Quantum Systems for Analog Simulation

For those tracking progress in Quantum… As my colleague Hartmut Neven has predicted, real-world applications possible only on quantum computers are much closer than people think – as near as five years, even though fully error corrected quantum computers may be further away.  Recently, my colleagues on our Quantum AI team at Google Research took another important step on that path with a new set of results we published last week in Nature that share a promising new approach to applications on today’s quantum computers. Our analog-digital quantum simulator using super-conducting qubits shows performance beyond the reach of classical simulations in cross-entropy benchmarking experiments. Simulations with the level of experimental fidelity in this simulator would require more than a million years on a Frontier supercomputer. The simulator brings together digital’s flexibility and control with the analog’s speed – and provides a path towards applications that cannot be accomplished on a classical computer. Along the way, my colleagues also made a scientific discovery – they observed the breakdown of a well-known prediction in non-equilibrium physics, the Kibble-Zurek mechanism - an important result in our understanding of magnetism, and also useful in various kinds of quantum simulations. Congratulations to Trond Andersen, Nikita Astrakhantsev, and the rest of the team on this exciting step – much more to come! You can read the Nature paper here: https://lnkd.in/gg2En5qe 

The Schrödinger Equation Gets Practical: Quantum Algorithm Speeds Up Real-World Simulations Quantum computing has taken a major leap forward with a new algorithm designed to simulate coupled harmonic oscillators, systems that model everything from molecular vibrations to bridges and neural networks. By reformulating the dynamics of these oscillators into the Schrödinger equation and applying Hamiltonian simulation methods, researchers have shown that complex physical systems can be simulated exponentially faster on a quantum computer than with traditional algorithms. This breakthrough demonstrates not only a practical use of the Schrödinger equation but also the deep connection between quantum dynamics and classical mechanics. The study introduces two powerful quantum algorithms that reduce the required resources to only about log(N) qubits for N oscillators, compared to the massive computational demands of classical methods. This exponential speedup could transform fields such as engineering, chemistry, neuroscience, and material science, where coupled oscillators serve as the backbone of real-world modeling. By bridging theory and application, this research underscores how quantum computing is redefining problem-solving in physics and beyond. With proven exponential advantages and the ability to simulate systems once thought computationally impossible, this quantum algorithm marks a milestone in quantum simulation, Hamiltonian dynamics, and real-world physics applications. The findings point toward a future where quantum computers can accelerate scientific discovery, optimize engineering designs, and even open new frontiers in AI and computational neuroscience. #QuantumComputing #SchrodingerEquation #HamiltonianSimulation #QuantumAlgorithm #CoupledOscillators #QuantumPhysics #ComputationalScience #Neuroscience #Chemistry #Engineering

Quantum-centric supercomputing is a new architecture where both a classical and quantum computer are used together to investigate a computation problem. Sample-based Quantum Diagonalization (SQD) has emerged as one of the leading algorithm for this architecture and it allows the simulation of the electronic structure. It has been used to look at electronic structure of iron sulfides (https://lnkd.in/eK8jW-Wp) and water and methane dimers (https://lnkd.in/epgUJeD8) and in this work (https://lnkd.in/eqh8J96M) our team working with Lockheed Martin have explored how SQD can be used to study molecular dissociation for both open-shell ground states and closed-shell excited states across different symmetry sectors. The study uses a CH2 molecular system, which is relevant for both interstellar and combustion chemistry. The circuits used are LUCJ ansatz and are executed on quantum hardware at a scale of 52 qubits and 3000 two-qubit gates. The results for the CH2 singlet state showed close alignment with Selected Configuration Interaction (SCI) calculations, with deviations of only a few mEh, while triplet state results also maintained reasonable accuracy within a few mEh at equilibrium. This work also marks the first SQD analysis of quantum phase transitions resulting from level crossings, expanding SQD’s applicability to new quantum phenomena. While there is still a lot of fundamental research to be done, given these results we can see a future in modeling larger radicals, transient species, and complex combustion reactions which will have Implications to the aerospace industry and beyond. If you want to get started with SQD check out https://lnkd.in/e6TuS5AZ.

