using programming instructions generated by an AI model, which itself was trained on quantum data that was simulated using HPC.

While the vision for GenQAI explores how future industrial simulation workflows might be optimized by training AI models using quantum data derived directly from a quantum computer, the framework currently consists of four main technical steps:

  1. Simulating quantum data: The process began by simulating quantum data with NVIDIA accelerated computing using .
  2. Fine-tuning the AI: This simulated quantum data was used to fine-tune a pre-trained AI model from the open .
  3. Generating instructions: The AI model then generated quantum circuits, which are the programming instructions required for the quantum simulation.
  4. Validating accuracy: To validate the results, the circuits were run on ˰’s ˰ quantum computer using its InQuanto quantum chemistry platform.

The core novelty of this development lies within the process of the framework itself. In this proof-of-principle experiment, an AI model fine-tuned on simulated quantum data generated circuits that were successfully executed and validated on ˰’s ˰ system. Rather than delivering an immediate commercial advantage, this achievement establishes a credible, verifiable baseline for how HPC, AI, and quantum computing can function in tandem.

The Case Study

The validation of the framework represents an early step toward the goal of developing scalable architectures for the pharmaceutical industry.

With a shared view toward eventually scaling the framework for pharmaceutical R&D applications, the researchers simulated a pharmaceutical compound: imipramine. This anti-depressant was chosen because it serves as a model compound for drug degradation and shelf-life studies, which are standard components of the pharmaceutical R&D lifecycle.

Developing hybrid infrastructure that enterprises may adopt requires a deep, coordinated effort among domain experts. As such, this successful test highlights the value of combining the strengths of a quantum computing hardware and software leader (˰), with a hybrid-quantum classical platform (NVIDIA), and a leading enterprise end-user to build and test future computing capabilities for industrial chemistry.

Scaling the Framework

Although demonstrated on a pharmaceutical compound, the architecture could eventually inform similar molecular-simulation workflows in sectors such as energy, agriculture, advanced materials, and electronics. At this stage, it provides a reference for further testing and development.

Engage Further

here to explore the full technical details of this demonstration and contact our team to learn more about joining ˰’s enterprise partner network.

partnership
All
Blog
|
events
July 21, 2026
˰ SG Grand Challenge 2026

˰ is pleased to announce that applications are now open for the ˰ SG Grand Challenge 2026, a global innovation challenge designed to bring together researchers, developers, scientists and innovators to explore practical applications of quantum computing.

Organized by ˰ and supported by Singapore's National Quantum Office and Aqora, the three-month program aims to foster collaboration across academia, industry and the quantum developer community while supporting the continued growth of Singapore's quantum ecosystem.

A Platform for Quantum Computing Innovation

Participants will work in teams to develop solutions across a range of challenge areas, including chemistry and molecular simulation, optimization, AI for quantum systems, quantum error correction, condensed matter and materials science, and open innovation. Throughout the program, participants will have access to mentoring, technical enablement and ˰ quantum computing resources.

Singapore Grand Finale

Selected finalist teams will be invited to present their work at the Grand Finale hosted in Singapore before representatives from academia, and industry. The event will celebrate innovative applications of quantum computing while providing an opportunity for participants to engage with Singapore's growing quantum community.

Join the Challenge

The ˰ SG Grand Challenge welcomes participants from around the world. Whether you are an experienced quantum researcher or beginning your quantum computing journey, the program offers an opportunity to collaborate, learn and contribute to the development of practical quantum applications.

Applications are now open. Spaces are limited and subject to review and approval.

events
All