Applying Algorithms to Stabilize Quantum Systems

Quantum error correction (QEC) is the primary strategy for protecting a quantum computer from the environment. However, there is a significant bottleneck: precise calibration is short-lived, which requires perpetually adapting the control parameters of the computer to the drifting environmental conditions. Just published in Nature, our team showed unified calibration with error-corrected computation on our Willow processor, training a reinforcement learning agent to stabilize a logical qubit and pave the way towards a quantum computer that continuously learns from its errors. Key research findings: -->✨ A New Paradigm: This work enables a future where we have a quantum computer that learns from its errors and never stops computing. Removing the need to take the system offline for calibration. --> Improved Stability: We experimentally demonstrated this framework on our Willow superconducting processor, improving the logical stability of the surface code 3.5-fold against injected drift. --> Beyond Traditional Limits: RL fine-tuning of an already well-calibrated processor yields an additional 20% suppression of the logical error rate, pushing performance beyond the limits of traditional physics-based calibration and human expert tuning. --> Scalability: Numerical simulations confirm the scalability of our RL framework, revealing that the optimization speed is independent of system size, ensuring this remains just as effective as we scale to much larger systems. This research demonstrates that the path to fault-tolerant quantum computing relies not just on better hardware, but on more intelligent control systems. I am proud of our teams for pioneering this approach, an important step towards solving the challenges in quantum information science. Nature article here: https://lnkd.in/gfuxKYJf

🚨 Exciting #quantumcomputing alert! Now #QEC primitives actually make #quantumcomputers more powerful! 75 qubit GHZ state on a superconducting #QPU 🚨 In our latest work we address the elephant in the room about #quantumerrorcorrection - in the current era where qubit counts are a bottleneck in the systems available, adopting full-blown QEC can be a step backwards in terms of computational capacity. This is because even when it delivers net benefits in error reduction, QEC consumes a lot of qubits to do so and we just don't have enough right now... So how do we maximize value for end users while still pushing hard on the underpinning QEC technology? To answer this the team at Q-CTRL set out to determine new ways to significantly reduce the overhead penalties of QEC while delivering big benefits! In this latest demonstration we show that we can adopt parts of QEC -- indirect stabilizer measurements on ancilla qubits -- to deliver large performance gains without the painful overhead of logical encoding. And by combining error detection with deterministic error suppression we can really improve efficiency of the process, requiring only about 10% overhead in ancillae and maintaining a very low discard rate of executions with errors identified! Using this approach we've set a new record for the largest demonstrated entangled state at 75 qubits on an IBM quantum computer (validated by MQC) and also demonstrated a totally new way to teleport gates across large distances (where all-to-all connectivity isn't possible). The results outperform all previously published approaches and highlight the fact that our journey in dealing with errors in quantum computers is continuous. Of course it isn't a panacea and in the long term as we try to tackle even more complex algorithms we believe logical encoding will become an important part of our toolbox. But that's the point - logical QEC is just one tool and we have many to work with! At Q-CTRL we never lose sight of the fact that our objective is to deliver maximum capability to QC end users. This work on deploying QEC primitives is a core part of how we're making quantum technology useful, right now. https://lnkd.in/gkG3W7eE

