Quantum Cloud Services: AWS Braket, Azure Quantum & IBM Quantum
Quantum cloud computing news: QCaaS platforms, AWS Braket, Azure Quantum, IBM Quantum Experience. Cloud quantum access & hybrid computing.
Quantum computing cloud services democratize access to quantum hardware, enabling researchers, enterprises, and developers to experiment with quantum processors without multi-million-dollar infrastructure investments.
Major global platforms include IBM Quantum with 20+ systems (5-1,000+ qubits); Amazon Braket providing hardware-agnostic access to IonQ, Rigetti, OQC, and D-Wave systems; and Microsoft Azure Quantum offering diverse hardware including IonQ, Quantinuum, and Rigetti.
India's Quantum Cloud Infrastructure
India's National Quantum Mission plans indigenous quantum cloud infrastructure development. The Foundation for QC Innovation at IISc Bengaluru will provide access to quantum computing resources as hardware matures. Until indigenous platforms are operational, the Department of Science and Technology facilitates cloud access to international quantum computers for Indian researchers.
The Andhra Pradesh Quantum Valley Tech Park, developed in partnership with IBM and TCS, will provide cloud access to an IBM Quantum System Two with 156-qubit Heron processor—the largest quantum computer in India. TCS will support development of algorithms and applications for Indian industry and academia through this facility.
The NQM targets making quantum computing resources accessible to startups, MSMEs, and researchers, with the quantum fabrication facilities at IISc Bengaluru and IIT Bombay providing prototyping and testing access.
SAS Unveils Quantum Lab on Viya Platform, Combining Emulated Workloads with a “Physics-First” Auditing Approach
SAS Unveils Quantum Lab on Viya Platform, Combining Emulated Workloads with a “Physics-First” Auditing Approach Enterprise analytics architecture pioneer SAS has introduced SAS Quantum Lab, a development and simulation environment embedded natively inside its cloud-native SAS Viya data platform. Announced at the SAS Innovate conference in Dallas, Texas, the platform treats quantum computing as a downstream step in a hybrid workflow, prioritizing heavy initial algorithmic verification and auto-tuning on classical infrastructure before committing code to physical quantum processors. The software rollout coincides with the publication of the company’s 2026 global industry survey evaluating over 500 technology executives, which revealed that uncertainty around practical, real-world business use cases has surpassed capital expense as the primary adoption barrier in the post-classical space. [ SAS Quantum Lab Ecosystem ] Integration Node ──► Native emulation layer inside the cloud-based SAS Viya architecture. Compute Core ──► Distributed parameter tuning powered by classical CAS workers. Validation Gains ──► Internal testing demonstrates a >100x speedup and 99% baseline development savings. Pragmatic Strategy ──► Algorithmic "classical-first" auditing to prevent excessive hardware fees. A Structural Shift in Enterprise Adoption Barriers The company’s annual analytics report indicates that while 2025 enterprise bottlenecks were defined by raw implementation costs (38%) and a baseline lack of basic comprehension (35%), 2026 industry leaders generally understand what quantum AI represents. Instead, they are proceeding with extreme caution, hesitant to allocate capital to expensive quantum hardware leases out of fear that the investments will not yield immediate, measurable problem-solving utility. To bridge this implementation gap, SAS outlines classical and quantum computing as a continuum. High-density optimization and machine learning workloads are segmented, allowing
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quantum-computingShanghai Quantum Sensing Intelligence Secures Tens of Millions of Yuan in Angel Round to Scale Photonic Sensing Infrastructure
Shanghai Quantum Sensing Intelligence Secures Tens of Millions of Yuan in Angel Round to Scale Photonic Sensing Infrastructure Deep-tech hardware developer Shanghai Quantum Sensing Intelligence Technology Co., Ltd. has completed an angel round of financing, securing tens of millions of yuan. The capitalization round was led by Futeng Capital (a specialized investment vehicle operating under the sovereign Shanghai State Investment banner), with co-investment from Liuhe Venture Capital. Established in September 2023 as an industrial spin-out incubated by members of the Quantum Sensing Research Institute at Shanghai Jiao Tong University (SJTU), the firm focuses on the commercialization of room-temperature quantum precision measurement devices. The capital injection accelerates