Quantum News
Sign InSign Up
Home/Quantum Technology/Quantum Machine Learning: QML Algorithms & Quantum AI Applications

Quantum Machine Learning: QML Algorithms & Quantum AI Applications

Quantum machine learning news: QML algorithms, quantum AI, quantum neural networks. Hybrid quantum-classical ML & quantum advantage research.

3,693 Articles
Updated Daily

Quantum machine learning (QML) explores intersections between quantum computing and artificial intelligence, investigating whether quantum algorithms can accelerate data analysis, pattern recognition, and model training beyond classical capabilities.

Theoretical foundations include quantum advantages for linear algebra subroutines central to machine learning—matrix inversion, principal component analysis, and vector inner products. The HHL algorithm promises exponential speedup for specific sparse, well-conditioned systems.

India's Quantum Machine Learning Landscape

India's National Quantum Mission supports quantum machine learning research through its Quantum Computing Thematic Hub at IISc Bengaluru. The Indian Institute of Science offers a Certificate Programme in Quantum Computing and Artificial Intelligence through its Centre for Continuing Education, providing comprehensive training in quantum AI applications with hands-on coding using Qiskit and PennyLane.

Tata Consultancy Services (TCS) develops quantum machine learning algorithms for enterprise applications. Infosys explores quantum AI through its Quantum Living Labs. IIT Delhi offers certification programs in quantum computing and machine learning in collaboration with industry partners.

The NQM targets developing quantum algorithms for optimization, simulation, and machine learning, with human resource development including training programs for quantum professionals.

Current NISQ-era QML relies on hybrid quantum-classical approaches including variational quantum algorithms, quantum neural networks, and quantum kernel methods. Challenges include "barren plateaus" in optimization landscapes limiting trainability, and limited qubit counts restricting model complexity.

Scientists Propose More Realistic Benchmarks For Quantum Algorithms - The Quantum Insiderquantum-computing

Scientists Propose More Realistic Benchmarks For Quantum Algorithms - The Quantum Insider

Scientists Propose More Realistic Benchmarks For Quantum Algorithms The Quantum Insider

Google News – Quantum ComputingLoading...0
A Quantum Optimization Framework for Data-Assimilation-Augmented Parameter Estimationquantum-computing

A Quantum Optimization Framework for Data-Assimilation-Augmented Parameter Estimation

--> Quantum Physics arXiv:2608.13614 (quant-ph) [Submitted on 12 Aug 2026] Title:A Quantum Optimization Framework for Data-Assimilation-Augmented Parameter Estimation Authors:Muhammad Jalil Ahmad, Mohammadhossein Mohammadisiahroudi, Animikh Biswas, Kathleen Hoffman View a PDF of the paper titled A Quantum Optimization Framework for Data-Assimilation-Augmented Parameter Estimation, by Muhammad Jalil Ahmad and 3 other authors View PDF HTML (experimental) Abstract:Parameter estimation is a fundamental challenge in the calibration of ordinary differential equation (ODE) models, where repeated numerical integration can lead to high computational cost. In this work, we investigate whether quantum algorithms can be leveraged to assist parameter estimation in nonlinear dynamical systems. We develop a hybrid classical-quantum framework that reformulates a data-assimilation-augmented parameter estimation problem as a combinatorial optimization task. Model dynamics and data assimilation are enforced entirely on the classical side, while the resulting parameter estimation cost functional is discretized and approximated by a quadratic unconstrained binary optimization (QUBO) surrogate. This surrogate is mapped to an Ising Hamiltonian, and quantum optimizers are used to search for low-energy configurations corresponding to candidate parameter estimates. We apply the framework to SIS and SIR epidemic models, the chaotic Lorenz-63 system, and a high-dimensional two-layer Lorenz-96 system. In this setting, the method is used to recover classical system parameters from partial state observations across steady-state, chaotic, and high-dimensional multiscale dynamical systems. Numerical experiments with synthetic data show that the proposed approach accurately recovers parameters while requiring data-assimilation solves only on a prescribed coarse grid. The framework avoids quantum state tomography, illustrating a viable pathway for integrating quantum optimization into data-driven par

arXiv Quantum PhysicsLoading...0
Efficient Hamiltonian Truncation: Fast Matrix Construction and Quantum Krylov Diagonalizationquantum-computing

Efficient Hamiltonian Truncation: Fast Matrix Construction and Quantum Krylov Diagonalization