By driving a quantum processor with laser pulses arranged according to the Fibonacci sequence, physicists observed the emergence of an entirely new phase of matter—one that displays extraordinary stability in a domain where fragility is the norm. Quantum computers operate using qubits, which differ radically from classical bits. A qubit can exist in superposition, occupying multiple states at once, and can become entangled with others across space. These properties enable immense computational power, but they come with a cost: quantum states are notoriously short-lived. Environmental noise, microscopic imperfections, and edge effects rapidly degrade coherence, limiting how long quantum information can survive. Seeking a new way to protect fragile quantum states, scientists at the Flatiron Institute, instead of applying laser pulses at regular intervals, they used a rhythm governed by the Fibonacci sequence—an ordered but non-repeating pattern long known to appear in biological growth, crystal structures, and wave interference. The experiment was carried out on a chain of ten trapped-ion qubits, driven by precisely timed laser pulses. The result was the formation of what is described as a time quasicrystal. Unlike ordinary crystals, which repeat periodically in space, a time quasicrystal exhibits structure in time without repeating in a simple cycle. The Fibonacci-based driving created a temporal order that resisted disruption, allowing the quantum system to remain coherent far longer than expected. The improvement was significant. Under standard conditions, the quantum state persisted for roughly 1.5 seconds. When driven by the Fibonacci pulse sequence, coherence times stretched to approximately 5.5 seconds—more than a threefold increase. Even more intriguing was the system’s temporal behavior. Measurements indicated that the quantum dynamics unfolded as if time itself possessed two independent structural directions. This does not imply time flowing backward, but rather that the system’s evolution followed two intertwined temporal pathways—an emergent property arising purely from the Fibonacci drive. The researchers propose that the non-repeating structure of the Fibonacci sequence suppresses errors that typically accumulate at the boundaries of quantum systems. By distributing disturbances in a highly ordered yet aperiodic way, the sequence stabilizes the collective behavior of the qubits. In effect, a mathematical pattern found throughout nature acts as a self-organizing error-management protocol. The findings suggest a powerful new strategy for quantum control. Rather than fighting noise solely with complex correction algorithms, future quantum technologies may harness structured patterns—drawn from mathematics and natural order—to achieve resilience at a fundamental level. https://lnkd.in/dVxp7R8J https://lnkd.in/dDVNRsPk

Quantum Computers Take a Leap in Self-Awareness by Analyzing Their Own Entanglement Machines Study the Very Phenomenon That Powers Them In a breakthrough that mirrors human introspection, researchers from Tohoku University and St. Paul’s School in London have enabled quantum computers to examine and optimize the very principle at the heart of their power—quantum entanglement. Published in Physical Review Letters on March 4, 2025, their work introduces a novel algorithm that could significantly advance how quantum systems detect, manage, and protect entangled states, making future quantum technologies more intelligent and efficient. The Science Behind the Discovery • Entanglement as Foundation and Subject • Quantum entanglement, famously described by Einstein as “spooky action at a distance,” is essential to the speed, security, and uniqueness of quantum computing. • The new approach allows quantum systems not just to utilize entanglement, but to study and understand it within themselves. • Variational Entanglement Witness (VEW) • The researchers developed the VEW algorithm, a quantum-based method that actively optimizes the detection of entanglement. • Unlike traditional techniques that rely on fixed mathematical criteria (and often miss complex entangled states), VEW adapts and learns during runtime to find entanglement even in challenging or noisy systems. • Self-Referential Quantum Analysis • For the first time, quantum computers are used to investigate the very quantum properties that define them, closing the loop between usage and understanding. • This creates a feedback mechanism, allowing systems to better maintain, regulate, or even enhance entanglement during computations. Broader Implications for Quantum Technology • Improved Error Detection and Correction • By giving machines the ability to assess their own entanglement states, VEW can contribute to more reliable quantum error correction, one of the biggest hurdles in quantum computing today. • Adaptive and Smarter Quantum Systems • With this self-diagnostic capability, future quantum computers could become adaptive, adjusting internal processes based on the quality and stability of entanglement. • Advancing Fundamental Research • The VEW algorithm may also aid in theoretical physics, offering a tool for studying complex entangled systems in quantum simulations and experiments. Why This Breakthrough Matters This development marks a philosophical and technological milestone: quantum computers are now not just tools for solving problems, but active participants in their own optimization. By turning entanglement—the very essence of quantum advantage—into both a computational resource and an object of study, researchers have opened new avenues for building more autonomous, resilient, and insightful quantum machines. As we edge closer to widespread quantum deployment, self-aware entanglement could be a key step toward unlocking the full potential of quantum computing.

