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
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 Quantinuum
Quantinuum, the world’s largest integrated quantum company, pioneers powerful quantum computers and advanced software solutions. Quantinuum’s technology drives breakthroughs in materials discovery, cybersecurity, and next-gen quantum AI. With over 500 employees, including 370+ scientists and engineers, Quantinuum leads the quantum computing revolution across continents.
In a follow-up to our recent work with Hiverge using AI to discover algorithms for quantum chemistry, we’ve teamed up with Hiverge, Amazon Web Services (AWS) and NVIDIA to explore using AI to improve algorithms for combinatorial optimization.
With the rapid rise of Large Language Models (LLMs), people started asking “what if AI agents can serve as on-demand algorithm factories?” We have been working with Hiverge, an algorithm discovery company, AWS, and NVIDIA, to explore how LLMs can accelerate quantum computing research.
Hiverge – named for Hive, an AI that can develop algorithms – aims to make quantum algorithm design more accessible to researchers by translating high-level problem descriptions in mostly natural language into executable quantum circuits. The Hive takes the researcher’s initial sketch of an algorithm, as well as special constraints the researcher enumerates, and evolves it to a new algorithm that better meets the researcher’s needs. The output is expressed in terms of a familiar programming language, like Guppy or NVIDIA CUDA-Q, making it particularly easy to implement.
The AI is called a “Hive” because it is a collective of LLM agents, all of whom are editing the same codebase. In this work, the Hive was made up of LLM powerhouses such as Gemini, ChatGPT, Claude, Llama, as well as NVIDIA Nemotron, which was accessed through AWS’ Amazon Bedrock service. Many models are included because researchers know that diversity is a strength – just like a team of human researchers working in a group, a variety of perspectives often leads to the strongest result.
Once the LLMs are assembled, the Hive calls on them to do the work writing the desired algorithm; no new training is required. The algorithms are then executed and their ‘fitness’ (how well they solve the problem) is measured. Unfit programs do not survive, while the fittest ones evolve to the next generation. This process repeats, much like the evolutionary process of nature itself.
After evolution, the fittest algorithm is selected by the researchers and tested on other instances of the problem. This is a crucial step as the researchers want to understand how well it can generalize.
In this most recent work, the joint team explored how AI can assist in the discovery of heuristic quantum optimization algorithms, a class of algorithms aimed at improving efficiency across critical workstreams. These span challenges like optimal power grid dispatch and storage placement, arranging fuel inside nuclear reactors, and molecular design and reaction pathway optimization in drug, material, and chemical discovery—where solutions could translate into maximizing operational efficiency, dramatic reduction in costs, and rapid acceleration in innovation.
In other AI approaches, such as reinforcement learning, models are trained to solve a problem, but the resulting "algorithm" is effectively ‘hidden’ within a neural network. Here, the algorithm is written in Guppy or CUDA-Q (or Python), making it human-interpretable and easier to deploy on new problem instances.
This work leveraged the NVIDIA CUDA-Q platform, running on powerful NVIDIA GPUs made accessible by AWS. It’s state-of-the art accelerated computing was crucial; the research explored highly complex problems, challenges that lie at the edge of classical computing capacity. Before running anything on Quantinuum’s quantum computer, the researchers first used NVIDIA accelerated computing to simulate the quantum algorithms and assess their fitness. Once a promising algorithm is discovered, it could then be deployed on quantum hardware, creating an exciting new approach for scaling quantum algorithm design.
More broadly, this work points to one of many ways in which classical compute, AI, and quantum computing are most powerful in symbiosis. AI can be used to improve quantum, as demonstrated here, just as quantum can be used to extend AI. Looking ahead, we envision AI evolving programs that express a combination of algorithmic primitives, much like human mathematicians, such as Peter Shor and Lov Grover, have done. After all, both humans and AI can learn from each other.