--> Quantum Physics arXiv:2608.13691 (quant-ph) [Submitted on 13 Aug 2026] Title:Efficient Hamiltonian Truncation: Fast Matrix Construction and Quantum Krylov Diagonalization Authors:Rachel Houtz, Marco Knipfer, Konstantin Matchev, Alexander Roman, Mia West View a PDF of the paper titled Efficient Hamiltonian Truncation: Fast Matrix Construction and Quantum Krylov Diagonalization, by Rachel Houtz and 4 other authors View PDF HTML (experimental) Abstract:Hamiltonian truncation offers a nonperturbative route to quantum field theory, yet its accuracy is limited by the rapid expansion of the truncated Hilbert space, which drives up computational cost. We tackle this bottleneck with a hybrid strategy that pairs classical and quantum algorithms: 1) we develop an efficient basis-generation scheme built on integer partitions; 2) we speed up the construction of the sparse Hamiltonian matrix using symmetry-aware algorithms; and 3) we explore quantum Krylov diagonalization as a route to the low-lying spectrum. Benchmarking against the free massive scalar and $\phi^4$ theories in two spacetime dimensions, we achieve substantial gains in the computational efficiency of Hamiltonian truncation and chart a path toward future quantum implementations. Comments: Subjects: Quantum Physics (quant-ph); High Energy Physics - Lattice (hep-lat); High Energy Physics - Phenomenology (hep-ph); High Energy Physics - Theory (hep-th) Report number: KA-TP-19-2026 Cite as: arXiv:2608.13691 [quant-ph]   (or arXiv:2608.13691v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2608.13691 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Mia West [view email] [v1] Thu, 13 Aug 2026 18:37:19 UTC (5,055 KB) Full-text links: Access Paper: View a PDF of the paper titled Efficient Hamiltonian Truncation: Fast Matrix Construction and Quantum Krylov Diagonalization, by Rachel Houtz and 4 other authorsView PDFHTML (experimental)TeX Sourc

arXiv Quantum PhysicsLoading...0
Resource-efficient quantum eigenvalue transform with commutator scalingquantum-computing

Resource-efficient quantum eigenvalue transform with commutator scaling

--> Quantum Physics arXiv:2608.13862 (quant-ph) [Submitted on 14 Aug 2026] Title:Resource-efficient quantum eigenvalue transform with commutator scaling Authors:Arul Rhik Mazumder, James D. Watson, Samson Wang View a PDF of the paper titled Resource-efficient quantum eigenvalue transform with commutator scaling, by Arul Rhik Mazumder and 2 other authors View PDF Abstract:We develop quantum algorithms for estimating properties of general matrix functions of Hermitian matrices, with applications to phase estimation, Green's function evaluation, and estimating measurement distributions of time-evolved states. The resulting methods exhibit commutator scaling in matrix parameters similar to that usually found for product formulae, lower circuit depth in other parameters, and require only a single ancillary qubit. Our central primitive consists of classically postprocessing randomly chosen product formulae circuits, which mathematically corresponds to an approximation of a Richardson extrapolation. Within our framework, we introduce a protocol for approximating the measurement distributions of quantum states, extending beyond standard observable estimation. We also provide tightened gate complexity bounds for practically relevant systems, including those with k-local interactions, long-tailed matrix ensembles, and conserved quantities. Finally, numerical experiments confirm that our method can achieve significantly shallower circuit depths than standard product formulae in certain parameter regimes, and highlight the potential of their heuristic application. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2608.13862 [quant-ph]   (or arXiv:2608.13862v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2608.13862 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Samson Wang [view email] [v1] Fri, 14 Aug 2026 01:23:30 UTC (740 KB) Full-text links: Access Paper: View a PDF of the paper titled

arXiv Quantum PhysicsLoading...0
Tried implementing the NAND-tree algorithm in Qiskitquantum-computing

Tried implementing the NAND-tree algorithm in Qiskit

I've been working on a Qiskit implementation of the NAND-tree quantum algorithm and thought I'd share it here. Right now it works with small finite trees. I implemented the quantum walk, the input oracle, query counting, and a few different ways of simulating the Hamiltonian. I've mostly been testing it on 2, 4, and 8 leaves. It's not a scalable implementation yet. Some parts still use dense Hamiltonian matrices, and figuring out how to get past that is one of the things I'm looking into. Here's the repo: https://github.com/mncrftfrcnm/qiskit-nand-tree-implementation If anyone here has worked with NAND-tree algorithms or quantum walks, I'd really appreciate feedback, especially if you spot something wrong with how I've handled the oracle or query counting. submitted by /u/FollowingEvery4802 [link] [comments]

Reddit r/QuantumComputing (RSS)Loading...0
Quantinuum to Partner with the Singapore Institute of Technology to Help Develop Singapore’s Future Quantum Workforce.quantum-computing

Quantinuum to Partner with the Singapore Institute of Technology to Help Develop Singapore’s Future Quantum Workforce.

