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Core quantum computing developments, breakthroughs, and innovations

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Finding the fingerprints of noisy distributions in random quantum systemsquantum-computing

Finding the fingerprints of noisy distributions in random quantum systems

By Perimeter Institute Imagine shuffling a deck of cards where some of the cards are fading just because of the way they interact with the external environment as you shuffle them.Your job is to figure out the interplay between the shuffling or “imposed randomness” due to the way you shuffle your cards, and how that relates to the fading as cards are exposed to the external environment.That’s a rough analogy for what Matthew Duschenes and his PhD advisors were doing in a paper that received an Editors’ Suggestion in the American Physical Society (APS) journal Physical Review A. An Editors’ Suggestion denotes a paper of importance, impact, and intrinsic interest.Duschenes is currently a postdoctoral researcher at the University of Guelph, and he earned his PhD at Perimeter Institute and the Institute for Quantum Computing (IQC) with the late Ray Laflamme, a Perimeter founding faculty member and founding director of the Institute for Quantum Computing, who passed away in 2025. The paper bears the names of Laflamme and other advisors — Juan Carrasquilla, a Perimeter visiting fellow who is currently at ETH Zurich in Switzerland, and Roger Melko, a Perimeter associate faculty member who is also at the University of Waterloo. by Perimeter Institute

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The art of communicating quantum to the massesquantum-computing

The art of communicating quantum to the masses

Institute for Quantum Computing alum and Faculty of Arts graduate student is bridging science and communications to improve quantum science literacy By Naomi GrosmanThe discovery of the new mathematics and physics of quantum mechanics 100 years ago unlocked a new frontier for technological progress. It also necessitates a new way to talk about the science — it’s easier to understand the forces behind why an apple falls from a tree compared to the interaction of light and matter as small as single particles.Institute for Quantum Computing (IQC) alum Fiona Thompson (MSc '24) is a PhD student in English within the University of Waterloo’s Faculty of Arts. Her graduate studies put an unconventional spin on the interdisciplinary nature of quantum research. Instead of staying in a research lab after receiving a Master of Science in Quantum Information from the Department of Chemistry, she decided to pursue a PhD with a focus on the rhetoric of science and best practices for communicating about emergent quantum technologies.She was recently awarded the 2026 Canada Graduate Research Scholarship (CGRS), administered by the Social Sciences and Humanities Research Council (SSHRC). The award for doctoral students is valued at $40,000 per year for three years and replaces the Vanier scholarship. “I'm interested in how we can use methods from rhetorical studies, semiotics and literary analysis to better understand the relationship between experimental quantum science and language. Being in Waterloo’s quantum environment and listening to researchers talk about their areas of expertise inspired me to understand more about language and how we could help non-experts better grasp quantum concepts and also give scientists impactful language to articulate their research.” - Fiona Thompson, IQC Scientific Outreach Officer and PhD student in English, University of Waterloo’s Faculty of Arts on Waterloo News

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Unique quantum technology Master’s program opens doors to diverse careersquantum-computing

Unique quantum technology Master’s program opens doors to diverse careers

After completing an undergraduate degree in mathematical physics, Jeremie Corkery (BSc ‘25) wanted to maximize his career opportunities and gain lab experience. The Master of Science degree in Physics with a specialization in Quantum Technology checked all the boxes, opening doors in the quantum industry job market and creating a niche for a career in more traditional industries. The 12-month MSc Physics – Quantum Technology specialization program is the only course-based option for a master's degree in physics at the University of Waterloo. It is offered by the Institute for Quantum Computing (IQC) in partnership with the Department of Physics and Astronomy and the Transformative Quantum Technologies (TQT) research initiative at the University of Waterloo. “It’s a demanding program that gives an overview of the field and is perfect if you want to get a taste of working in quantum technology or still deciding on an academic path—it builds a large toolbox to take into any career”- Jeremie Corkery, MSc Physics – Quantum Technology specializationCorkery says what he learned in the Master’s program is a strong differentiator in his work as Data Science and AI Associate at consultancy firm PricewaterhouseCoopers (PwC). Particularly as quantum specialization today can be compared to a specialization in artificial intelligence a decade ago. “Quantum technology companies are gaining traction, and there is also an opportunity to bring quantum expertise to broader industry and be the subject matter expert,” Corkery says. “You’ll get out of the program what you put in. Whether you want to take classes and network to prepare for a PhD, get to know quantum startups or prepare for a quantum industry job—if you’re ready to work hard this degree will help you stand out.” The program’s main activities take place at the Research Advancement Centre (RAC) just north of University of Waterloo’s main campus where there is dedicated lab space. Students also have access to events and semin

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A Quantum Breakthrough Makes Light Behave Like Both a Solid and a Liquidquantum-computing