Over the years, quantum computing has been judged mostly by its limitations — especially the gap between what today’s hardware can achieve and what classical algorithms can simulate. But the truth is more subtle and more exciting: the classical tools we rely on to simulate accurately quantum systems, like chemical compounds and materials, also have deep, well-known limitations. At Algorithmiq, we have been exploring how to turn this tension into something useful: a way to design and control information flow in artificial quantum materials, and to map out where classical methods begin to break while quantum methods provide reliable information. Why does this matter beyond physics? Because these simulations lies at the heart of the key industries driving the next decade: - catalytic processes for decarbonisation, - solid-state battery interfaces, - complex energy materials, - high-coherence quantum devices, - and next-generation computational chemistry. The challenge is that classical simulation becomes unreliable in precisely the regimes where these systems become most interesting — where disorder, interference, and entanglement govern their behaviour. We show that by pushing both quantum processors and classical algorithms into these hard regimes, we are beginning to see how quantum hardware can reveal properties impossible to discover with classical methods. Our initial evidence of quantum advantage for a useful use case is not just a scientific milestone — it is the early evidence of a technology crossing into real-world relevance. And challenges matter. They inspire people, create accountability, and accelerate progress. This is why I believe the Quantum Advantage Tracker, launched yesterday together with IBM Quantum, represents a turning point. It introduces the transparency, verification, and community benchmarking that every emerging technology needs to mature — and that investors rightly expect before deploying large-scale capital. We have published a detailed technical blog post explaining why information-flow modeling in artificial materials may become one of quantum computing’s most powerful use cases. 🔗 Link in the comments #QuantumComputing #QuantumAdvantage #InvestingInScience #DeepTech #MaterialsInnovation #Benchmarking #QDC2025 #QuantumMaterials #OpenScience

⚛️ Harnessing Intrinsic Noise for Quantum Simulation of Open Quantum Systems 📑 Simulating open quantum systems on quantum computers presents a fundamental challenge: open quantum dynamics are intrinsically nonunitary, whereas quantum computers operate through unitary evolution. Conventional approaches overcome this mismatch by encoding nonunitary processes into unitary circuits, but such methods incur substantial overhead in both qubits and gates. Here, we propose an alternative perspective. Quantum processors are themselves open systems, inherently subject to noise. Instead of correcting all errors and then encoding nonunitary dynamics with unitary logical qubits and gates, we show how noise can be harnessed as a computational resource. We develop a noise-assisted quantum algorithm that selectively preserves physical noise to emulate nonunitary channels, enabling efficient simulation of open quantum dynamics with minimal qubit requirements. Our approach applies both to noisy intermediate-scale quantum (NISQ) devices and future fault-tolerant architectures. By leveraging intrinsic noise, this method circumvents the need to encode nonunitary dynamics into unitary gates and relaxes fidelity requirements on physical qubits, thereby reducing the overhead of quantum error correction. This framework reframes noise from a limitation into a resource, opening new directions for practical quantum simulation of open systems ℹ️ Dambal et al - 2025