small-batch trial production lines, team extensions, and the deployment of microfabrication modules across aerospace, military, and energy infrastructure markets. [ Quantum Sensing Intelligence Architecture ] Financial Injection ──► Tens of millions of yuan in Angel Funding led by Futeng Capital. Core Technology ──► Room-temperature Photonic Quantum Enhancement Module platforms. Operational Markets ──► Navigation-grade inertial gyroscopes and trace power grid gas monitoring. Strategic Roadmap ──► Integrating quantum precision sensing with Edge Quantum AI computing. Photonic Quantum Enhancement Mechanics The technological framework developed by the co-founding team addresses an engineering limitation in classical precision instruments: the high signal-to-noise ratio (SNR) degradation that occurs when trying to isolate ultra-weak physical indicators from background system noise. Rather than shifting to cryogenic control mechanisms or large vacuum enclosures that limit field transportability, the company employs a proprietary photonic quantum enhancement technology that operates continuously at room temperature. This approach integrates a quantum enhancement layer directly into existing classical op
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quantum-computingQuTech Launches Open-Architecture Tuna-17 Superconducting Processor on Quantum Inspire Cloud Platform
QuTech Launches Open-Architecture Tuna-17 Superconducting Processor on Quantum Inspire Cloud Platform Quantum research center QuTech—a joint collaboration between the Delft University of Technology (TU Delft) and the Netherlands Organisation for Applied Scientific Research (TNO)—has announced the deployment of its latest superconducting quantum computer, Tuna-17. Accessible globally through the Quantum Inspire public cloud platform, the processor provides researchers, engineers, and educators with open, un-capped access to live physical quantum hardware. The launch represents the third system release within a 12-month development cycle, succeeding the earlier Tuna-5 and Tuna-9 processors, and establishes a highly standardized operational baseline before the upcoming deployment of the larger 28-qubit variant (Tuna-28). [ Tuna-17 System Architecture ] QPU Modality ──► 17 superconducting qubits integrated with 24 tunable couplers. Value Chain Node ──► 100% European open-architecture consortium anchored in Delft. Software Interface ──► Direct open-source SDK compilation via Qiskit and PennyLane libraries. Cloud Access Model ──► Free public access via Quantum Inspire; up to 100,000 shots per batch. The Architecture of the Tuna-17 Processor The underlying hardware design, developed by the DiCarlo Lab at QuTech, features a planar layout of 17 superconducting qubits cross-connected by 24 tunable couplers. This physical architecture is engineered specifically to execute multi-qubit Quantum Error Correction (QEC) protocols and surface-code logic gates. By integrating tunable couplers, the system can dynamically adjust inter-qubit coupling frequencies, suppressing parasitic spectator effects and residual crosstalk during parallel gate operations. This specific hardware optimization strategy was detailed in the team’s peer-reviewed paper, “Optimizing the Frequency Positioning of Tunable Couplers in a Circuit QED Processor to Mitigate Spectator Effects on Quantum Operations,” pu
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quantum-computingQUDORA Partners with QAI to Bring Ion-Trap Quantum Computing to South Korea
Insider Brief Press release – QUDORA Technologies GmbH (“QUDORA“), a European full-stack ion-trap quantum computing company, today announced the signing of a Memorandum of Understanding (MOU) with Korean quantum AI specialist QAI Co., Ltd. (“QAI”) to pursue the deployment of QUDORA’s ion-trap quantum computing technology in Korea and demonstrate hybrid AI-Quantum services for a wide […]
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quantum-computingSpectral Born machines: classically trainable quantum generative models for discrete data