The first time I saw machine learning in action for quantum computing was during my time at the Niels Bohr Institute, University of Copenhagen. Anasua Chatterjee and colleagues were exploring AI-driven methods to automate the tune-up of spin qubits. To be honest, I didn’t give it much attention at the time. Fast forward to today, and AI feels like the secret sauce accelerating almost every aspect of quantum computing. Think about it: quantum computing is all about mastering exponentially complex systems. AI thrives in high-dimensional, data-rich environments. This pairing? It’s like finding the perfect dance partner. Here’s what’s exciting: AI isn’t just helping to debug or optimize—it’s diving deep into the heart of quantum research. It’s designing qubits, discovering novel error correction codes, and making circuit synthesis more efficient than ever. Tasks that once took teams of researchers weeks to figure out are now becoming automated, adaptive, and scalable. One example I really like? AI-enhanced quantum error correction. Researchers are using neural networks and transformers to achieve error rates below what traditional methods can manage—and they’re doing it at a fraction of the computational cost. Another idea that’s caught my attention is quantum feedback control using transformers. This approach could change how we stabilize and steer quantum systems in real time by leveraging AI models to predict and counteract noise. The question now is: how long before we see more of these theoretical breakthroughs transition to real hardware? Natalia Ares, is quantum feedback control with transformers already in the works? This is such an exciting direction for quantum control and AI! 📸 Credits: Yuri Alexeev et al. (2024)

SCIENTISTS FED THE FIBONACCI SEQUENCE INTO A QUANTUM COMPUTER AND SOMETHING STRANGE HAPPENED. The results were astounding — it manipulates the flow of time. By applying the mathematical elegance of the Fibonacci sequence to quantum hardware, researchers have created a new phase of matter that preserves data four times longer. Physicists have achieved a major breakthrough in quantum computing by using laser pulses patterned after the Fibonacci sequence to create a stable new phase of matter. In an experiment involving a lineup of ten atoms, researchers at the Flatiron Institute discovered that blasting qubits with this mathematical rhythm allowed them to maintain their quantum state for an impressive 5.5 seconds—nearly four times longer than standard methods. This remarkable stability stems from the quasi-periodic nature of the Fibonacci sequence, which effectively creates a temporal "quasicrystal" that organizes information without repeating it, shielding the system from the environmental noise that typically crashes quantum calculations. The most mind-bending aspect of this discovery is how it manipulates the flow of time within the quantum system. Lead author Philip Dumistrescu explains that the Fibonacci pulses make the system behave as if it exists in two distinct directions of time simultaneously. This complex temporal structure acts as a protective barrier, canceling out the errors that usually live on the edges of the quantum array. By overcoming the extreme fragility of qubits, this "two-time" approach provides a much-needed path toward developing reliable, large-scale quantum computers capable of solving problems that are currently impossible for classical machines. source: Dumistrescu, P. T., et al.. Dynamical topological phases realized in a trapped-ion quantum simulator. Nature.

QUANTUM SYSTEM AT THE EDGE OF CHAOS: A PATH TOWARD STABLE QUANTUM COMPUTATION Quantum physics rarely offers moments where theory, engineering, and the raw behavior of many‑body systems collide to reveal a new dynamical regime. Yet that is exactly what the 78‑qubit Chuang‑tzu 2.0 processor has uncovered: a quantum system pushed to the brink of chaos can be held in a long‑lived, tunable prethermal state—an island of order suspended inside non‑equilibrium turbulence. This discovery goes far beyond Floquet physics. Periodic driving has already given us time crystals and engineered topological phases, but non‑periodic driving—especially with structured randomness—has long been synonymous with rapid heating and the loss of quantum information. Instead, this experiment shows that temporal randomness can be engineered to suppress heating, stabilize dynamics, and preserve coherence far longer than expected. Random multipolar driving, neither periodic nor chaotic, acts as a hidden temporal scaffold that shapes how energy flows through the system. Applied to a two‑dimensional Bose–Hubbard model across 78 qubits and 137 couplers, this protocol prevents the system from collapsing into chaos. Instead, it enters a robust prethermal plateau where imbalance decays slowly, entanglement grows in a controlled way, and the heating rate becomes tunable—matching universal algebraic scaling predicted for multipolar drives. This is not a subtle correction; it is a macroscopic reshaping of the system’s dynamical landscape. The geometry of entanglement is equally striking. Different subsystems show distinct behaviors—some oscillate coherently, others settle into plateaus—revealing a highly non‑uniform spread of correlations across the lattice. It is the first time such fine‑grained entanglement dynamics have been observed in a large, non‑periodically driven quantum simulator. Classical tensor‑network methods like GMPS and PEPS cannot keep pace once heating accelerates, confirming that these dynamics lie firmly beyond classical reach. Quantum systems at the brink of chaos are not doomed to disorder. With the right temporal geometry, they can be shaped, stabilized, and made computationally powerful. This work demonstrates that the boundary between coherence and chaos is not a hard limit but a navigable frontier—and that the future of quantum computation may lie precisely in mastering this edge. # https://lnkd.in/eJBkGts5