Quantinuum to Partner with the Singapore Institute of Technology to Help Develop Singapore’s Future Quantum Workforce. Quantinuum has signed a Memorandum of Understanding (MoU) with the Singapore Institute of Technology (SIT) to train and expand Singapore’s quantum workforce. Building on Quantinuum’s existing R&D footprint and the planned deployment of its Helios quantum processor in Singapore, the collaboration aims to prepare an industry-ready workforce across engineering, systems development, and applied technologies. Key Initiatives of the Partnership Practical Curriculum: Joint development of hands-on training modules tailored for both undergraduate students and working professionals. Tool Access: Direct access to Quantinuum’s suite of quantum software, development tools, and simulators for educational use. Community Engagement: Hosting regular workshops, seminars, and campus events to build local interest and technical literacy in quantum computing. This strategic alignment addresses the growing commercial demand for skilled talent, ensuring local developers and engineers gain direct exposure to state-of-the-art quantum hardware and software environments. Additional information can be found in a LinkedIn post here. August 15, 2026 dougfinke2026-08-15T20:43:39-07:00 Leave A Comment Cancel replyComment Type in the text displayed above Δ This site uses Akismet to reduce spam. Learn how your comment data is processed.

Quantum Computing ReportLoading...0
Lattice-Based Cryptography Explainedquantum-computing

Lattice-Based Cryptography Explained

Lattice-based cryptography is the branch of modern encryption that hides its secrets inside a grid of points in high-dimensional space. It is the mathematics underneath the two headline standards the United States published in 2024, and it is the reason most of the internet’s future key exchanges will look nothing like the ones running today. This page is about the machinery rather than the policy. If you want the wider picture of the migration, our guide to post-quantum cryptography covers it, and the vendor landscape is mapped separately. What follows is the geometry that lattice-based cryptography is built from, the hard problems, the protocols standing on them, and an honest account of why anyone believes a quantum computer cannot break them. Core assumption Finding short or close vectors in a high-dimensional lattice is hard, even with a quantum computer Workhorse problem Learning With Errors, introduced by Oded Regev in 2005 Standardised as ML-KEM in FIPS 203 and ML-DSA in FIPS 204, both published 13 August 2024 Descended from CRYSTALS-Kyber and CRYSTALS-Dilithium, submitted to the NIST process in November 2017 Status of the security claim No known efficient quantum attack, which is not the same thing as a proof Practical cost Keys and signatures measured in kilobytes rather than tens of bytes Key takeaways A lattice is a repeating grid of points, and the hard part is finding the nearest one. In two dimensions a child can do it by eye, and in the several hundred dimensions lattice-based cryptography uses, nobody knows how. The same lattice can be described by an easy basis or an impossible one. That asymmetry between a short near-orthogonal description and a long skewed one is the trapdoor the whole field is built on. Learning With Errors is linear algebra with the answers slightly wrong. Remove the errors and the system falls to schoolbook elimination, add them back and no efficient method is known. Quantum resistance here is an absence of attack, not a theor

Quantum ZeitgeistLoading...0
Global Consortium Launches Quantum Optimization Benchmarking Library (QOBLIB) to Track Path to Quantum Advantagequantum-computing

Global Consortium Launches Quantum Optimization Benchmarking Library (QOBLIB) to Track Path to Quantum Advantage

An international research consortium led by IBM Quantum, Zuse Institute Berlin (ZIB), Technische Universität Berlin, and Purdue University—alongside global academic and industrial partners—has introduced the Quantum Optimization Benchmarking Library (QOBLIB). Published in Nature Computational Science (“The Quantum Optimization Benchmarking Library“), the open-source initiative establishes a standardized, model-independent benchmarking framework to evaluate quantum, classical, and hybrid algorithms across ten NP-hard combinatorial optimization problem classes. [ QOBLIB Model-Independent Benchmarking Stack ] │ ┌────────────────────────────────────────┼────────────────────────────────────────┐ ▼ ▼ ▼ The "Intractable Decathlon" Open-Source Repository & Web Portal Cross-Paradigm Evaluation • 10 Hard Combinatorial Classes. • 1,260+ Curated Problem Instances. • Head-to-Head Solver Tracking. • 20 to 3,000,000+ Variables. • 2,600+ Benchmark Submissions. • Classical MIP/QUBO Baselines. • MIP, ILP, MIQP, & QUBO Formulations. • Live Best-Known Solution Tracking. • Near-Term Quantum Hardware Runs. Structuring the “Intractable Decathlon” QOBLIB addresses a critical gap in quantum optimization: while heuristic algorithms like the Quantum Approximate Optimization Algorithm (QAOA) or quantum annealing lack theoretical performance guarantees, empirical advantage claims require rigorous comparisons against state-of-the-art classical solvers. The library curates 1,264 specific instances spanning ten problem classes that become computationally hard for classical solvers at scales ranging from tens to tens of thousands of decision variables: Market Split (Multidimensional Subset Sum): Hard binary integer linear programming (ILP) instances with dense constraint matrices (20–140 variables). Low-Autocorrelation Binary Sequences (LABS): A canonical spin-glass benchmark with applications in radar and signal processing (2–100 variables). Minimum Birkhoff Decomposition: Doubly stochasti