--> Quantum Physics arXiv:2607.06675 (quant-ph) [Submitted on 7 Jul 2026] Title:Spectral Born machines: classically trainable quantum generative models for discrete data Authors:Austin Huang, William Maxwell, Vasilis Belis, Evan Peters, Jason Pye, Soran Jahangiri, Joseph Bowles View a PDF of the paper titled Spectral Born machines: classically trainable quantum generative models for discrete data, by Austin Huang and 6 other authors View PDF HTML (experimental) Abstract:We present \emph{spectral Born machines}, a class of quantum generative models that results from viewing and generalizing the class of IQP Born machines through the lens of group Fourier analysis. These quantum models exploit the quantum Fourier transform to create an inductive bias that make them naturally suited to learning integer-structured data, while remaining classically hard to sample from in general. Similar to IQP Born machines, spectral Born machines can be trained efficiently at scale on classical hardware via a maximum mean discrepancy loss based on graph spectral analysis, which we make available in a new \emph{tcdq} module of the PennyLane software platform. In numerical experiments, we show how the spectral bias of the model leads to significantly reduced parameter counts compared to unstructured approaches, and demonstrate the scalability of the software by training a 190-qubit model with over 1 million parameters to successfully learn a distribution of 93 nucleotide-long ribosomal RNA. Our results suggest that highly over-parameterized spectral Born machines may be immune to overfitting, even in strongly data-scarce regimes. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2607.06675 [quant-ph] (or arXiv:2607.06675v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2607.06675 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Joseph Bowles [view email] [v1] Tue, 7 Jul 2026 18:00:17 UTC (1,839 KB) Full-text li
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quantum-computingNew quantum breakthrough achieves first-known computations of fusion material - Interesting Engineering
An IBM quantum computer was used alongside classical computing to model a key fusion fuel material (Representative image).IBM Scientists from Oak Ridge National Laboratory (ORNL), Cleveland Clinic and IBM have used quantum computers to calculate molecular configurations of a key fusion fuel material, marking what the team says is the first known demonstration of its kind. The work focuses on FLiBe, a molten salt made of fluorine, lithium, and beryllium that is considered one of the leading materials for producing and extracting tritium inside future fusion reactors. Tritium is an extremely scarce hydrogen isotope needed to fuel most proposed fusion power plants. Researchers calculated nine molecular configurations of FLiBe using quantum-centric supercomputing, combining quantum and classical computers to solve a problem that becomes increasingly difficult for conventional computing alone. The results could help scientists better understand how tritium interacts with molten salt at the atomic level, providing insights needed to optimize future fusion reactor designs and improve tritium production. Chasing fusion fuel Securing enough tritium remains one of the biggest challenges facing commercial fusion energy. Because the isotope occurs only in tiny amounts naturally, future reactors are expected to generate their own tritium using materials such as FLiBe inside a surrounding molten salt blanket. Quantum computers are particularly suited for studying the behavior of electrons that determine how atoms bond and interact. In this work, researchers applied the same quantum-centric computing techniques previously used to simulate proteins containing 12,635 atoms, extending the approach from biology into materials science.More from ScienceSee AllScienceQuantum chip just 0.3 inches long stores memory through tiny mechanical vibrationsScienceAerospace engineers cut composite curing time by almost 50% with 3960-FC materialScienceToyota backs Joby’s all-electric air taxis as p
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quantum-computingPodcast with Christopher Godfree, Commercial Director at Across the Pond
Why Startup Storytelling is a Good Investment Overview How do you make your quantum startup stand out in an industry where every company name starts with the letter Q? One powerful way to do that is to tell the stories of the people building the technology. Christopher Godfree, Commercial Director at Across the Pond, has worked with Ai executives, robotics experts and Google’s Quantum Team to explain how their work will change the world. In this episode of The Quantum Spin by HKA, Christopher and host Veronica Combs discuss how storytelling can derisk deep tech investments and why it’s important to do more than just explain how something works to attract new employees and investment. 