u/Earachelefteye

Even a broken clock is right twice a day

“Section 1.  Background and Policy.  The advent of large-scale quantum computers, particularly in the hands of adversaries, will pose a significant threat to widely used cryptographic security systems.  Ongoing cyber activity against our Nation also presents the risk of adversaries collecting United States information now, and decrypting it later once large-scale quantum computers are operational.  In light of these threats, the United States must take steps to strengthen cryptographic protections for the Nation’s sensitive data, critical infrastructure, and digital economy.
It is the policy of the United States to safeguard national security and maintain technological leadership by responsibly and effectively executing the transition of Federal information systems to National Institute of Standards and Technology (NIST)-approved Federal Information Processing Standards (FIPS) for Post-Quantum Cryptography (PQC), and to assist critical infrastructure owners and operators with their transitions.”

“Types of Quantum Programming Languages
The ecosystem divides into three layers.
1. Instruction-set languages (OpenQASM, Quil) sit close to the hardware, providing low-level gate sequences that quantum processors can execute directly. Powerful, but requiring deep knowledge of quantum gate design.
2. High-level SDKs (Qiskit, Cirq, Q#, PennyLane) provides Python-based frameworks with abstractions for circuit construction, noise modeling, and optimization. These are what most developers use.
3. Domain-specific languages (Bloqade for neutral atoms) target specific hardware platforms or problem types, trading generality for specialization.
Quantum Programming Frameworks
The following is a non-exhaustive selection. The landscape is broad and evolving rapidly, and the inclusion or omission of any entry should not be interpreted as a ranking or endorsement.
QISKIT (IBM)
Qiskit is the most widely used quantum programming framework, maintained by IBM with a large global developer community. The v1.4 series received security support until March 2026, with full support now on v2.x+. The v2.x series introduced a C++ interface powered by a C-API, enabling HPC-accelerated error mitigation; v2.2 completed end-to-end C++ workflow support with the addition of a transpiler function. A 24% accuracy improvement in dynamic circuits at 100+ qubit scale was confirmed at IBM’s Quantum Developer Conference in November 2025.
Qiskit Functions, expanded in 2025, provides pre-built quantum services across chemistry, optimization, and machine learning. The framework covers educational use, research prototyping, and production application development, and carries the widest ecosystem and largest hiring premium across quantum industry roles.
CIRQ (GOOGLE)
Cirq is Google’s open-source quantum framework, optimized for superconducting qubit architecture. It emphasizes simulation fidelity, parameterized circuits, and variational quantum algorithms, with integration into Google’s quantum processor cloud and TensorFlow for hybrid quantum-classical workflows. Cirq is primarily a research and algorithm prototyping tool.
PENNYLANE (XANADU)
PennyLane is the primary framework for quantum machine learning, providing automatic differentiation of quantum circuits and integration with PyTorch, TensorFlow, and NumPy. According to Xanadu’s published benchmarks, Lightning simulators scale to 1,000+ AMD GPUs with strong scaling via Kokkos, demonstrated on Oak Ridge’s Frontier supercomputer.