Quantum Computing ReportLoading...0
Hirata & Tsukada Build Language for Quantum-Controlled Channelsquantum-computing

Hirata & Tsukada Build Language for Quantum-Controlled Channels

A new theoretical study proposes a quantum programming language capable of expressing one of quantum information science’s most powerful control mechanisms: the quantum SWITCH. Kengo Hirata of Kyoto University and Takeshi Tsukada of Chiba University have developed a programming framework that overcomes a fundamental obstacle in controlling quantum programs with qubits. By introducing a novel linear type system, the researchers show that quantum programs involving general quantum channels can be described in a mathematically consistent way while naturally supporting the quantum SWITCH. Quantum computers derive their power from the ability of quantum data to exist in superposition, allowing a qubit to represent multiple states simultaneously. This naturally raises a deeper question: if quantum data can exist in superposition, can entire quantum programs also be placed into superposition? The quantum SWITCH, which allows the order of two quantum operations to depend on a quantum control state, has emerged as one of the best-known examples of quantum-controlled computation and has attracted considerable attention in quantum information theory. A common method for controlling quantum programs is through controlled operations. In this approach, a control qubit determines whether an operation F is applied when the qubit is in the state |1⟩ or whether the identity operation is performed when the qubit is in the state |0⟩. While this construction works well for unitary operations, Hirata and Tsukada show that it is not well-defined for general quantum channels, which include measurements, noise, and other non-unitary processes that occur in realistic quantum systems. The researchers identify the source of this limitation as the way quantum conditional branching handles measurements. Specifically, the measurements performed in the then and else branches of a conditional statement may not correspond to one another, preventing the overall program from representing a valid quant

Quantum ZeitgeistLoading...0
Canada, G7 & Nordic nations seek quantum research projectsquantum-computing

Canada, G7 & Nordic nations seek quantum research projects

Researchers face a deadline of September 24, 2026, at 1:00am UK time to submit proposals for international quantum research projects. Canada, G7 nations, and Nordic countries are collaborating to fund university-based research, seeking to build partnerships and advance quantum innovation, the company says. This funding opportunity supports non-confidential work, even with potential dual-use applications, and encourages exploration of how quantum technologies can enhance privacy and security. Projects must focus on areas like quantum algorithms, encryption, and communications, or integrate these with natural sciences and engineering. G7-Nordic Collaboration Funds Quantum Science and Technologies The funding call, issued jointly by Canada, G7 nations, and Nordic countries, specifically targets university-based projects focused on advancing quantum algorithms, encryption, and communications technologies. Proposals integrating these areas with natural sciences and engineering are also welcomed, provided they address at least one core quantum theme. The collaborative effort explicitly supports research involving potential dual-use applications while maintaining a requirement for non-confidential, unclassified work, signaling an interest in technologies with both civilian and national security implications. Funding aims to improve national resilience and security through quantum technologies, as well as enable new scientific discoveries and industrial applications. The program utilizes a single-stage proposal model, streamlining the application process for international teams seeking to build partnerships in quantum science. Source: https://www.euroquic.org/canada-g7-nordic-call-for-proposals-on-quantum-technologies/ Stay currentSee today’s quantum computing news on Quantum Zeitgeist for the latest breakthroughs in qubits, hardware, algorithms, and industry deals. Tags: Ivy Delaney Ivy Delaney has been working with neural networks and machine learning since the mid-nineti

Quantum ZeitgeistLoading...0
Fraunhofer IAF review pushes for more robust quantum algorithm benchmarksquantum-computing

Fraunhofer IAF review pushes for more robust quantum algorithm benchmarks

The Fraunhofer Institute for Applied Solid State Physics IAF has published two papers challenging how quantum advantage is measured, suggesting current methods lack sufficient rigor. Researchers are pushing for more realistic benchmarks in quantum chemistry by questioning the common practice of modeling molecules as perfectly isolated systems. These idealized approaches, they argue, don’t reflect natural conditions where molecules constantly interact with their environment; a shift is needed to account for “open dynamics that are ubiquitous in nature.” “The exciting question is not just whether quantum computers can outperform classical computers, but when, why, and under what conditions,” says Dr. Florentin Reiter, head of the Quantum Systems business unit at Fraunhofer IAF. Open System Dynamics for Robust Quantum Chemistry This work challenges the prevailing practice of modeling molecules as closed systems perfectly isolated from their environment, asserting that real-world interactions are critical to accurately assessing potential quantum advantages. The review, “Beyond Unitary Quantum Simulation: Open-System Approaches for Quantum Chemistry Toward Quantum Advantage,” proposes a shift toward incorporating these interactions into quantum simulations, acknowledging the constant energy release and relaxation inherent in natural processes. This focus on open dynamics stems from the understanding that dissipative processes aren’t simply disturbances, but potentially valuable resources for quantum algorithms. Researchers suggest controlled dissipation can aid in preparing, stabilizing, and sampling quantum states relevant to chemistry, solid-state physics, and materials science. This contrasts with traditional approaches that primarily focus on Hamiltonian dynamics of closed systems, a simplification that may not translate to practical applications. A second publication examines the Quantum Approximate Optimization Algorithm (QAOA) and its ability to maintain efficien