00:00 Welcome to Quantum Spin00:46 Meet Christopher Godfree01:59 Making Complex Human03:55 Finding Quantum Use Cases08:29 Storytelling Grows Business11:30 Engineers as Story Fuel13:21 Google Quantum Launch Playbook16:12 Branding That Stands Out20:15 Storytelling at Tech Speed24:45 Culture and Quantum Narratives26:50 What’s Next and Wrap Up Christopher Godfree is the Commercial Director of Across the Pond, a creative consultancy and studio in one. Based in London, but with teams in San Francisco and Singapore, he works with some of the world’s fastest-growing tech businesses, helping them tell more effective stories about their science and technology. He has worked in communications for twenty years, including at a scale up tech business in Tokyo, ad agency JWT London, and the BBC. Transcript [00:00:00] Veronica: Hello, and welcome to The Quantum Spin by HKA. I’m Veronica Combs. I’m a writer and an editor here at the agency. I get to talk every day with really smart people working on really fascinating subjects, everything in the Quantum industry, from hardware to software. On our podcast, we focus in on quantum communication, and by that I don’t mean making networks safe from hacking or entangling photons over long distance, but talking about the technology. [00:00:26] How do you explai
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quantum-computingBTQ Technologies Finalizes Full Acquisition of QPerfect to Establish Unified Quantum Infrastructure and Security Ecosystem
BTQ Technologies Finalizes Full Acquisition of QPerfect to Establish Unified Quantum Infrastructure and Security Ecosystem BTQ Technologies Corp. (Nasdaq: BTQ | Cboe CA: BTQ) has officially finalized its full acquisition of French quantum software developer QPerfect SA. Following the receipt of regulatory foreign direct investment (FDI) clearance from the French Ministry for the Economy and Finance, BTQ exercised its definitive option to absorb the remaining outstanding securities of the Strasbourg-based deep-tech startup, rendering QPerfect a wholly owned corporate subsidiary. The transaction integration links BTQ’s existing post-quantum cryptography (PQC) validation structures with QPerfect’s specialized hardware modeling, software emulation, and automation frameworks to deliver a combined, quantum-ready network architecture. [ BTQ - QPerfect Transaction Close Matrix ] Subsidiary Status ──► QPerfect SA finalized as a wholly owned subsidiary of BTQ Technologies Corp. Regulatory Baseline ──► Executed under the June 18, 2026 Prospectus and French FDI sovereign mandates. Core Software Stack ──► MIMIQ™ quantum emulator, Digital Twin modeling, and Quantum Logical Unit (QLU). Integration Mandate ──► Hardening defense, telecom, and critical infrastructure against quantum-enabled risks. The completion of the acquisition allows BTQ to directly monetize and deploy QPerfect’s three proprietary software pillars into industrial networks requiring post-quantum transition verification. The primary layer, MIMIQ™, functions as a high-density software emulator capable of running stable 100+ qubit circuit simulations on conventional classical computing systems to benchmark next-generation Transport Layer Security (TLS) handshakes and stress-test PQC protocol resilience under severe network overhead. This is paired with the Digital Twin framework, which generates software-based structural representations of neutral-atom processors to optimize physical layouts prior to cleanroom fabric
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quantum-computingJin and Colleagues Designs Quantum Circuit for Simulating Incompressible Stokes Flow
A new quantum algorithm tackles the key computational challenges of simulating incompressible Stokes flow, a process vital for understanding microfluidics and low-Reynolds number hydrodynamics. Shi Jin of the University of Science and Technology of China and colleagues use the Schrödingerisation technique and artificial compressibility to reduce the costs of high-dimensional simulations. Their approach designs an explicit quantum circuit encoding the regularised system, showing an exponential speedup in problem dimensionality and validating the method through numerical simulations on Qiskit. The algorithm offers a promising pathway towards efficient simulation of complex fluid dynamics using quantum computation. Quantum algorithm circumvents dimensionality limitations in Stokes equation modelling The developed quantum algorithm achieves an exponential speedup in problem dimensionality, a significant contrast to previous methods hampered by the curse of dimensionality in high-dimensional Stokes equation simulations. This breakthrough overcomes classical computational constraints, enabling the modelling of fluid dynamics in scenarios previously intractable due to the exponential growth of required resources with increasing dimensions. The computational cost of classical methods for solving the Stokes equations scales poorly with dimensionality, often requiring resources that grow exponentially with the number of dimensions. This limitation severely restricts the ability to model complex systems accurately. Combining Schrödingerisation with artificial compressibility, researchers designed an explicit quantum circuit to efficiently encode the regularised system, offering a unified framework for solving these complex equations. Schrödingerisation, a technique borrowed from quantum mechanics, transforms the partial differential equation into a time-dependent Schrödinger equation, allowing it to be solved using quantum algorithms. The artificial compressibility method intr
