Gregory Quiroz, a senior physicist at the Johns Hopkins Applied Physics Laboratory (APL) and associate research professor at Johns Hopkins University, along with co-author William Watkins, have achieved a breakthrough in quantum noise characterization within quantum systems. Their findings, published in Physical Review Letters, address a long-standing obstacle to developing reliable quantum computing by improving the assessment of noise impacts on quantum algorithms. This work bridges the gap between today’s simplistic noise models and the complexities of real quantum hardware, specifically capturing noise that spreads across both space and time within the processor.
Characterizing Quantum Noise in Quantum Systems
Researchers at Johns Hopkins APL and University have made a breakthrough in characterizing quantum noise – a critical step toward building reliable quantum computers. Existing models often oversimplify noise, failing to capture its spread across time and location within a quantum processor. This new work addresses that limitation, focusing on understanding how noise impacts quantum algorithms and ultimately, how to mitigate those effects for fault-tolerant computation.
To overcome the complexity of scaling quantum systems, the team exploited the principle of symmetry. They applied a mathematical technique called root space decomposition, organizing quantum actions to simplify analysis. This allowed them to represent the system as a “ladder” of discrete states, enabling observation of how noise causes transitions between those states. This categorization is key, informing different noise mitigation techniques.
This noise characterization is expected to contribute to both hardware design and software development for quantum computing. By understanding noise impacts, researchers can build better physical systems and create algorithms that account for these effects. APL’s broad quantum portfolio, including studies of noise sources like cosmic rays, positions them to continue developing novel characterization and mitigation protocols.
Exploiting Symmetry to Simplify Noise Analysis
Researchers from Johns Hopkins APL and Johns Hopkins University addressed a key obstacle to developing quantum computers: characterizing noise. Current models often simplify how noise impacts computation on real hardware. To overcome this, the team exploited symmetry within quantum systems, a property of physics that helps simplify complex problems. This approach allowed them to move beyond analyzing single instances of noise, and consider how it spreads across both space and time within the quantum processor.
The team applied a mathematical technique called root space decomposition to radically simplify how the quantum system is represented and analyzed. This technique organizes actions within the quantum system, allowing the researchers to represent it as a ladder, with each rung representing a discrete state. By applying noise and observing transitions between rungs, they could classify it into categories, informing techniques for mitigation and building error-resilient systems.
This novel framework provides a mathematically compact way to describe quantum noise. Classifying noise based on whether it causes transitions between system states allows for tailored mitigation strategies. This work contributes to both the physical design of better quantum systems and the development of algorithms and software that account for quantum noise—a critical step toward large-scale, functional quantum computers.
Today’s models are commonly too simplistic to capture how quantum noise affects computation on real hardware,” Quiroz said. “Our work is trying to bridge that gap.”
Gregory Quiroz
APL’s Quantum Computing and Noise Mitigation Work
Researchers at Johns Hopkins APL and Johns Hopkins University achieved a breakthrough in characterizing quantum noise—a key obstacle to reliable quantum computing. Their work, published in Physical Review Letters, addresses the limitations of simplified noise models which often fail to capture how noise spreads across space and time within quantum processors. Understanding the impact of this noise is the first step towards mitigating its effects and enabling successful quantum error-correcting codes for larger-scale computers.
To overcome the complexity of scaling quantum systems, the team exploited the physics principle of symmetry. They applied a mathematical technique called root space decomposition—previously used in other areas of quantum mechanics—to radically simplify how the system is represented and analyzed. This allowed them to visualize the quantum system as a ladder, enabling observation of how different noise types cause transitions between states, ultimately categorizing noise for targeted mitigation techniques.
APL possesses broad expertise spanning quantum computing challenges, from experimental physics to error correction, and views noise mitigation as central to its quantum portfolio. Research includes studying noise sources like cosmic rays and developing new characterization protocols. This particular study provides insight into noise impacts on algorithms and error correction, and APL plans to pursue further research based on these findings.