A Quantum Breakthrough Makes Light Behave Like Both a Solid and a Liquid

Scientists have demonstrated an extraordinary form of quantum matter that combines two properties that seem incompatible. Known as a supersolid, this unusual state has the orderly structure of a crystal while also displaying the frictionless flow associated with a special kind of liquid. Even more remarkably, researchers have now observed this behavior at room temperature, eliminating the need for the extreme cooling traditionally used to investigate such phenomena.The breakthrough was achieved by an international team from the Faculty of Physics at the University of Warsaw, the Military University of Technology (Poland), IBM Research Zurich, and the French National Centre for Scientific Research (CNRS). Rather than relying on atoms cooled to temperatures near absolute zero, the scientists created a supersolid using particles that combine properties of light and matter.Published in Science Advances, the findings introduce a new way to investigate exotic quantum behavior under much more accessible conditions. They could also help advance future technologies ranging from optical computing to highly sensitive sensors.How Can Matter Behave Like Both a Solid and a Liquid?Under ordinary circumstances, solids and liquids behave very differently. Crystals have an organized internal structure, with their particles arranged in repeating patterns. Liquids, by comparison, can flow and change shape.Quantum physics, however, allows matter to behave in ways that would be impossible under the familiar rules of everyday life.One example is superfluidity, a remarkable phenomenon in which a fluid loses its viscosity, or internal resistance to flowing. A superfluid can move through extremely narrow passages without experiencing the usual friction. Imagine placing an oar into such a liquid. You would encounter almost no resistance until the oar reached a certain speed. Beyond that threshold, tiny swirling structures known as quantum vortices would suddenly form.During the 1960s, physici

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Chip-based lasers cool trapped ions 3x faster for quantum computing - Interesting Engineeringquantum-computing

Chip-based lasers cool trapped ions 3x faster for quantum computing - Interesting Engineering

Optical fibers feed an integrated ion-trap chip (gold), delivering laser light to cool trapped ions.Cornell UniversityKey TakeawaysCornell researchers cooled a trapped calcium ion close to its quantum ground state in 150 microseconds, versus approximately 500 microseconds with a conventional setup.The chip-based technique simultaneously addressed three motional modes across an approximately 5 MHz frequency range.The experiment demonstrates how integrated photonics could improve cooling operations in larger trapped-ion quantum computers. Researchers at Cornell University have developed a chip-based laser-cooling technique that brings trapped ions close to their quantum ground state in just 150 microseconds, potentially reducing a major time bottleneck in quantum computing. The method is around three times faster than a conventional laser arrangement, which takes approximately 500 microseconds. It also cools three different modes of an ion’s motion simultaneously, rather than requiring separate cooling operations for each mode. The findings demonstrate how integrated photonics could improve the control of trapped ions as researchers work to build larger quantum computers. The study was led by Karan Mehta, an assistant professor of electrical and computer engineering at Cornell’s Duffield College of Engineering. Cooling slows quantum operations Trapped-ion quantum computers use electrically charged atoms held in electromagnetic fields to store and process quantum information. Their precision makes them promising candidates for quantum computing, but unwanted atomic motion can interfere with operations. Researchers must repeatedly use lasers to remove this motion and cool ions close to their lowest possible energy state. These cooling procedures can take up a substantial portion of a quantum computer’s operating time. Mehta said cooling, particularly to the quantum ground state, has become a major bottleneck in leading trapped-ion systems.More from ScienceSee AllScience

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Qunova Computing Achieves Chemical Accuracy at 80 Qubits on Iron-Sulfur Cluster Using IBM Quantum System Two and Fugakuquantum-computing

Qunova Computing Achieves Chemical Accuracy at 80 Qubits on Iron-Sulfur Cluster Using IBM Quantum System Two and Fugaku

Quantum–classical resource architecture. South Korean quantum algorithm developer Qunova Computing has demonstrated chemical accuracy in an 80-qubit electronic-structure simulation of the [4Fe-4S] iron-sulfur cubane cluster, executing a hybrid workflow across an IBM Quantum System Two processor and the Fugaku supercomputer at RIKEN. Running its Handover Iterative Variational Quantum Eigensolver (HI-VQE) algorithm, the team achieved an extrapolated ground-state energy within 1.6 milliHartree (1 kcal/mol) of classical density-matrix renormalization group (DMRG) benchmarks while cutting classical supercomputing costs by roughly 300× compared to prior quantum diagonalization runs. Qunova HI-VQE 80-Qubit [4Fe-4S] Simulation BenchmarksBenchmark DimensionMeasured Result / SpecificationComparison & SignificanceTarget System[4Fe-4S] iron-sulfur cubane cluster (80 qubits)Catalytic core in nitrogenase and hydrogenase enzymes; intractable for exact classical solvers.Extrapolated Energy−327.2482 ± 0.0007 HartreeMatches classical DMRG reference (−327.2471 Ha) within chemical accuracy (1.6 mHa).Classical HPC Efficiency2,518 Fugaku node-hours (reached −326.9821 Ha by iteration 2)Represents an ~300× reduction in classical compute cost versus prior SQD benchmarks.Compute InfrastructureIBM Quantum System Two & RIKEN Fugaku supercomputerSupported by Japan’s NEDO Post-5G quantum-HPC hybrid platform initiative. Transition-metal complexes such as iron-sulfur clusters govern critical biological and industrial processes, including nitrogen fixation and clean hydrogen synthesis, yet their strongly correlated electrons challenge both standard density functional theory and early quantum algorithms. While prior quantum-centric supercomputing experiments demonstrated subspace sampling on smaller active spaces, reaching quantitative chemical accuracy at 80 qubits has remained an open milestone. HI-VQE reduces the computational complexity of the electronic structure problem by efficiently