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quantum-computingGoogle Cuts Surface Code Error Rate to 7.72 × 10−4 With RL
Google Quantum AI researchers report achieving a logical error rate of 7.72 × 10−4 using the surface code, a crucial step toward stable quantum computation. The team unified calibration with computation by repurposing quantum error detection events, typically used for correction, as a learning signal for a reinforcement learning agent. This allows the quantum computer to continuously adjust its control parameters during computation, improving the logical stability of the surface code 3.5-fold against injected drift with complementary decoder steering. Numerical simulations suggest this framework’s optimization speed remains consistent even as quantum codes scale to include over a thousand control parameters, potentially enabling larger, more powerful machines. The researchers state that this work “enables a new paradigm: a quantum computer that learns from its errors and never stops computing.” A logical error rate of 7.72 × 10−4 achieved with the surface code demonstrates a significant advance toward stable quantum computation. Researchers at Google Quantum AI and Google DeepMind unified the processes of calibration and computation to reach this milestone. The core innovation lies in giving the quantum error correction process a dual role: not only correcting the quantum state, but also teaching a reinforcement learning agent to stabilize the system. This framework was experimentally demonstrated on a Willow superconducting processor, improving the logical stability of the surface code 3.5-fold with complementary decoder steering. Beyond surface codes, the team also achieved an average logical error rate of 8.19 × 10−3 with the color code. The reinforcement learning agent manages over a thousand control parameters, which specify how an abstract quantum error correction circuit translates into analog waveforms controlling the quantum system. Numerical simulations reveal the optimization speed of this reinforcement learning framework is independent of system size, su
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quantum-computingBenhemou and Colleagues Designs Automated Framework for Inter-Code Logical CNOT Synthesis
Scientists at Quantinuum have developed a new automated framework that establishes connections between diverse quantum error-correcting codes, addressing a fundamental challenge in the construction of practical, large-scale quantum computers. Asmae Benhemou and Noah Berthusen, from Quantum AI, present a system utilising chain maps to generate logical CNOT circuits between arbitrary CSS codes, resolving limitations encountered when integrating different code families. The approach not only rediscovers established connections between codes but also identifies new, low-depth solutions, potentially improving the efficiency of operations such as code switching and Pauli product measurements in heterogeneous quantum architectures. Automated framework enables low-depth connections between arbitrary quantum error correction Quantinuum researchers achieved a five-fold reduction in the complexity of connecting disparate quantum error correction codes, moving from circuits requiring a depth of ten to those with a depth of two in certain instances. Their automated framework, utilising ‘chain maps’, now enables logical CNOT circuits between arbitrary CSS codes, a key step towards building more flexible quantum computers. CSS codes, named after Calderbank-Shor-Steane, are a prominent class of quantum error-correcting codes defined by their structure relating to classical error-correcting codes. The ability to perform logical operations, such as the CNOT gate, between different CSS codes is crucial for modular quantum computation and fault-tolerant quantum information processing. Previously, such connections were largely limited to structurally related code families, hindering the development of heterogeneous quantum systems. The new method not only replicates established connections but also uncovers novel, low-depth solutions, including those preserving or partially preserving error detection capabilities, and can extend these to full code distance with additional measurements.
