Methods for Accurate Quantum Process Simulation

Today in Science Magazine, work from our IBM team, in collaboration with The University of Manchester, University of Oxford, ETH Zürich, EPFL and the University of Regensburg, shows the creation and simulation of a new molecule with an electronic structure that has never existed before — a half‑Möbius topology: https://lnkd.in/eFU5s9qR. The molecule was assembled using scanning probe microscopy at temperatures just above absolute zero — building it one atom at a time using STM, atom manipulation, and AFM. The electronic orbitals of this half‑Möbius molecule twist by 90 degrees with every loop around the ring, completing a full turn only after four revolutions. Why is this also important for quantum computing? This work demonstrates, for the first time, that quantum computing calculations can provide decisive scientific guidance and powerful characterization capabilities to support the discovery of new complex chemical molecules. In close collaboration with leading experimental laboratories, quantum simulations can now contribute directly to interpreting experimental observations and to guiding the design and understanding of novel molecular systems. The calculations performed in this project go well beyond the regime accessible to brute-force classical simulations, although we do not exclude the possibility that approximate classical methods could also provide valuable insights. Nevertheless, the discovery process itself benefited from quantum simulation, and we chose to employ quantum computing because it offers a natural and scalable framework for tackling problems of this kind. In particular, by comparing Dyson orbitals measured with scanning tunneling microscopy (STM) with images reconstructed from electronic structure calculations performed on a quantum computer using the SqDRIFT algorithm, we were able, for the first time, to contribute directly to the discovery and characterization of a new molecule exhibiting entirely novel electronic structure properties. paper: https://lnkd.in/esg9sHqV

Quantum Simulator Merges Digital and Analog Modes for Unprecedented Precision in Physics Calculations Scientists from Google and universities across five countries, in collaboration with theoretical physicists Andreas Läuchli and Andreas Elben at PSI, have developed a groundbreaking digital-analog quantum simulator capable of calculating complex physical processes with unprecedented precision. Their research, published in Nature on February 5, brings us closer to realizing Richard Feynman’s 1982 vision of quantum simulation as a superior alternative to classical computing for physics problems. Key Advancements • Overcoming Classical Computing Limitations: • Even the fastest supercomputers struggle with simulating quantum processes, such as how cold milk disperses in hot coffee. • Quantum simulators, unlike classical computers, can efficiently model quantum behaviors by replicating the underlying physics within their own quantum states. • Hybrid Digital-Analog Approach: • The new simulator combines digital quantum gates with high-fidelity analog evolution, allowing it to simulate a broader range of physical systems than purely digital or purely analog approaches. • This flexibility enables simulations across solid-state physics, condensed matter, and even astrophysical processes. • Scalability and Precision: • Unlike previous quantum simulators, this design is highly scalable, making it applicable to a wide range of scientific problems with higher accuracy than classical models. Why This Matters • Accelerating Scientific Discoveries: The simulator can model real-world physical systems more efficiently, impacting materials science, quantum chemistry, and fundamental physics. • Bridging the Gap Between Theory and Experimentation: The ability to simulate quantum interactions with extreme accuracy allows researchers to test theoretical models that were previously impossible to verify. • Potential for a Quantum Computing Breakthrough: This hybrid approach demonstrates the power of quantum simulation, potentially leading to practical, scalable quantum computers capable of solving real-world problems. What’s Next? • Expanding the Simulator’s Applications: Researchers will explore how this hybrid digital-analog approach can be applied to more complex quantum systems. • Scaling Up Quantum Simulations: Larger quantum processors will be tested to further push the limits of computational physics. • Collaboration with Industry & Research Institutions: Google and academic institutions are likely to integrate this technology into broader quantum computing efforts, enhancing its practical applications. This milestone in quantum simulation represents a major step toward realizing quantum computing’s potential, proving that hybrid quantum approaches may be the key to unlocking the next era of scientific computing.

⚛️ A Rigorous Introduction to Hamiltonian Simulation via High-Order Product Formulas 📑 This work provides a rigorous and self-contained introduction to numerical methods for Hamiltonian simulation in quantum computing, with a focus on high-order product formulas for efficiently approximating the time evolution of quantum systems. Aimed at students and researchers seeking a clear mathematical treatment, the study begins with the foundational principles of quantum mechanics and quantum computation before presenting the Lie-Trotter product formula and its higher-order generalizations. In particular, Suzuki’s recursive method is explored to achieve improved error scaling. Through theoretical analysis and illustrative examples, the advantages and limitations of these techniques are discussed, with an emphasis on their application to k-local Hamiltonians and their role in overcoming classical computational bottlenecks. The work concludes with a brief overview of current advances and open challenges in Hamiltonian simulation. ℹ️ Javier Lopez-Cerezo - Department of Applied Mathematics - University of Malaga - Spain - 2025