PennyLane supports hardware from IonQ (via Qiskit/Cirq plugins), Rigetti, D-Wave, AWS Braket, and IQM through hardware-agnostic APIs. In 2025, PennyLane’s Catalyst compiler integrated with Open Quantum Design’s trapped-ion hardware control signals, enabling quantum frontends to compile directly to hardware instructions – described by Xanadu and Open Quantum Design as the first fully open-source quantum computing stack from software to hardware.
Q# (MICROSOFT)
Q# is Microsoft’s dedicated quantum programming language for Azure Quantumintegration. Its syntax draws from C# and Python, with an emphasis on type safety and quantum resource management. Compiler features cover qubit allocation, gate optimization, and resource estimation – particularly useful for evaluating what a quantum program will actually require on real hardware.
AMAZON BRAKET SDK
Amazon Braket SDK is a Python framework for quantum circuits on AWS quantum processors and simulators, supporting hardware from IonQ, Rigetti, D-Wave, and AWS’s own systems through unified APIs. The SDK integrates with SageMaker and Lambda for hybrid classical-quantum workflows, making it a practical choice for organizations already operating in AWS environments.
CUDA-Q (NVIDIA)
CUDA-Q is NVIDIA’s QPU-agnostic platform for accelerated quantum-classical computing,released as open-source in 2023. It supports both Python and C++ and integrates with 75% of publicly available QPUs, including IonQ, Quantinuum, and Pasqal – using MLIR/LLVM/QIR compilation with GPU-accelerated simulators.
In 2026, Classiq integrated CUDA-Q for accelerated hybrid workflows, reducing circuit synthesis and execution of a 31-qubit circuit from 67 minutes to 2.5 minutes on a single NVIDIA A100 GPU. Similarly, QCentroid also combined its QuantumOps platform with CUDA-Q, giving enterprise users access through an AI copilot layer.
BLOQADE (QUERA)
Bloqade is QuEra’s specialized SDK for neutral-atom quantum computing, supporting both digital and analog quantum computing paradigms. Bloqade-analog handles analog/Hamiltonian simulation on neutral atoms; Bloqade handles gate-based digital circuit execution. Version 0.26+ provides fine-grained control over atom positioning, mid-circuit measurements, and real-time feedback. 
STIM (GOOGLE)
Stim is Google’s specialized library for quantum error correction simulation and stabilizer codes. It can analyze a distance 100 surface code circuit in 15 seconds and then sample shots at kilohertz rates, making it the primary tool for researchers designing fault-tolerant quantum computers. Not a general programming framework – purpose-built for error correction work. 
PYQUIL (RIGETTI)
PyQuil is Rigetti’s Python framework for superconducting quantum processors, built around the Quil language and quantum-classical virtual machines. Rigetti’s focus on hybrid algorithms makes PyQuil relevant for applications requiring tight integration between classical and quantum processing.
OPENQASM (OPEN STANDARD)
OpenQASM is an open-source hardware-agnostic quantum instruction language maintained by IBM and the quantum community. QASM 3.0 introduced classical control structures, function definitions, and classical variable integration. Most quantum compilers target OpenQASM as an intermediate representation – it is infrastructure rather than a user-facing framework.
QUIPPER (ACADEMIC)
Quipper is an academic quantum programming language from Dalhousie University, written in Haskell with strong typing, circuit templating, and scalable parameterized circuits. Not widely used in industry, but valued in research settings for exploring quantum programming language theory.
PYTKET (QUANTINUUM)
PyTKET is Quantinuum’s compiler framework for quantum circuit optimization and hardware compilation across IBM, Google, IonQ, and other backends. it works as a post-compilation optimization layer that improves fidelity on noisy hardware.”