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quantum-computingQuantum Software Lab: £6.2M Funds Quantum Cybersecurity for UK Energy Networks
£6.2 million has been awarded to the University of Edinburgh’s Quantum Software Lab to address growing cybersecurity vulnerabilities within the UK energy sector as quantum computing capabilities advance. The project, titled Network Security in a Quantum Future, will deliver the open-source Quantum Threat Tracker, a tool designed to estimate when existing energy systems will become susceptible to quantum attacks, shifting from reactive to proactive security measures. Collaboration is central to the effort, with Scottish Power Energy Networks and National Gas working alongside the University to prepare for these emerging risks. “By combining expertise in quantum computing, uncertainty quantification and energy systems, this project will provide evidence-based tools to support a secure and cost-effective transition to a post-quantum future,” says Dr. Petros Wallden, Deputy Director of Research at the Quantum Software Lab. While current computers would take millions of years to crack certain complex codes, quantum computing promises to dramatically reduce that timeframe, creating a significant cybersecurity threat that demands proactive mitigation. This project, formally known as Network Security in a Quantum Future, moves beyond theoretical risk assessment and into the development of practical tools for energy companies. Complementing this is the Quantum-Aware Risk Management tool, intended to support strategic planning for the adoption of quantum-safe technologies across a broad range of energy assets and prioritize future protective measures. The initiative builds upon earlier research identifying potential vulnerabilities and mitigation strategies, now progressing to deliver operational tools for network operators. Professor Chris Dent added, “In addition to the importance to the energy system of maintaining cyber security in the post-quantum world, our work on the consequences of uncertainty in technology projections is an exciting technical challenge, which we are
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quantum-computingBTQ Technologies Gains Quantum Software From 2023 QPerfect Startup
BTQ Technologies has finalized its acquisition of QPerfect, a French quantum computing company founded in 2023, expanding its quantum software capabilities. The July 8, 2026 announcement details the completion of a deal following a prior strategic investment, bringing QPerfect’s MIMIQ quantum emulator, Digital Twin capabilities, and Quantum Logical Unit directly into BTQ’s technology stack. These additions are intended to strengthen BTQ’s mission of “Building Trusted Quantum Technologies” as organizations prepare for the challenges of post-quantum cryptography. BTQ Technologies, traded on both Nasdaq (BTQ) and CBOE CA (BTQ), believes the transition to quantum security will require optimized hardware, software, simulation, and control layers to enable practical deployment at scale. BTQ Acquisition of QPerfect Advances Trusted Quantum Technologies BTQ Technologies’ completion of its acquisition of QPerfect expands the capabilities available for building practical quantum systems, adding crucial software tools for modeling and testing before hardware deployment. The deal, finalized on July 8, 2026, integrates QPerfect’s specialized technologies directly into BTQ’s infrastructure stack, signaling a strategic push toward verifiable and secure quantum networks. Central to this integration is QPerfect’s MIMIQ quantum emulator, a software platform designed to simulate quantum algorithms on conventional computing infrastructure. BTQ reports that MIMIQ has demonstrated the ability to handle simulations of s + qubit, a significant step toward lowering the barrier to large-scale quantum algorithm development and security testing. Beyond emulation, QPerfect’s Digital Twin technology offers a system modeling capability, allowing researchers to simulate and optimize quantum architectures before physical construction, potentially reducing development costs and accelerating timelines. The third key component is QPerfect’s Quantum Logical Unit (QLU), a multi-layered control framework
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quantum-computingBTQ Technologies Completes Acquisition of QPerfect to Expand Quantum Software Capabilities
Insider Brief Press release – BTQ Technologies Corp. (“BTQ” or the “Company”) (Nasdaq: BTQ) (CBOE CA: BTQ), a global quantum technology company focused on securing mission-critical networks, is pleased to announce that it has completed the acquisition of QPerfect SA (“QPerfect“), a French quantum computing company based in Strasbourg specializing in quantum software, emulation, digital twin capabilities, and control […]