Quantum ZeitgeistLoading...0
BTQ Technologies moves four quantum business lines toward commercial usequantum-computing

BTQ Technologies moves four quantum business lines toward commercial use

BTQ Technologies completed the acquisition of QPerfect during the second quarter of 2026, signaling a shift toward commercializing its quantum technologies platform. The company reports progress across four core business lines, including advancements in quantum software and a step toward quantum-resistant cryptocurrency infrastructure. BTQ believes the transition to quantum computing requires more than increased capability; it also demands secure systems to connect classical and quantum infrastructure, and is increasingly focused on translating technical capabilities into recurring revenue opportunities. QPerfect Acquisition Expands BTQ’s Quantum Software Capabilities The purchase adds critical software and technologies for quantum emulation, digital twins, validation, and logical quantum computing to BTQ’s existing capabilities, positioning the company to support clients designing and deploying applications on future quantum hardware. This expansion signals a shift from technological validation toward commercial execution for BTQ. The acquisition directly supports BTQ’s Quantum Accelerated Networks layer, a critical component of its overarching strategy. This strategy aims to establish trust at the silicon level, extend it across digital and blockchain networks, and integrate that trust into quantum-accelerated infrastructure. “Q2 represented an important transition for BTQ as we continued moving from technology validation toward commercial execution,” said Olivier Roussy Newton, Chief Executive Officer of BTQ Technologies. “We completed the acquisition of QPerfect, expanded customer and institutional engagements across multiple markets, advanced QSSN toward production deployment, and brought Bitcoin Quantum infrastructure to mainnet readiness.” Beyond MIMIQ, QPerfect is also developing a Digital Twin product and a Quantum Logic Unit. The company’s broader platform remains aligned with emerging cryptographic standards and regulatory initiatives globally, and its Qu

Quantum ZeitgeistLoading...0
Quantinuum: Now Is Not The Right Time To Buyquantum-computing

Quantinuum: Now Is Not The Right Time To Buy

Tangerine Tan Capital3.61K FollowersFollowSummaryQuantinuum Inc. receives a 'sell' rating due to extreme valuation despite being a high-quality quantum computing company.QNT's revenue growth is lagging peers, with 2026 guidance of $28–$32 million and significant cash burn estimated at $250 to $300 million annually.The company is well-capitalized post-IPO ($2.1 billion cash), enabling continued R&D for Sol (2027) and Apollo (2029) quantum products.Strategic partnerships and ecosystem development position QNT for long-term potential, but current multiples are unsustainably high relative to future revenue scenarios. NiPlot/iStock via Getty Images The Quantinuum Investment thesis Quantinuum Inc. (QNT) is one of the most promising quantum computing companies and is now a public company. Unfortunately, even good companies can have prices at which they are not attractive investments. And this high price tellsThis article was written byTangerine Tan Capital3.61K FollowersFollowMy primary area of concentration will be on identifying companies of exceptional caliber, with a proven ability to reinvest capital for impressive returns. The ideal scenario is for these companies to demonstrate a long-term capability of capital compounding, with a high enough compound annual growth rate to potentially deliver tenfold returns or even greater.My approach is to maintain a long-term perspective on these companies, as I believe this will generate higher returns compared to the market index, in a rapidly evolving investment landscape where short-term holdings are becoming increasingly prevalent.I primarily adopt a conservative investment strategy, but occasionally I may pursue opportunities with a favorable risk-reward ratio where the potential upside is substantial and downside is limited. These ventures are carefully considered and allocated a proportional amount within my portfolio to maintain overall stability.Bachelor's degree in finance and accounting All ideas and articles are

Seeking AlphaLoading...0
JIJ organizes QuBench 2026 at IEEE Quantum Week in Torontoquantum-computing

