Quantum Algorithms, Quantum Physics

1-bit quantum filter cuts complexity of particle tracking

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August 5, 2026 by Ivy Delaney

The impending transition to the High-Luminosity Large Hadron Collider (HL-LHC) is creating a computational bottleneck where particle reconstruction complexity threatens to overwhelm classical resources. Researchers at Nikhef National Institute for Subatomic Physics and Maastricht University have responded with a 1-Bit Quantum Filter, a new approach achieving an asymptotic gate complexity of O(√N log N), a potential speedup over existing quantum algorithms.

Validated on LHCb Monte Carlo events and benchmarked on Quantinuum System Model H2 and IBM Heron R3 processors, this work demonstrates a quantum track reconstruction method viable even within the constraints of current Noisy Intermediate Scale Quantum (NISQ) era hardware.

Researchers are now exploring quantum solutions to address this challenge, but conventional quantum algorithms present their own hurdles in the near term. A team led by Xenofon Chiotopoulos has introduced a 1-Bit Quantum Filter, a novel approach designed to circumvent the limitations of algorithms like Harrow-Hassidim-Lloyd (HHL) which demand extensive circuit depths. This new filter reformulates the complex task of particle tracking from a computationally expensive matrix inversion into a more manageable binary ground-state filtering problem.

This represents a potential speedup compared to standard quantum methods, which struggle with scalability. The published work explains that they validate this approach on LHCb Monte Carlo events, demonstrating segment finding efficiency competitive with classical methods. Benchmarking was performed on both the Quantinuum System Model H2, a trapped-ion processor, and the IBM Heron R3, a superconducting processor.

The results indicate that the 1-Bit Quantum Filter can successfully solve realistic event topologies on noise-free simulators and tackle smaller tracking scenarios even within the constraints of quantum computers. The team acknowledges remaining hurdles, including the need for efficient readout mechanisms and streamlined Hamiltonian construction, before a complete end-to-end tracking solution can be realized.

Funding for this research came from multiple sources, including the CERN Quantum Technology Initiative (QTI) which provided access to IBM Quantum computers and Quantinuum platforms. The LHCb Data Processing and Analysis (DPA) project also provided support, alongside the LHCb simulation and computing projects responsible for generating the simulated data used in the study.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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