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quantum-computingHartmut Neven
Quantum PeopleHartmut NevenHe turned a NASA side project into the lab that crossed quantum computing’s hardest thresholds, and he keeps telling the world the machines are arriving faster than anyone expects.Founder, Google Quantum AIWillow chip 2024Sycamore supremacy 2019Quantum Echoes 2025Neven’s lawIn this articleFrom computer vision to the quantum frontierHow machine learning led him to qubitsFounding the Google Quantum AI labThe Sycamore supremacy momentWillow and the error correction thresholdNeven’s law and the case for fast progressQuantum Echoes and useful advantageA public voice for quantum computingRecognition and lasting influenceWhy Hartmut Neven mattersFrequently asked questionsHartmut Neven at a glanceBorn1964, Aachen, GermanyNationalityGerman AmericanPhDPhysics, Ruhr University Bochum, 1996Founded Google Quantum AI2012, at NASA AmesCurrent roleVP of Engineering, GoogleLandmark chipWillow, 105 qubits, 2024Earlier milestoneSycamore supremacy, 2019Latest resultQuantum Echoes advantage, 2025Known forNeven’s law of doubly exponential progressKey takeawaysHartmut Neven founded Google Quantum AI in 2012 and still leads it as a Vice President of Engineering, one of the longest continuous tenures at the top of any quantum program.He reached quantum computing from computer vision, having built face recognition startups that Google acquired in 2006 before he turned to qubits.Under his direction the lab produced the 2019 Sycamore supremacy result and the 2024 Willow chip that crossed the error correction threshold.In 2025 the team reported a verifiable quantum advantage with its Quantum Echoes algorithm, a result another quantum machine can check.Neven’s law captures his central public message, that quantum progress is arriving at a doubly exponential pace that most observers underestimate.From computer vision to the quantum frontierHartmut Neven built one of the most consequential research programs in modern computing, yet his path into quantum hardware ran thro
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quantum-computingDario Gil
Quantum PeopleDario GilFrom the head of IBM Research to a seat shaping American science, Dario Gil has spent two decades arguing that quantum computing deserves a national strategy.US Under Secretary for ScienceFormer IBM Research DirectorMIT PhDQuantum advantage advocateIn this articleWho Dario Gil isFrom Los Altos to MITTwo decades inside IBM ResearchBuilding the IBM Quantum programQuantum utility and the supremacy debateA bridge into national science policyUnder Secretary for ScienceRecognition and academic tiesHow Dario Gil talks about quantum computingWhy Dario Gil matters in quantum computingFrequently asked questionsDario Gil at a glanceBorn1975, El Palmar, Murcia, SpainSchoolingLos Altos High School, California, 1993BachelorStevens Institute of Technology, 1998DoctoratePhD, MIT, 2003 (Electrical Engineering and Computer Science)IBM tenureJoined 2003; Director of IBM Research from 2019IBM top roleSenior Vice President and Director of IBM ResearchNational Science BoardMember 2020; Chair from 2024HonorsNational Academy of Engineering; two honorary doctorates; former PCAST memberCurrent roleUS Under Secretary for Science, confirmed 18 September 2025Succeeded at IBM byJay Gambetta, effective 1 October 2025Key takeawaysDario Gil led IBM Research as Senior Vice President and Director, then left the company in 2025 to become the US Under Secretary for Science.He was confirmed by the Senate on 18 September 2025 by a vote of 51 to 47, and took up the Department of Energy role later that month.Jay Gambetta succeeded him as Director of IBM Research, effective 1 October 2025.Gil pushed the idea of quantum utility and advantage over a single supremacy milestone, favouring reliable, useful machines.As Under Secretary for Science he now oversees the Office of Science and its role across the seventeen US National Laboratories. Who Dario Gil is Dario Gil is one of the most influential figures connecting quantum research, corporate science, and United States national policy. B
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quantum-computingJay Gambetta
