The Platforms That Power Quantum and Physical AI

NVIDIA's GPU clusters provide the classical high-performance compute layer that runs quantum-classical hybrid algorithms at scale. CUDA-Q, NVIDIA's open-source quantum computing platform, enables simulation of quantum circuits and seamless integration of quantum processors with GPU-accelerated classical workloads — bridging the gap between today's noisy quantum hardware and production-grade optimisation.

NVIDIA Jetson and Isaac platforms power the edge AI inference that drives Physical AI agents — robotic arms, autonomous mobile robots, vision inspection systems, and intelligent network units. NVIDIA's Omniverse digital twin environment allows Physical AI systems to be trained and validated in photorealistic simulation before deployment on the factory floor or network infrastructure.

Microsoft is pursuing a fundamentally different path to fault-tolerant quantum computing through topological qubits — embodied in Majorana 1, the world's first topological quantum processor. Unlike conventional qubits that are prone to environmental noise, topological qubits encode quantum information in non-Abelian anyons, making them inherently more stable and error-resistant. Azure Quantum provides a unified cloud platform that integrates Microsoft's topological hardware alongside partner quantum systems from IonQ, Quantinuum, and Rigetti, giving enterprises a single access point to the full quantum computing landscape.

Microsoft Azure AI and Copilot infrastructure provide the enterprise-grade AI orchestration layer that Physical AI systems require to operate at scale. Azure IoT, Digital Twins, and Edge computing services connect Physical AI agents on the factory floor and communications network directly to quantum-computed decision models running in the cloud. Microsoft's partnership with Quantinuum on reliable quantum computing — achieving logical qubit error rates 800× better than physical qubits — accelerates the timeline for quantum-enhanced Physical AI in manufacturing and industrial operations.

IBM is the world's most advanced superconducting quantum computing platform, with its Heron and Condor processors delivering 156 and 1,121 qubits respectively. IBM Quantum Network connects over 250 organisations — including Fortune 500 companies, research universities, and national laboratories — to IBM's quantum systems via the cloud. IBM's Qiskit open-source quantum SDK is the most widely adopted quantum programming framework globally, providing the software layer that connects Physical AI decision pipelines to quantum hardware. IBM's quantum roadmap targets 100,000-qubit fault-tolerant systems by 2033, with error-corrected logical qubits already demonstrated on IBM Heron.

IBM's watsonx AI platform provides the enterprise-grade foundation model and AI orchestration layer that Physical AI systems require for natural language understanding, multimodal perception, and autonomous decision-making at scale. IBM's hybrid cloud infrastructure (Red Hat OpenShift) enables Physical AI agents deployed at the edge — in factories, networks, and infrastructure — to seamlessly access quantum-computed optimisation models running in IBM Cloud. IBM's AI for IT Operations (AIOps) and Maximo Asset Management platforms connect Physical AI maintenance agents to quantum-optimised scheduling, enabling predictive and prescriptive maintenance across industrial and utilities assets.

Nokia Bell Labs is at the forefront of quantum-safe cryptography and quantum networking research, developing post-quantum encryption protocols that protect the communications infrastructure and optimises. Nokia's network telemetry and analytics platforms provide the real-time data streams that feed quantum optimisation algorithms — enabling millisecond-level network state awareness across thousands of nodes.

Nokia's private wireless networks (NDAC — Nokia Digital Automation Cloud) provide the ultra-low-latency, high-reliability connectivity that Physical AI agents require to act on quantum-computed decisions in real time. Nokia's industrial-grade 5G infrastructure connects robotic systems, autonomous vehicles, and intelligent sensors to the quantum decision layer without the latency constraints of public networks.

Ericsson is advancing quantum-safe networking and quantum-enhanced radio resource management through its research labs and partnerships with quantum computing providers. Ericsson's Radio System portfolio and Cloud RAN architecture generate the real-time network telemetry that feeds quantum optimisation algorithms for dynamic spectrum allocation, interference management, and traffic engineering across dense 5G deployments. Ericsson's post-quantum cryptography roadmap ensures that the communications infrastructure connecting Physical AI agents remains secure against quantum-enabled threats.

Ericsson's Intelligent Automation Platform (EIAP) and rApp ecosystem provide the AI-driven network orchestration layer that Physical AI agents depend on for guaranteed connectivity. EIAP's agentic AI capabilities enable autonomous network healing, optimisation, and deployment — ensuring that robotic systems, autonomous vehicles, and industrial IoT devices maintain the sub-millisecond latency and ultra-high reliability required for real-time Physical AI operation. Ericsson's private 5G solutions for industrial campuses deliver the dedicated wireless fabric that connects Physical AI execution systems directly to quantum decision models at the edge.