JIJ organizes QuBench 2026 at IEEE Quantum Week in Toronto

On September 14, 2026, researchers Wei-Hao Huang, Keisuke Sato, Hiromichi Matsuyama, Kuan-Cheng Chen, and Rosse Grassie will introduce Qamomile, a new tool designed to bridge the gap between estimating quantum resource needs and executing actual programs. JIJ developed Qamomile to support a single quantum program description for both stages, utilizing a typed programming model and integration with the OMMX optimization format. The team reports this unified workflow is increasingly crucial as quantum algorithms become more complex, offering a path from algorithm design to executable implementations. JIJ is also the main organizer of QuBench 2026, a workshop focused on quantum benchmarking and resource estimation, held in conjunction with IEEE Quantum Week in Toronto. Qamomile Tutorial: Symbolic Estimation and Concrete Execution Workflow Recognizing the increasing complexity of quantum algorithms, JIJ developed Qamomile to address the need for practical methods to scale resource requirements alongside problem size while maintaining a clear path to executable code. The “Introduction to Qamomile: Estimating Symbolically and Executing Concretely” tutorial will instruct participants on writing quantum programs using the tool, estimating resource needs as functions of problem size, and transpiling programs for execution on supported software backends. Attendees will also explore quantum optimization workflows leveraging OMMX. According to the tutorial description available online, participants will learn how to write quantum programs in Qamomile through a hands-on approach. JIJ intends to continue bridging research advances with practical tools for testing and application. Source: https://www.j-ij.com/en/news/20260814 Stay currentSee today’s quantum computing news on Quantum Zeitgeist for the latest breakthroughs in qubits, hardware, algorithms, and industry deals. Tags: The Neuron With a keen intuition for emerging technologies, The Neuron brings over 5 years of deep expe

Quantum ZeitgeistLoading...0
Florida State University Launches Florida’s First Graduate Certificate in Quantum Information Science & Technologyquantum-computing

Florida State University Launches Florida’s First Graduate Certificate in Quantum Information Science & Technology

Florida State University Launches Florida’s First Graduate Certificate in Quantum Information Science & Technology Florida State University (FSU) has announced the launch of Florida’s first formal graduate credential in quantum information science and engineering: the Graduate Certificate in Quantum Information Science & Technology (QIST). Administered by the FSU Quantum Initiative, the 14-credit-hour interdisciplinary program is accepting applications through October 1, 2026, for its inaugural Spring 2027 enrollment cohort. The program bridges departments across the FSU College of Arts and Sciences and the FAMU-FSU College of Engineering—including Physics, Chemistry & Biochemistry, Computer Science, Mathematics, Materials Science & Engineering, Electrical & Computer Engineering, and Mechanical & Aerospace Engineering—to train graduate students and industry professionals across quantum materials, low-temperature device packaging, and quantum algorithm design. [ FSU QIST Graduate Certificate Ecosystem ] │ ┌─────────────────────────────────┼─────────────────────────────────┐ ▼ ▼ ▼ Core Academic Curriculum Specialized Research Facilities Industry & Center Networks • Mandatory Quantum Computing. • National MagLab (High Fields). • Commercial Partnerships (IonQ). • 3 Advanced Technical Electives. • Interdisciplinary Research Bldg. • Hardware Integration (Qblox, Keysight). • QSE Research Seminars. • Cleanroom & Cryogenic Dilution. • Quantum Software Labs (Amazon). Program structure and institutional research assets include: Curriculum Requirements: A 14-credit-hour framework comprising a mandatory core course in Quantum Information and Computing (3 credits), three specialized STEM electives (9 credits), and two semesters of the Quantum Science & Engineering Seminar (2 credits). Research Infrastructure Access: Enrolled students gain direct access to the National High Magnetic Field Laboratory (MagLab) and the newly constructed Interdiscip

Quantum Computing ReportLoading...0
Quantinuum, NVIDIA, and Pfizer Validate Generative Quantum AI (GenQAI) Framework for Pharmaceutical R&Dquantum-computing

Quantinuum, NVIDIA, and Pfizer Validate Generative Quantum AI (GenQAI) Framework for Pharmaceutical R&D

Quantinuum, NVIDIA, and Pfizer Validate Generative Quantum AI (GenQAI) Framework for Pharmaceutical R&D Researchers from Quantinuum, NVIDIA, and Pfizer Inc. have validated a Generative Quantum AI (GenQAI) framework designed to automate and accelerate quantum circuit synthesis for pharmaceutical research and electronic structure modeling. In their paper, “Learning to Prepare Molecular Ground States with Transformer Models“, the hybrid architecture combines classical High-Performance Computing (HPC), generative transformer models, and quantum processing units (QPUs) to compute ground-state preparation circuits for complex active pharmaceutical ingredients (APIs). The multi-institutional team introduced ADAPT-GQE, a generative AI model trained on quantum chemistry datasets generated via GPU-accelerated classical supercomputing. The model predicts complete ground-state quantum circuits for imipramine—a tricyclic antidepressant used as an industry benchmark for forced degradation and shelf-life stability studies—executing the resulting circuits on Quantinuum’s 98-qubit Helios-1 trapped-ion hardware. [ GenQAI / ADAPT-GQE Quantum Circuit Synthesis Pipeline ] │ ┌────────────────────────────────────────┼────────────────────────────────────────┐ ▼ ▼ ▼ HPC Data Generation (NVIDIA CUDA-Q) Generative AI Circuit Synthesis QPU Execution & Validation • GPU-Accelerated ADAPT-VQE Circuits. • Fine-Tuned NVIDIA Nemotron Models. • Quantinuum Helios-1 Processor. • OpenMM & MACE-OFF MD Conformers. • Gemma 3 / Nemotron-Nano Transformer. • InQuanto Chemistry Platform. • 12 to 16 Active-Space Qubit Maps. • 3-4 Orders of Magnitude Speedup. • Validated Imipramine Ground State. The experiment resolves a fundamental computational bottleneck in near-term variational quantum algorithms (VQEs): Bypassing Iterative Gradient Calculations: Standard adaptive algorithms like ADAPT-VQE require evaluating thousands of operator gradients and re-optimizing parameter landscapes at every step, ren