Quantum PeopleJay GambettaThe physicist who turned a research curiosity into a global quantum program, plotting IBM’s course from cloud qubits to fault tolerance.Director of IBM ResearchIBM FellowQiskit leaderSuperconducting qubitsNYSE: IBMIn this articleFrom open quantum systems to a global programJoining IBM and building the quantum stackQiskit and the software that made hardware usableMeasuring progress and the rise of Gambetta’s lawThe era of quantum utilityThe machines that house the qubitsDesigning the road to fault toleranceThe race to verifiable quantum advantagePartnerships that anchor the roadmapA leadership style rooted in physicsThe honors and the record behind themWhy Jay Gambetta matters in quantum computingFrequently asked questionsJay Gambetta at a glanceBorn29 January 1979, AustraliaFieldQuantum computing, physicsPhDGriffith University, 2004, under Howard WisemanPostdocsYale University and the Institute for Quantum Computing, WaterlooJoined IBM2011IBM Fellow2018VP, Quantum2019Director, IBM ResearchSince October 2025EmployerIBM, traded on the New York Stock Exchange as IBMKey takeawaysJay Gambetta is the Australian-born physicist behind IBM’s quantum roadmap, and since 1 October 2025 he has served as Director of IBM Research, succeeding Dario Gil.He joined IBM in 2011, became an IBM Fellow in 2018, and drove the decisions to put quantum hardware on the cloud and to build the open-source Qiskit toolkit.He championed the idea of quantum utility, backed by the 2023 IBM and UC Berkeley experiment on the 127-qubit Eagle processor published on the cover of Nature.The roadmap he oversees targets verifiable quantum advantage by the end of 2026 and Starling, a fault-tolerant machine with roughly 200 logical qubits, by 2029.Its error-correction bet rests on qLDPC bivariate bicycle codes, which IBM says need roughly ten times fewer physical qubits than the surface code.Jay Gambetta is the Australian-born physicist who sets the technical direction for one of the
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quantum-computingQpiAI Open-Sources Quantum SDK for 8- and 25-Qubit Cloud Access
QpiAI has released its Quantum SDK as open-source software, immediately providing developers with a pathway to run algorithms on the company’s 8-qubit and 25-qubit quantum computers via QpiAI-QCloud. The Python-based toolkit includes both local state-vector and density matrix simulators, allowing for algorithm prototyping and validation before utilizing actual quantum hardware. This move is designed to expand access to quantum software development for a global audience, fostering innovation across industries like finance, logistics, and artificial intelligence. “Quantum computing will scale only when developers can experiment, learn, and deploy without friction,” said Lakshya Priyadarshi, VP, Quantum Platforms & Solutions, QpiAI, emphasizing the SDK’s role as a bridge between theory and real-world application. QpiAI Quantum SDK Enables Algorithm Development and Hardware Access QpiAI has empowered developers with direct access to quantum hardware through the open-sourcing of its Quantum SDK, a move that bypasses the typical limitations of simulation-only environments and facilitates real-world algorithm testing. The Python-based toolkit is now freely available at https://github.com/qpiai/quantum-sdk and allows users to deploy algorithms on QpiAI’s 8-qubit and 25-qubit quantum computers via the QpiAI-QCloud platform at https://qcloud.qpiai.tech, representing a significant step toward democratizing access to quantum resources. This release isn’t merely about providing software; it’s about establishing a tangible connection between theoretical development and practical execution, crucial for accelerating progress in the field. QpiAI intends this release to broaden participation in quantum software development, targeting developers, researchers, universities, startups, and enterprise innovation teams globally. This dual approach is designed to optimize the development lifecycle, allowing for rapid iteration and refinement of quantum solutions. The toolkit is engineer
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quantum-computingQpiAI Launches Open Quantum SDK for Developers and Researchers
Insider Brief Press release – QpiAI, a globally leading full-stack quantum computing company, today released the QpiAI Quantum SDK as open-source software. Available now at https://github.com/qpiai/quantum-sdk, the QpiAI Quantum SDK gives developers, researchers, and startups an accessible, developer-friendly toolkit to build and run quantum algorithms and to connect their quantum development workflows directly to QpiAI’s 8-qubit […]
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quantum-computingQpiAI Open-Sources Its Quantum SDK to Accelerate Global Quantum Software Development
BENGALURU, India — QpiAI, a globally leading full-stack quantum computing company, today released the QpiAI Quantum SDK as open-source software. Available now at the QpiAI Quantum SDK gives developers, researchers, and startups an accessible, developer-friendly toolkit to build and run quantum algorithms and to connect their quantum development workflows directly to QpiAI’s 8-qubit and […]
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