In finance, Monte Carlo simulations help us to measure risks like VaR or price derivatives, but they’re often painfully slow because you need to generate millions of scenarios. Matsakos and Nield suggest something different: they build everything directly into a quantum circuit. Instead of precomputing probability distributions classically, they simulate the future evolution of equity, interest rate, and credit variables inside the quantum computer, including binomial trees for stock prices, models for rates, and credit migration or default models. All that is done within the circuit, and then quantum amplitude estimation is used to extract risk metrics without any offline preprocessing. This means you keep the quadratic speedup of quantum MC while also removing the bottleneck of classical distribution generation. If you want to explore the topic further, here is the paper: https://lnkd.in/dMHeAGnS #physics #markets #physicsinfinance #derivativespricing #quant #montecarlo #simulation #finance #quantitativefinance #financialengineering #modeling #quantum

QPINN (QUANTUM PHYSICS-INFORMED NEURAL NETWORK) Quantum physics informed neural networks for multi-variable partial differential equations https://lnkd.in/ejaUxnts Quantum Physics-Informed Neural Networks (QPINNs) integrate quantum computing and machine learning to impose physical biases on the output of a quantum neural network, aiming to either solve or discover differential equations. The approach has recently been implemented on both the gate model and continuous variable quantum computing architecture, where it has been demonstrated capable of solving ordinary differential equations. Here, we aim to extend the method to effectively address a wider range of equations, such as the Poisson equation and the heat equation. To achieve this goal, we introduce an architecture specifically designed to compute second-order (and higher-order) derivatives without relying on nested automatic differentiation methods. This approach mitigates the unwanted side effects associated with nested gradients in simulations, paving the way for more efficient and accurate implementations. By leveraging such an approach, the quantum circuit addresses partial differential equations – a challenge not yet tackled using this approach on continuous-variable quantum computers. As a proofof-concept, we solve a one-dimensional instance of the heat equation, demonstrating its effectiveness in handling PDEs. Such a framework paves the way for further developments in continuous-variable quantum computing and underscores its potential contributions to advancing quantum machine learning. 

🚀 A team from Quantum Elements, University of Southern California, Harvard University, and Amazon Web Services (AWS) demonstrated hardware-faithful, distance-7 surface code quantum error correction simulations powered by AWS high-performance computing infrastructure - read the details in our blog today Faithful simulation of real quantum noise is computationally challenging at meaningful scale. This collaboration has opened up new techniques with classical HPC, simulating a full 97-qubit distance-7 surface code (on par with the largest experimental demonstrations to date) in about 75 minutes on a single compute node. The team built on a real-time quantum Monte Carlo method that stochastically compresses density-matrix evolution and, running on Amazon EC2 Hpc7a instances orchestrated by AWS ParallelCluster, they simulated a single syndrome-extraction round of a distance-7 rotated surface codes. This work was carried out entirely using Amazon EC2 HPC infrastructure - a great example of how working with AWS can enable foundational quantum research through classical cloud resources as well as quantum computing through Braket. 👍 Huge thanks to the team at Quantum Elements, Arian Vezvaee, Daniel Lidar, Huo Chen, Izhar Medalsy and Tong Shen, along with Amazon Web Services (AWS) team Tyler Takeshita, Benchen Huang and Sebastian Hassinger 📄 https://lnkd.in/gyi7WpH6 👋Thierry Pellegrino Kirti Devi Andrea Rodolico Mark Sauceda Dominic Young Anh Tran #QuantumComputing #AWS #AmazonBraket #QuantumResearch #QuantumErrorCorrection #QEC #DigitalTwin #HPC

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

🚀 New Paper: Simulating Quantum Materials on Quantum Computers  🚀 In our new scientific article, we use Pauli Path Simulation (PPS) in the BlueQubit SDK as a practical tool for utility-scale quantum state preparation in quantum materials -- from spin models and phase diagrams to topological excitations. Why it matters for materials: 🔹 Predict ground-state energies and order parameters to map phase boundaries and structure–property behavior 🔹 Probe frustration and topology (e.g., Kitaev-type interactions) relevant to spin-liquids and next-gen devices Results (from our latest publication): ⚛️ 48-qubit Kitaev honeycomb on Quantinuum hardware with ~5% relative energy error 📈 PPS outperforms DMRG in select 2D Ising regimes 🌀 First anyon braiding beyond fixed-point models on real quantum hardware Big shoutout to the BlueQubit team – Cheng-Ju Lin and Vincent Su – for driving this forward. Read the full study: https://lnkd.in/d9m9hh87 #QuantumComputing #QuantumMaterials #CondensedMatter #PauliPathSimulation #TopologicalOrder #KitaevModel #IsingModel #MaterialsDiscovery

Our new Pauli Path Simulator paper is out! 🎉 It highlights a powerful new approach for simulating quantum circuits beyond brute-force limits — think 40+ qubits, especially when you're interested in computing expectation values of observables 🧠 🔵 Most simulators tap out at 35-40 qubits without insane compute requirements 🔵 PPS takes a smarter route — it approximates expectation values using multiple "paths" through the circuit, bypassing the need to simulate the full statevector And the best part is: 🔵 It’s super easy to run these simulations on the BlueQubit platform 🔵 There is a code snippet in the paper you can literally copy-paste and get results out of the box This is exactly the kind of stuff we love doing at BlueQubit — taking cutting-edge quantum techniques and making them simple and accessible to everyone 🚀 🔗 Arxiv link to the paper in the comments #QuantumComputing #BlueQubit #QuantumSimulation #PPS