Quantum Computing Tools for Algorithm Designers

Most people hear "quantum computing" and think: not for me. Too theoretical. Too far away. Maybe someday. These past two weeks have been a fire hose of learning. I've gotten to see what different teams are building and some of it genuinely stopped me in my tracks. Some things are still on the horizon. But others are here, right now, and they're remarkable. The team behind the QDK (Quantum Development Kit) demoed their January release in a meeting, which also includes contributions from the Error Correction and Chemistry teams and maybe some others. Count me as impressed. It's fully open source and here's what's in it: A Chemistry extension that optimizes molecular modeling for near-term quantum hardware, reducing circuit complexity by orders of magnitude in some cases. If you work in pharma, materials science, or computational chemistry, this was built for you. An Error Correction toolkit with open source modules for designing and testing fault-tolerant quantum programs. If you're a researcher pushing the boundaries of reliable quantum systems, this was built for you. Full GitHub Copilot integration for AI-assisted quantum programming, from code generation to hardware submission. If you're a developer who knows Python but not quantum, this was built for you too. What I keep coming back to is this: the people who built these tools spent countless hours making something that works so simply that we might never fully appreciate how hard it was to get here. That's the kind of work that quietly moves an entire field forward. If you've been waiting for a sign that quantum is ready for curious people, here it is. https://lnkd.in/g4YrE9Xm #QuantumComputing #Python #OpenSource #QDK #Microsoft

Last week, at IBM Quantum Developer Conference (#QDC25), we updated dynamic circuits, a powerful tool for researchers exploring algorithms and applications that use concurrent classical compute. Learn more on the IBM Quantum blog: https://lnkd.in/ev5pbEHg Dynamic circuits give us the ability to incorporate real-time classical logic within the execution time of quantum circuits. With this capability, we can implement many complex quantum protocols shallow circuit depth—greatly expanding the level of complexity we can explore with today’s quantum computers. We’ve seen many promising demonstrations over the years highlighting how valuable this capability can be, with researchers leveraging dynamic circuits to make exciting progress in quantum error correction (https://lnkd.in/eTbk7ZXp), long-range qubit entanglement (https://lnkd.in/eq4MA5Ch), and complex state preparation protocols (https://lnkd.in/eqijrtCH). However, most of these demonstrations were impossible to scale with the original dynamic circuits implementation we released back in 2022. The new utility-dynamic circuits deliver powerful new features and sizable performance improvements to remove those barriers and enable Qiskit Runtime users to explore their full potential. Key new features include parallel execution for independent sets of conditional operations, a new stretch duration feature that helps you better express timing intent in the design of your circuits, an optimized new `MidCircuitMeasure` instruction, and a new circuit-timing visualization tool to facilitate circuit debugging and optimization, and more. Performance improvements are equally impressive with mid-circuit measurement nearly a full microsecond faster than the prior implementation (65% improvement in duration for dynamic circuit runs), feedforward time down to ~600 nanoseconds, and a 20x speedup in wall clock time for circuit preparation (really a 400x speedup in CPU time thanks to better resource utilization). We put these speedups to work running a 46-site kicked Ising Hamiltonian simulation experiment on 106 qubits—a larger version of an experiment previously studied in the 2024 Qiskit white paper (https://lnkd.in/eze8cRJf). Utility-scale dynamic circuits delivered a a 28% reduction in two-qubit gates for each Trotter step, and up to a 24% improvement in performance over corresponding unitary circuits. This new capability is still a work in progress and we have many additional features and performance improvements in the works. However, they are already proving to be an exciting tool for exploration with enormous potential to accelerate the journey to quantum advantage. Read the blog linked above or take a look at our documentation (https://lnkd.in/e6bESry9) to get started with them today.

I'm really happy with the rapid development of CUDA-Q QEC, our toolkit for quantum error correction. QEC is an incredibly rich and fast-moving field, and in CUDA-Q QEC we aim to provide a platform with a diverse set of accelerated decoders, AI infrastructure, tools to enable researchers to develop and test their own codes, decoders, and architectures, hopefully even better than our own! As we dig deeper into the problem of scalable QEC, the benefits of GPUs and AI have become much clearer. We started with research tools, for simulation and offline decoding, which is still an important capability. Now with the 0.5.0 release we also provide the infrastructure for real-time decoding, where syndrome processing occurs concurrently with quantum operations. This release also introduces GPU-accelerated algorithmic decoders like RelayBP, a promising approach developed in the past year that aims to overcome the convergence limitations of traditional belief propagation. For scenarios demanding maximum throughput, we have integrated a TensorRT-based inference engine that allows researchers to deploy custom AI decoders trained in frameworks like PyTorch and exported to ONNX directly into the quantum control loop. To address the complexities of continuous system operation, we added sliding window decoders that handle circuit-level noise across multiple rounds without assuming temporal periodicity. These tools are designed to be hardware-agnostic and scalable, supporting our partners across the ecosystem who are building the first generation of reliable logical qubits. Check out the full technical breakdown in our latest developer blog by Kevin Mato, Scott Thornton, Ph.D., Melody Ren, Ben Howe, and Tom L. https://lnkd.in/gvC__zRd

Qiskit Functions: A Potential Masterstroke by IBM Quantum computing has long been viewed as a powerful but inaccessible technology, requiring deep expertise in quantum physics and specialized hardware optimization. IBM's Qiskit Functions, launched in September 2024, are changing this narrative by making quantum computing as easy to use as calling a regular software function. We have all struggled with understanding QPUs, mastering error suppression techniques and spending countless hours optimising circuits for specific hardware. This massive barrier between quantum computing's potential and the users, especially researchers in chemistry, finance, logistics, and other fields, could be solved by Qiskit Functions! 💠 IBM has created two types of Qiskit Functions to serve different user needs: a) Circuit Functions -  Designed for researchers who want to work with quantum circuits but don't want to deal with hardware optimisation headaches. - These functions handle the complex tasks of circuit transpilation, error suppression, and error mitigation automatically. b) Application Functions - These functions take abstraction one level further, accepting regular data as input and returning regular results.  - A chemist can input a molecular structure and get back energy calculations without ever seeing a quantum circuit or worrying about quantum measurements. 🌐 A Growing Ecosystem of Partners Rather than going it alone, IBM has built Qiskit Functions as a collaborative platform. Leading quantum companies contribute specialised capabilities: - Q-CTRL provides AI-driven quantum control techniques that have enabled world records in quantum computing - Algorithmiq offers tensor-network error mitigation that reduces the number of quantum measurements needed - QEDMA contributes quantum error suppression protocols based on detailed hardware characterisation - QunaSys focuses on quantum chemistry simulations for drug discovery and materials science - Kipu Quantum provides optimisation algorithms that outperform both classical methods and existing quantum approaches Qiskit Functions are designed to evolve with this advancing hardware. But they are currently limited for use. Having said that, these functions remain constrained by today's hardware, but this step is definitely in the right direction. Since these functions are an experimental feature IBM Quantum Cloud platform (https://lnkd.in/dUUaG4Ge). What do you think, is this feature a masterstroke or a miss? #ibm #qiskit #quantumcomputing #quantumtechnology #functions Image Credit: IBM Quantum