Siemens is integrating quantum computing into its industrial software stack through partnerships with IBM Quantum and internal research at Siemens Technology. Siemens' Xcelerator platform — spanning MindSphere, Teamcenter, and Opcenter — provides the digital thread that connects quantum-computed optimisation outputs directly into manufacturing execution systems. Siemens' quantum-classical hybrid algorithms target production scheduling, energy optimisation, and logistics routing problems across its global manufacturing customer base.

Siemens' SIMATIC industrial controllers, SINUMERIK CNC systems, and SIMOTION motion control platforms are the Physical AI execution layer in factories worldwide. Siemens' Industrial Edge and MindSphere IoT platforms connect shop-floor Physical AI agents to cloud-based quantum decision models, enabling closed-loop optimisation from sensor data to machine action. Siemens' digital twin technology (Simcenter, NX) allows Physical AI systems to be validated in simulation before deployment, reducing commissioning risk.

Schneider Electric's EcoStruxure architecture generates the operational data streams — from electrical distribution to HVAC to production lines — that quantum optimisation algorithms require to solve energy dispatch, demand response, and grid balancing problems. Schneider's collaboration with quantum software partners targets quantum-enhanced optimisation of building energy management, microgrid scheduling, and industrial process control, where combinatorial complexity exceeds classical solver capabilities.

Schneider Electric's Modicon PLCs, SCADA systems, and EcoStruxure Machine Expert provide the Physical AI control layer for energy-intensive industries including data centres, manufacturing, and utilities. Schneider's AVEVA industrial software suite connects Physical AI agents to real-time process data, enabling quantum-computed energy and production schedules to be executed autonomously on the plant floor. Schneider's Universal Automation initiative promotes open, software-defined control that accelerates Physical AI deployment across heterogeneous industrial environments.

Rockwell Automation's FactoryTalk analytics platform and Plex Smart Manufacturing Cloud provide the operational data infrastructure that feeds quantum optimisation models for production scheduling, predictive maintenance, and supply chain coordination. Rockwell's partnership with PTC (Vuforia, ThingWorx) and Microsoft Azure creates a connected enterprise data layer where quantum algorithms can access real-time machine state, quality, and throughput data to compute globally optimal production decisions.

Rockwell's Allen-Bradley ControlLogix PLCs, Kinetix servo drives, and PowerFlex variable frequency drives form the Physical AI actuation backbone in discrete and process manufacturing. FactoryTalk Analytics GuardianAI delivers autonomous anomaly detection and predictive maintenance, enabling Physical AI systems to act on quantum-computed maintenance schedules before equipment failure occurs. Rockwell's Logix ecosystem provides the deterministic, real-time control layer that translates quantum-optimised production plans into precise machine commands on the factory floor.

Ansys is integrating quantum computing into its multiphysics simulation stack to tackle computational fluid dynamics, structural mechanics, and electromagnetics problems that exceed classical HPC capacity. Ansys' partnership with quantum hardware providers targets quantum-accelerated finite element analysis and topology optimisation — enabling engineers to explore vastly larger design spaces for aerospace, automotive, and industrial components. Ansys' open simulation ecosystem provides the validated physics models that quantum algorithms require to produce physically meaningful optimisation outputs.

Ansys' Twin Builder and Fluent platforms create the high-fidelity digital twins that Physical AI systems use to predict equipment behaviour, optimise control strategies, and validate autonomous decisions before execution on real hardware. Ansys' real-time simulation capabilities enable Physical AI agents to run physics-based inference at the edge — allowing robotic systems and autonomous machines to anticipate mechanical loads, thermal limits, and fluid dynamics in real time. Ansys' integration with industrial IoT platforms closes the loop between live sensor data and simulation-driven Physical AI control.

Dassault Systèmes is exploring quantum computing to accelerate the combinatorial optimisation problems embedded in product lifecycle management — from generative design and materials discovery to supply chain configuration and manufacturing process planning. Through its 3DEXPERIENCE platform, Dassault connects quantum-computed design and optimisation outputs directly into the engineering and manufacturing workflows of aerospace, automotive, life sciences, and industrial equipment customers. Dassault's SIMULIA simulation suite provides the physics-validated environment where quantum algorithms can be benchmarked against real engineering constraints.