As quantum computing power grows, so does the difficulty of error correction. Meeting that demand requires tight integration with high-performance classical computing, which is why we’ve partnered with NVIDIA to push the boundaries of real-time decoding performance.
Realizing the full power of quantum computing requires more than just qubits, it requires error rates low enough to run meaningful algorithms at scale. Physical qubits are sensitive to noise, which limits their capacity to handle calculations beyond a certain scale. To move beyond these limits, physical qubits must be combined into logical qubits, with errors continuously detected and corrected in real time before they can propagate and corrupt the calculation. This approach, known as fault tolerance, is a foundational requirement for any quantum computer intended to solve problems of real-world significance.
Part of the challenge of fault tolerance is the computational complexity of correcting errors in real time. Doing so involves sending the error syndrome data to a classical co-processor, solving a complex mathematical problem on that processor, then sending the resulting correction back to the quantum processor - all fast enough that it doesn’t slow down the quantum computation. For this reason, Quantum Error Correction (QEC) is currently one of the most demanding use-cases for tight coupling between classical and quantum computing.
Given the difficulty of the task, we have partnered with NVIDIA, leaders in accelerated computing. With the help of NVIDIA’s ultra-fast GPUs (and the GPU-accelerated BP-OSD decoder developed by NVIDIA as part of NVIDIA CUDA-Q QEC library), we were able to demonstrate real-time decoding of Helios’ qubits, all in a system that can be connected directly to our quantum processors using NVIDIA NVQLink.
While real-time decoding has been demonstrated before (notably, by our own scientists in this study), previous demonstrations were limited in their scalability and complexity.
In this demonstration, we used Brings’ code, a high-rate code that is possible with our all-to-all connectivity, to encode our physical qubits into noise-resilient logical qubits. Once we had them encoded, we ran gates as well as let them idle to see if we could catch and correct errors quickly and efficiently. We submitted the circuits via both NVIDIA CUDA-Q as well as our own Guppy language, underlining our commitment to accessible, ecosystem-friendly quantum computing.
The results were excellent: we were able to perform low-latency decoding that returned results in the time we needed, even for the faster clock cycles that we expect in future generation machines.
A key part of the achievement here is that we performed something called “correlated” decoding. In correlated decoding, you offload work that would normally be performed on the QPU onto the classical decoder. This is because, in ‘standard’ decoding, as you improve your error correction capabilities, it takes more and more time on the QPU. Correlated decoding elides this cost, saving QPU time for the tasks that only the quantum computer can do.
Stay tuned for our forthcoming paper with all the details.
Every year, APS Global Physics Summit brings together scientific community members from around the world across all disciplines of physics.
Join Quantinuum at this year’s conference, taking place in our backyard, Denver, Colorado, from March 15th – 20th, where we will showcase how our quantum hardware, software, and partnerships are helping define the next era of high-performance and quantum computing.
Find our team at booth #1020 and join our sessions below to discover how we’re advancing quantum technologies and building the bridge between HPC and quantum.