I’m excited to share two new papers from our NVIDIA Quantum Applied Research team that push the frontier of quantum error correction — both in theory and in how we accelerate progress toward scalable quantum systems. What I love about both of these works is how they show that relaxing certain theoretical assumptions can reveal more hardware‑compatible pathways for fault‑tolerant operations. A broadened design space means more opportunity for optimization for hardware. The first, led by Rui Chao, develops new morphing circuit constructions that enable data and ancilla qubits to dynamically take on either role under realistic hardware constraints. By expressing these circuits in block‑algebra form with explicit connectivity requirements, the work clarifies how to implement morphing using only CNOTs while limiting how many other qubits each qubit must interact with — opening up a more flexible and hardware‑aware design space. The second, led by Adam Holmes, advances our understanding of logical operations in stabilizer codes and resolves a long‑standing open question. This work opens a new direction for designing logical gates on encoded qubits by examining how modest increases in effective connectivity expand what is possible. With slightly higher connectivity, a much larger space of low‑overhead code constructions becomes accessible. The work introduces Quantum Logic Codes, a scalable family supporting a constant‑depth transversal Clifford operation set, showing how relaxing assumptions about code structure and connectivity can lead to more efficient logical operations and reduced overhead. Together, these works highlight how AI‑accelerated co‑design and advanced search and optimization techniques can reduce the cost of fault tolerance and speed our path to quantum scale. Congratulations to the team! We’re excited to see how the community builds on and extends these QEC contributions to help advance the quantum ecosystem. https://lnkd.in/e9Cg9NGu https://lnkd.in/eqphJBnj #QEC #quantumcomputing #NVIDIAquantum #quantumAI

Quantum simulation of dissipation for Maxwell equations in dispersive media  https://lnkd.in/euBxG4b5 Abstract: The dissipative character of an electromagnetic medium breaks the unitary evolution structure that is present in lossless, dispersive optical media. In dispersive media, dissipation appears in the Schrodinger representation of classical Maxwell equations as a sparse ¨ diagonal operator occupying an r-dimensional subspace. A first order Suzuki-Trotter approximation for the evolution operator enables us to isolate the non-unitary operators (associated with dissipation) from the unitary operators (associated with lossless media). The unitary operators can be implemented through qubit lattice algorithm (QLA) on n qubits, based on the discretization and the dimensionality of the pertinent fields. However, the non-unitary-dissipative part poses a challenge both physically and computationally on how it should be implemented on a quantum computer. In this paper, two probabilistic dilation algorithms are considered for handling the dissipative operators. The first algorithm is based on treating the classical dissipation as a linear amplitude damping-type completely positive trace preserving (CPTP) quantum channel where an unspecified environment interacts with the system of interest and produces the non-unitary evolution. Therefore, the combined system-environment is now closed, and must undergo unitary evolution in the dilated space. The unspecified environment can be modeled by just one ancillary qubit, resulting in an implementation scaling of O(2n−1n 2 ) elementary gates for the total system-environment unitary evolution operator. The second algorithm approximates the non-unitary operators by the Linear Combination of Unitaries (LCU). On exploiting the diagonal structure of the dissipation, we obtain an optimized representation of the non-unitary part, which requires O(2n ) elementary gates. Applying the LCU method for a simple dielectric medium with homogeneous dissipation rate, the implementation scaling can be further reduced into O[poly(n)] basic gates. For the particular case of weak dissipation we show that our proposed postselective dilation algorithms can efficiently delve into the transient evolution dynamics of dissipative systems by calculating the respective implementation circuit depth. A connection of our results with the non-linear-in-normalization-only (NINO) quantum channels is also presented.