Dassault Systèmes' 3DEXPERIENCE platform provides the virtual twin foundation that Physical AI systems use to plan, simulate, and validate autonomous operations before deployment in the physical world. DELMIA manufacturing operations management and CATIA design tools connect Physical AI agents to the full product and process knowledge base — enabling autonomous systems to understand part geometry, assembly sequences, and quality requirements without human programming. Dassault's virtual twin technology allows Physical AI systems in aerospace, automotive, and industrial manufacturing to continuously update their operational models from live sensor feedback, maintaining alignment between the digital and physical worlds.

ABB is exploring quantum computing to solve the combinatorial complexity in robot motion planning, energy dispatch, and grid optimisation that classical algorithms cannot address at scale. ABB's collaboration with quantum software partners targets quantum-enhanced scheduling for its electrification and grid automation businesses — where optimal power flow across thousands of nodes and assets requires computational capabilities beyond classical solvers. ABB's digital twin platform (ABB Ability) provides the real-time operational data streams that quantum optimisation algorithms require to compute globally optimal control decisions.

ABB's YuMi and GoFa collaborative robots, IRB industrial robot arms, and OmniCore robot controllers form one of the world's most comprehensive Physical AI execution platforms — deployed in automotive, electronics, food and beverage, and logistics industries globally. ABB Ability industrial IoT platform connects Physical AI agents to quantum-computed production schedules and maintenance plans, enabling closed-loop autonomous operation from sensor data to robot action. ABB's electrification portfolio — from grid automation to EV charging infrastructure — provides the energy management layer that Physical AI systems require to operate sustainably at scale.

Honeywell Quantum Solutions operates the world's highest-fidelity trapped-ion quantum computers — the H-Series systems that form the basis of Quantinuum (Honeywell's quantum computing joint venture with Cambridge Quantum). Honeywell's quantum volume leadership and its integration of quantum computing into industrial process optimisation make it a unique catalyst that bridges quantum hardware development and real-world Physical AI deployment. Honeywell's quantum-classical hybrid algorithms target refinery scheduling, chemical process optimisation, and supply chain coordination — problems where quantum advantage translates directly into industrial value.

Honeywell's Experion Process Knowledge System (PKS) and Universal Process Controllers provide the Physical AI control layer for oil and gas, refining, chemicals, and power generation — industries where autonomous operation requires quantum-level decision optimisation. Honeywell Forge Industrial IoT platform connects Physical AI agents to real-time process data across thousands of sensors and actuators, enabling quantum-computed process schedules to be executed autonomously on the plant floor. Honeywell's Advanced Process Control (APC) and Profit Suite optimisation software provide the algorithmic bridge between quantum-computed optimal setpoints and physical plant control systems.

Autodesk is investigating quantum computing to accelerate generative design — the AI-driven process that explores millions of design permutations to find optimal structures for weight, strength, and manufacturability. Quantum-enhanced topology optimisation could dramatically expand the design space that Autodesk Fusion and Inventor can explore, enabling engineers to discover novel geometries for aerospace, automotive, and industrial components that classical algorithms cannot reach within practical timeframes. Autodesk's Forge platform provides the cloud-based design data infrastructure that quantum design algorithms require to access and process complex 3D geometry at scale.

Autodesk Fusion's integrated CAD/CAM/CAE environment connects Physical AI manufacturing systems directly to the design intent — enabling CNC machines, additive manufacturing systems, and robotic fabrication cells to autonomously interpret and execute complex geometries without manual programming. Autodesk's Informed Design platform uses AI to continuously optimise product designs based on real-world manufacturing performance data, creating a closed loop between Physical AI production systems and the design models they execute. Autodesk's construction and infrastructure software (BIM 360, Revit, Civil 3D) extends Physical AI capabilities into the built environment — enabling autonomous construction robots and infrastructure monitoring systems to operate from rich digital models.

Synopsys is at the intersection of quantum computing and semiconductor design — developing EDA tools capable of designing the quantum processors and quantum-classical interface chips that next-generation Physical AI systems will require. Synopsys.ai, the industry's first full-stack AI-driven EDA platform, applies machine learning to chip design optimisation problems that share structural similarity with quantum combinatorial optimisation — providing a natural integration point for quantum-accelerated design space exploration. Synopsys' partnership with TSMC on angstrom-scale process nodes ensures that the quantum-era chips designed with Synopsys tools can be manufactured at the leading edge of semiconductor technology.