Quantum Computing ReportLoading...0
EPFL Integrates Quantinuum Trapped-Ion Cloud Access into SCITAS HPC Platformquantum-computing

EPFL Integrates Quantinuum Trapped-Ion Cloud Access into SCITAS HPC Platform

EPFL Integrates Quantinuum Trapped-Ion Cloud Access into SCITAS HPC Platform The EPFL Center for Quantum Science and Engineering (QSE), in collaboration with EPFL’s SCITAS high-performance computing (HPC) platform, has partnered with Quantinuum to provide cloud-based access to Quantinuum’s trapped-ion quantum computers. The agreement establishes EPFL as the first Swiss academic institution to natively integrate commercial QPU access directly into its institutional supercomputing infrastructure. [ EPFL SCITAS Hybrid Quantum-HPC Platform ] │ ┌─────────────────────────────────┴─────────────────────────────────┐ ▼ ▼ SCITAS HPC Infrastructure Interface Quantinuum Cloud QPU Hardware • Unified Academic Queue & Auth Workflow. • High-Fidelity Trapped-Ion Processors. • Integrated Digital Quantum Simulations. • Low Decoherence & High Gate Fidelity. • Native Integration for EPFL Researchers. • Digital Quantum & Many-Body Simulations. The partnership allows EPFL research groups to execute quantum algorithms, digital quantum simulations, and many-body physics calculations without navigating separate external management workflows: Research Applications: EPFL groups led by Prof. Giuseppe Carleo (Computational Quantum Science Laboratory) and Prof. Zoë Holmes (Quantum Information and Computing Group) are deploying the hardware to explore complex many-body quantum simulations and evaluate practical quantum algorithmic limits. SCITAS Integration: Developed alongside SCITAS Operational Director Gilles Fourestey, the integration allows researchers to submit hybrid classical-quantum jobs directly through familiar HPC batch job interfaces. Workforce & Academic Curriculum: Led by QSE Academic Director Prof. Vincenzo Savona and Master’s Program Co-Director Prof. Nicolas Macris, EPFL plans to extend hardware access to students enrolled in its Master’s program in Quantum Science and Engineering for hands-on circuit design and QPU execution. Review the official announcement on E

Quantum Computing ReportLoading...0
SPLIT-Q: A Scalable Sequential Quantum Computing Framework for Coherent Controlled Islandingquantum-computing

SPLIT-Q: A Scalable Sequential Quantum Computing Framework for Coherent Controlled Islanding

--> Quantum Physics arXiv:2608.12711 (quant-ph) [Submitted on 13 Aug 2026] Title:SPLIT-Q: A Scalable Sequential Quantum Computing Framework for Coherent Controlled Islanding Authors:Yuqi Jiang, Zhiding Liang, Qiang Guan, Yan Li, Ganesh Kumar Venayagamoorthy View a PDF of the paper titled SPLIT-Q: A Scalable Sequential Quantum Computing Framework for Coherent Controlled Islanding, by Yuqi Jiang and 4 other authors View PDF HTML (experimental) Abstract:Growing integration of distributed energy resources increases power-system variability and uncertainty. During disturbances, these effects can intensify generation-load imbalances and cascading failures. Controlled islanding limits their propagation by partitioning a compromised grid into connected, electrically sustainable islands. However, classical methods face rapidly growing computational costs as network size and island count increase. Quantum optimization offers an alternative for exploring this combinatorial partition space. Yet monolithic quantum formulations encode all assignment decisions in one circuit, causing qubit demand and circuit complexity to scale with network size. In this study, a qubit-bounded sequential distributed quantum approximate optimization algorithm (QAOA) framework is proposed to tackle coherent controlled islanding under limited quantum resources. It formulates the optimization as boundary-conditioned regional quadratic unconstrained binary optimization (QUBO) subproblems that are solved sequentially within a fixed qubit budget. Thus, circuit width remains independent of network size, with aggregate quantum workload scaling linearly on bounded-degree networks. Evaluation covers eleven IEEE systems from 9 to 300 buses using IBM quantum computing resources, with Gurobi and monolithic QAOA as references. Across all systems, the framework recovers feasible Gurobi-optimal partitions under noise, confirming the resilience of its solution quality. The results further show that the proposed met

arXiv Quantum PhysicsLoading...0
Parity Floors in Quantum Denoisers: A Closed-Form Benchmark for Fixed-Map Denoising Networksquantum-computing