“Winner: Technology
Xanadu
Pioneering quantum technology for the masses

Imagine a maze. A conventional computer tests each possible path, one by one, until it finally finds the exit. A quantum computer uses qubits—units of information that can exist in multiple states at once. These can explore many possibilities simultaneously and solve complex problems far faster: calculations that would take today’s supercomputers thousands of years might be done in hours. Quantum computing could transform AI, health care, transportation and more.
Born in Australia, founder Christian Weedbrook pursued a Ph.D. in quantum computing at the University of Queensland, followed by postdoctoral work at MIT and the University of Toronto. Canada’s major government investments in quantum research were a huge draw for him. The National Research Council projects its quantum sector could reach $139 billion by 2045, supporting 200,000 jobs, while the global market could hit US$1.3 trillion by 2035.

Weedbrook is particularly excited about quantum computing’s potential for drug discovery. Right now, testing can take 10 to 15 years and billions of dollars, while 90 per cent of early candidates fail. Quantum systems could accelerate this process, improving success rates and enabling faster responses to future pandemics.”

“AMD also added a new position in Xanadu Quantum Technologies, valued at about $1.5 million.”

just read pasqal joins xndu in the finals,
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Finalist teams were chosen through a thorough evaluation of their submissions, which demonstrated plausible pathways to quantum advantage, clear technical novelty, and strong algorithmic rigor. Judges assessed whether teams addressed problem domains where quantum methods could make a difference if resources scale as projected, supported by early resource estimates and classical comparisons. Submissions were also evaluated for evidence beyond conceptual sketches, including quantifying assumptions, acknowledging limitations, and benchmarking against classical methods rather than relying solely on theoretical asymptotic scaling arguments.”

“Ab initio wavefunction methods provide accurate molecular simulations but their computational scaling restricts applications to small systems. We develop a workflow combining quantum embedding to decompose a molecule into fragments with a heterogeneous quantum-classical (HQC) method to simulate fragments. We sample fragment electronic configurations on two 156-qubit quantum processors (ibm
_
cleveland, ibm
_
kobe), using up to 94 qubits, running 9,200 circuits for over 100 hours, collecting
1.3⋅
10
9
measurement outcomes - the most resource-intensive HQC computation for quantum chemistry to date. We compute fragment wavefunctions via optimized subspace diagonalization on two supercomputers (Fugaku, Miyabi-G), achieving 72.5
%
parallel efficiency with scalable distributed linear algebra kernels. We simulate two protein-ligand complexes spanning dispersion- and electrostatics-dominated regimes (11,608 and 12,635 atoms), demonstrate
>40×
increase in system size and up to
210×
improvement in accuracy over the previous state-of-the-art, with HQC matching coupled-cluster (CCSD) accuracy in fragment energies, and establish a scalable pathway for systematically improvable biomolecular simulations.”

“Quantum computing is particularly well suited to two kinds of problems at the heart of the energy and climate challenge.
The first is simulation. Climate systems are complex networks of feedback loops and dependencies. A quantum algorithm can model a system using the same physical laws it follows, promising greater accuracy than classical computers can achieve.
The second is material discovery. Many energy technologies hinge on designing materials with specific properties. Right now, finding these materials still relies too much on trial and error, supported by calculations that do not fully capture molecular behavior. Quantum computing can simulate those interactions more faithfully, cutting down uncertainty and speeding the path from lab to market.”

“Revenue Exceeds Midpoint of Guidance Range by 30%
Reported Record GAAP Revenues of $64.7 Million, Representing 755% Year-On-Year Growth, Fueled by Quantum Computing Growth and Expansion of the Quantum Platform
Raises Full Year Guidance to be between $260 and $270 Million as Remaining Performance Obligations grow 554% year-on-year to $470 Million
Continued to Drive Commercial Momentum with Approximately 60% of Revenue from Commercial Customers, 35% of Revenue from International Customers, and 35% of Revenue from Multi-Product Customers
Sold IonQ’s First 6th-Generation, Chip-Based, 256-Qubit System, Anchored by a Secure Quantum Network and Broad IP-Generation Partnership Spanning Computing, Networking, Sensing, and Security. Demand for Fifth-Generation Tempo Remains Strong
Selected for DARPA’s HARQ Program, Reflecting IonQ’s Leadership in Modular Quantum Computing and Scalable Networking Architectures Using Quantum Interconnects
Published World’s First Definitive and Detailed Architectural Blueprint For Fault-Tolerant Quantum Computing, Setting a New Standard for Technical Specificity and Transparency
“

“Users of Frontier and the broader Oak Ridge Leadership Facility (OLCF) community can now utilize PennyLane to write and execute quantum programs directly on the Frontier supercomputer using PennyLane's high-performance Lightning simulator. This collaboration pushes the limits of quantum computing simulation by merging Frontier's exascale capabilities, powered by AMD's CPUs and GPUs, with PennyLane's accessible programming interface.

In order to prototype, test, and validate larger quantum programs and algorithms, researchers often need supercomputing resources to simulate a large number of qubits. With the ability to run PennyLane on Frontier, researchers can now explore complex problems and identify performance bottlenecks that are not present within smaller simulations.

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