"STRING BREAKING" AND FLUX TUBE DYNAMICS IN (2+1) DIMENSIONAL QUANTUM SYSTEMS Using a programmable neutral-atom quantum simulator, research team at the University of Innsbruck, Harvard, and QuEra Computing evaluated the string breaking in a (2+1)D lattice gauge theory (LGT), a tabletop analog of quark confinement in quantum chromodynamics (QCD). In QCD, a significant amount of energy is concentrated within the gluon flux tube that confines a quark–antiquark pair. As a quark–antiquark pair is pulled apart, the energy confined within the gluonic flux string connecting them increases linearly with separation. This stored energy eventually becomes sufficient to nucleate new quark–antiquark pairs from the vacuum, resulting in the effective fragmentation of the original flux tube, string, into the shorter segments. In high-energy particle collisions, this mechanism results in the indirect observation of quarks through the formation of "jets", streams of secondary particles generated by the sequential breaking of the color field. Analogous phenomena are widespread across gauge theories that mirror the structure of QCD, particularly in lattice formulations governed by local gauge symmetry constraints. These models capture confinement and string-breaking dynamics through discrete spacetime representations, offering a powerful framework to investigate nonperturbative field behavior. Despite their ubiquity, capturing LGTs real-time evolution through simulation remains a profound computational challenge. Utilizing QuEra’s Aquila neutral-atom quantum simulator and programmable Rydberg atom array, researchers configured several rubidium atoms into a kagome-geometry optical-tweezer array, effectively implementing a synthetic LGT that mirrors the confinement dynamics of strong nuclear force in QCD. By finely tuning the laser control parameters, the team engineered and dynamically extended flux-tube-like structures between synthetic charges. As the simulated energy scale approached, the experiment captured both static confinement signatures and nanosecond-scale, real-time evolution of the string rupture, pushing beyond the reach of classical computational methods. In contrast to efforts employing digital quantum processors, these experiments revealed that string breaking is a genuine many-body phenomenon, which arises from physical realization of a confining U(1) LGT with dynamical matter, mapped directly onto the system’s Hamiltonian. Unlike prior implementations limited to (1+1)D configurations, this approach facilitates the construction of an equilibrium phase diagram, characterizing regimes of broken and unbroken strings in (2+1)D space-time, achieved through finely tunable local control and quasi-adiabatic state preparation and controlled quench of local parameters on preconfigured flux tubes, providing access to the real-time dynamics of confinement and string breaking with the fine spatio-temporal resolution. # https://lnkd.in/eKgfAFPb

Simulating spectroscopy is the the way to go for quantum computers. Therefore I am proud to share new joint work from our team at HQS together with colleagues from the Institute for Photonic Quantum Systems in Paderborn. The paper asks a simple question with practical consequences: do we always need the full power of Gaussian Boson Sampling to simulate vibronic spectra with photonics? Our results show that for many molecules, the answer is no. By mapping standard approximations from physical chemistry to photonic hardware, we demonstrate that simplified architectures—up to and including implementations based only on coherent light with photon‑number resolving detection—can reproduce vibronic spectra with high accuracy. We also identify where these simplifications break down and a complete GBS setup becomes necessary, for example when strong Duschinsky mixing and frequency changes are at play. This matters for anyone building useful quantum‑photonic workflows. It reduces experimental complexity, improves robustness, and gives a clear decision rule: start with the simpler photonic approximations and escalate to full GBS only when the molecular physics demands it. I’m grateful to our collaborators for a rigorous joint effort and to the HQS team, with special thanks to my colleague Vladimir Rybkin, for bringing chemical intuition and photonic practicality together. The preprint is now available; happy to share details and discuss how these insights could translate into real simulation pipelines for chemistry and materials in the future.

By blending digital control with analog simulations, scientists have created a powerful new quantum simulator that pushes beyond traditional limitations. This hybrid system allows precise manipulation of quantum states while naturally modeling real-world physics, enabling breakthroughs in fields like magnetism, superconductors, and even astrophysics. Physicists working in Google’s laboratory have developed a new type of digital-analog quantum simulator, capable of studying complex physical processes with unprecedented precision and adaptability. Consider the simple act of pouring cold milk into hot coffee — how does it spread and mix? Even the most advanced supercomputers struggle to model this process with high accuracy because the underlying quantum mechanics are incredibly complex. In 1982, Nobel Prize-winning physicist Richard Feynman proposed an alternative: instead of using classical computers, why not build quantum computers that can directly simulate quantum physical processes? Now, with rapid advancements in quantum computing, Feynman’s vision is closer than ever to becoming reality. https://lnkd.in/gjNP8q3e #universal #simulation #3Ddigitaltwin #Feynman #quantum

D-Wave researchers have published findings in Science demonstrating that their quantum annealing processors can simulate quantum spin glass dynamics more efficiently than leading classical methods. The study shows their quantum computers can perform simulations in minutes that would reportedly take classical supercomputers millions of years, marking a significant step toward practical quantum advantage in scientific applications. #QuantumComputing #DWave #QuantumSimulation #SpinGlass #ComputationalPhysics #QuantumSupremecy