Parity Floors in Quantum Denoisers: A Closed-Form Benchmark for Fixed-Map Denoising Networks

--> Quantum Physics arXiv:2608.12712 (quant-ph) [Submitted on 13 Aug 2026] Title:Parity Floors in Quantum Denoisers: A Closed-Form Benchmark for Fixed-Map Denoising Networks Authors:Jaeuk Kim View a PDF of the paper titled Parity Floors in Quantum Denoisers: A Closed-Form Benchmark for Fixed-Map Denoising Networks, by Jaeuk Kim View PDF HTML (experimental) Abstract:Fixed quantum feature maps are increasingly inserted into diffusion denoisers, but standard image benchmarks do not reveal which structural constraint limits them. We introduce CoupledPhaseTexture, a torus-diffusion benchmark with analytic heat-kernel noising that separates parity, within-sector approximation, and sample-complexity limitations. For the depth-1 RY+CNOT+Pauli-Z family we prove a containment-free parity floor: all reachable features are even functions of the encoded angles while the sine components of the Bayes denoiser are odd, so the excess risk splits exactly into an inaccessible odd part and a within-sector residual. The first term is an irreducible, noise-scale-resolved lower bound holding for every even feature class, with no containment, linearity, or closedness assumption on the feature class. The obstruction is a property of the noise-conditioned denoising target rather than static representability: the floor is re-derived at each noise scale because the target's parity content changes with noise. The measured excess is dominated by the parity proxy on two distinct priors. Higher-order Z readouts improve the even sector, but entanglement does not lower the floor and re-uploading does not reliably close it. Classical controls confirm the deficit is parity rather than quantumness: a cosine-only bank is floored similarly, while adding the sine sector matches the reference. Among tested constructions, odd readouts and a noise-coupled encoder do not match the sine-carrying classical bank. These results motivate nonclassical data access or feature classes without efficient classical surro

arXiv Quantum PhysicsLoading...0
Hybrid HPC-Quantum Simulations: DFT-Quantum Embedding for Molecular Systemsquantum-computing

Hybrid HPC-Quantum Simulations: DFT-Quantum Embedding for Molecular Systems

--> Quantum Physics arXiv:2608.12884 (quant-ph) [Submitted on 13 Aug 2026] Title:Hybrid HPC-Quantum Simulations: DFT-Quantum Embedding for Molecular Systems Authors:Namrata Manglani, Samrit Maity, Shashank Sharma, Tejjan Arora, Soham Phulare, Shreyas Kadam, Sanjay Wandhekar View a PDF of the paper titled Hybrid HPC-Quantum Simulations: DFT-Quantum Embedding for Molecular Systems, by Namrata Manglani and Samrit Maity and Shashank Sharma and Tejjan Arora and Soham Phulare and Shreyas Kadam and Sanjay Wandhekar View PDF HTML (experimental) Abstract:Scientific simulations demand methods combining scalability with predictive accuracy. Density Functional Theory (DFT) on High-Performance Computing (HPC) enables large-scale electronic-structure simulations but is limited by approximations affecting strongly correlated systems and band-gap predictions. Quantum computing offers a pathway to address this, though current Noisy Intermediate-Scale Quantum (NISQ) hardware remains constrained by qubit resources, noise, and execution cost. This work presents a hybrid DFT-Quantum Embedding (QDFT) framework integrating classical HPC-based DFT with a quantum electronic-structure solver. Large systems are partitioned to isolate a chemically relevant active space, treated via the Variational Quantum Eigensolver (VQE), while the remaining degrees of freedom are described by DFT. The framework incorporates active-space selection, embedded Hamiltonian construction, symmetry preservation, operator mapping, self-consistent density updating, and modular classical-quantum coupling. We focus on noiseless quantum simulation to systematically evaluate accuracy, convergence, active-space dependence, computational cost, and HPC scalability without hardware noise. Detailed profiling identifies computational bottlenecks and highlights limitations of CPU-based quantum simulation. A QPU runtime-estimation methodology is additionally developed to assess execution requirements on actual quantum hardware.

arXiv Quantum PhysicsLoading...0

Related Topics

Quantum Computing Drug DiscoveryQuantum Finance BankingQuantum Optimization LogisticsQuantum Software Development