Monday, March 16th
Programmable quantum matter at the frontier of classical computation
Speaker: Andrew Potter
Time: 10:12 – 10:48 am
Benchmarking a 98-qubit trapped-ion quantum computer
Speaker: Charles Baldwin
Time: 12:36 – 12:48 pm
High-Fidelity Quantum operations in the Helios Barium-Ion Processor
Speaker: Anthony Ransford
Time: 4:18 – 4:30 pm
Generative AI Model for Quantum State Preparation
Speaker: Jem Guhit
Time: 4:42 – 4:54 pm
Quantum digital simulations of holographic models using Quantinuum Systems
Speaker: Enrico Rinaldi
Time: 5:54 – 6:30 pm
Tuesday, March 17th
Software-Enabled Innovations that Drive Robust Commercial Operation on Quantinuum Helios
Speaker: Caroline Figgatt
Time: 8:00 – 8:12 am
Improving Clock Speed in the Quantinuum Helios Quantum Computer
Speaker: Adam Reed
Time: 8:12 – 8:24 am
Less Quantum, More Advantage: An End-to-End Quantum Algorithm for the Jones Polynomial
Speaker: Konstantinos Meichanetzidis
Time: 8:48 – 9:00 am
Quantum Operation Pipelining in the Quantinuum Helios Processor
Speaker: Colin Kennedy
Time: 9:00 - 9:12 am
Directly estimating the fidelity of measurement-based quantum computation
Speaker: David Stephen
Time: 9:12 - 9:24 am
Logical algorithms in a quantum error-detecting code on a trapped-ion quantum processor
Speaker: Matthew DeCross
Time: 9:36 - 9:48 am
Separate and efficient characterization of SPAM errors in the presence of leakage
Speaker: Leigh Norris
Time: 10:00 - 10:12 am
Logical benchmarking on a trapped-ion quantum processor
Speaker: Andrew Guo
Time: 12:00 - 12:12 pm
Modelling Actinides Chemistry with Trapped Ion Quantum Computers
Speaker: Carlo Alberto Gaggioli
Time: 3:30 - 3:42 pm
Wednesday, March 18th
Digital quantum magnetism at the frontier of classical simulation
Speaker: Michael Foss-Feig
Time: 8:36 - 9:12 am
Shorter width truncated Taylor series for Hamiltonian dynamics simulations
Speaker: Michelle Wynne Sze
Time: 9:24 - 9:36 am
Quantum-Accelerated DFT+DMFT for Correlated Subspaces in Hemoglobin
Speaker: Juan Pedersen
Time: 9:48 - 10:00 am
Simple logical quantum computation with concatenated symplectic double codes
Speaker: Noah Berthusen
Time: 12:48 - 1:00 pm
When is enough enough? Efficient estimation of quantum properties by stopping early
Speaker: Oliver Hart
Time: 12:48 - 1:00 pm
High-Level Programming of the Quantinuum Helios Processor
Speaker: John Campora
Time: 1:48 - 2:24 pm
Error detection without post-selection in adaptive quantum circuits
Speaker: Eli Chertkov
Time: 4:42 - 4:54 pm
Thursday, March 19th
Below Threshold Logical Quantum Computation at Quantinuum
Speaker: Shival Dasu
Time: 8:00 - 8:36 am
Performing optimal phase measurements with a universal quantum processor
Speaker: Ross Hutson
Time: 8:36 - 8:48 am
Benchmarking with leakage heralded measurements on the Quantinuum Helios processor
Speaker: Victor Colussi
Time: 10:00 am
High-throughput bidirectional microwave-to-optical transduction assessed with a practical quantum capacity
Speaker: Maxwell Urmey
Time: 12:00 - 12:36 pm
Fast quantum state preparation via AI-based Graph Decimation
Speaker: Matteo Puviani
Time: 5:54 - 6:06 pm
Friday, March 20th
2D Tensor Network Methods for Simulation of Spin Models on Quantum Computers
Speaker: Reza Haghshenas
Time: 8:36 - 8:48 am
High-Performance Computing Simulations for Optical Multidimensional Coherent Spectroscopy Studies of Strained Silicon-Vacancy Centers in Diamond
Speaker: Imran Bashir
Time: 10:36 - 10:48 am
High-Performance Statevector Simulation for TKET and Selene with NVIDIA cuStateVec
Speaker: Fabian Finger
Time: 12:36 - 12:48 pm
Part 1: Logic gates on High-rate Quantum LDPC codes using ion trap devices
Speaker: Elijah Durso-Sabina
Time: 12:48 - 1:00 pm
Driving Quantum Computing Forward: QEC, Hardware, and Applications with Quantinuum
Speaker: Natalie Brown
Time: 1:12 - 1:48 pm
A new QCCD computer and new applications
Speaker: Anthony Ransford
Time: 2:24 - 3:00 pm
*All times in MT