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Cleveland Clinic, RIKEN, and IBM Cross 12,000-Atom Barrier in Heterogeneous Quantum-Classical Drug Binding Simulationsquantum-computing

Cleveland Clinic, RIKEN, and IBM Cross 12,000-Atom Barrier in Heterogeneous Quantum-Classical Drug Binding Simulations

Systems investigated (A), overarching algorithmic scheme adopted, (B), representation of a fragment (C), and details of the lower scaling fragment construction (D) introduced in this work. We performed workflow automation on computational tasks in panel B (red dashed frame). A research collaboration spanning the Cleveland Clinic Foundation, the RIKEN Center for Computational Science, and IBM Quantum has executed the largest heterogeneous quantum-classical (HQC) electronic-structure calculation to date, simulating full solvated protein–ligand complexes exceeding 12,000 atoms. Detailed in a technical preprint on arXiv, the team paired two 156-qubit IBM Heron r2 processors with three leadership supercomputers to collect 3.0 billion measurement shots across 21,006 circuits, achieving a greater than 40× increase in molecular size and up to a 210× improvement in fragment energy accuracy over previous quantum-embedding benchmarks. 12,635-Atom HQC Biomolecular Simulation Campaign ProfileSystem DimensionExperimental SpecificationTechnical & Computational BenchmarkTarget BiomoleculesTrypsin–benzamidine (12,635 atoms) & T4-lysozyme–n-butyl-benzene (11,608 atoms)Up to 31,795 molecular orbitals in solvated environments; >40× larger than prior 303-atom Trp-cage benchmark.Quantum HardwareTwo 156-qubit IBM Heron r2 QPUs (ibm_cleveland & ibm_kobe)21,006 circuits executed using up to 94 active qubits over 239+ QPU hours, gathering 3.0 × 109 samples.Classical HPC SystemsFugaku (152,064 nodes), Miyabi-G (1,104 GH200 nodes), & ROQUO (126 GB200 nodes)Distributed GPU-accelerated Selected-Basis Diagonalization (SBD-G) reaching 72.5% strong-scaling parallel efficiency.Fragment Solver AccuracyTrimSQD & O(1)-scaling Embedded Wavefunction (EWF)0.10 mHa deviation vs. DMRG on trypsin’s largest 45-orbital fragment (210× accuracy gain over ExtSQD).Biochemical DeliverableFirst HQC protein–ligand binding energy predictionMixed-basis (3-21G / STO-3G) workflow resolved T4-lysozy

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LTM Becomes First Global Systems Integrator to Launch Function in IBM Quantum Functions Catalog with AI-QEM
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LTM Becomes First Global Systems Integrator to Launch Function in IBM Quantum Functions Catalog with AI-QEM

Global technology services firm LTM (a Larsen & Toubro Group company) has deployed its proprietary AI-QEM (AI-powered Quantum Error Mitigation) software in the IBM Quantum Functions Catalog, becoming the first global systems integrator to publish a circuit function on the platform. Available to IBM Quantum Premium, Flex, and On-Prem Plan users via the ltm/ai-qem interface, the function integrates into Qiskit Estimator workflows to suppress hardware noise through classical deep-learning inference. LTM AI-QEM Circuit Function Specifications & BenchmarksSystem DimensionTechnical SpecificationMeasured Benchmark / Platform ImpactModel ArchitectureGraph Convolutional Network (GCN)-TransformerPredicts per-Pauli correction factors (ζi) from circuit structure, backend topology, and qubit calibrations.Hardware OverheadZero additional QPU runtime, circuit repetitions, or ancilla qubitsMatches native IBM EstimatorV2 QPU times (12 s commuting / 23 s non-commuting on ibm_kingston).Hardware BenchmarksUp to 100 qubits on IBM Heron r2 processorsAchieved 95.06% accuracy on 100-qubit Clifford circuits (depth 45) and 90.29% on 96-qubit 2D Ising runs.Current Scope & LimitsIBM Heron processor family; applied when |⟨O⟩noisy| ≥ 0.1Targets non-trivial expectation values while avoiding noise amplification near zero. Physical quantum processors remain constrained by gate errors and environmental decoherence that bias expectation values as circuit depth and qubit counts increase. Conventional error-mitigation protocols such as zero-noise extrapolation (ZNE) and probabilistic error cancellation (PEC) compensate for noise by executing ensembles of modified circuits, introducing exponential sampling overheads that inflate quantum runtime costs. AI-QEM workflow. LTM’s circuit-agnostic approach bypasses additional quantum sampling by decomposing observables into Pauli operators and applying trained classical neural networks to rescale each noisy expectation value (⟨Pi⟩mitigated = ⟨Pi⟩no

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