Synopsys provides the silicon intelligence layer that Physical AI systems depend on — from the custom AI accelerator chips designed with Synopsys Design Compiler and IC Compiler to the embedded processor IP (ARC, RISC-V) that runs inference at the edge. Synopsys' functional safety and security IP portfolio ensures that Physical AI chips deployed in automotive, industrial, and medical applications meet the rigorous reliability standards required for autonomous operation. Synopsys' Software Integrity Group provides the security testing and verification tools that validate Physical AI software stacks before deployment on safety-critical hardware.

D-Wave's Advantage quantum annealing processors are purpose-built for the combinatorial optimisation problems at the core of AI's value proposition — production scheduling, network routing, supply chain configuration, and resource allocation. With over 5,000 qubits and 15-way qubit connectivity, D-Wave's systems solve optimisation problems that are intractable for classical computers, delivering quantum advantage on real industrial workloads today.

D-Wave's Leap quantum cloud platform enables real-time integration of quantum-computed solutions into operational workflows, providing the low-latency API access that Physical AI execution systems require. D-Wave's hybrid solvers combine quantum and classical compute to handle the large-scale, constrained optimisation problems that arise when Physical AI systems feed back real-world state data into the quantum decision layer.

IonQ builds universal quantum computers using trapped ytterbium ions — individual atoms suspended in electromagnetic fields and manipulated with laser pulses. This approach delivers world-record two-qubit gate fidelity exceeding 99.99%, making IonQ's systems the most accurate commercially available quantum processors. IonQ's Forte and Aria systems are accessible via major cloud platforms and provide the high-fidelity quantum circuit execution that complex optimisation and machine learning algorithms demand.

IonQ's quantum-classical hybrid capabilities allow Physical AI systems to offload computationally intensive inference and optimisation tasks to quantum processors in real time. IonQ's cloud-native architecture integrates directly with enterprise AI pipelines, enabling Physical AI agents in manufacturing, logistics, and autonomous systems to leverage quantum-enhanced decision models without requiring on-premises quantum hardware.

Rigetti Computing designs and fabricates superconducting quantum processors in its own semiconductor foundry — one of the few quantum companies with full-stack hardware control from chip design to cloud deployment. Rigetti's multi-chip architecture and chiplet-based scaling approach targets 1,000+ qubit systems, while its Quil quantum instruction language and Forest SDK provide a developer-friendly interface for building and executing quantum algorithms across its Ankaa and Novera processor families.

Rigetti's QCS (Quantum Cloud Services) platform provides low-latency, dedicated quantum compute access that Physical AI systems can invoke as part of real-time decision loops. Rigetti's hybrid quantum-classical programming model enables Physical AI applications in manufacturing and communications operations optimisation to seamlessly blend classical ML inference with quantum-enhanced sampling and search, accelerating the path from sensor data to actionable decisions.

Quantinuum — formed from the merger of Honeywell Quantum Solutions and Cambridge Quantum — operates the H-Series trapped-ion quantum computers, which consistently achieve the highest quantum volume scores of any commercially available system. The H2 processor delivers 99.8% two-qubit gate fidelity using Honeywell's QCCD (Quantum Charged Coupled Device) architecture, enabling mid-circuit measurement and qubit reuse that dramatically extends effective circuit depth for real-world quantum algorithms.

Quantinuum's TKET quantum compiler and InQuanto computational chemistry platform allow Physical AI systems to run quantum-enhanced molecular simulations that directly inform physical-world manufacturing and process decisions. Quantinuum's partnership with Microsoft Azure Quantum brings fault-tolerant quantum capabilities to enterprise AI pipelines, enabling Physical AI agents to leverage quantum-computed insights at cloud scale.

Xanadu pioneers photonic quantum computing — using photons (particles of light) as qubits, enabling quantum processors that operate at room temperature without the extreme cryogenic cooling required by superconducting systems. Xanadu's Borealis photonic processor demonstrated quantum computational advantage, and its Aurora architecture targets fault-tolerant, scalable quantum computing using squeezed light and continuous-variable quantum information. This approach is inherently suited to quantum communication and quantum networking applications.

Xanadu's PennyLane — the world's leading open-source quantum machine learning framework with over 1 million downloads — enables Physical AI developers to build quantum-enhanced neural networks, quantum kernels, and variational quantum algorithms that run on any quantum hardware or simulator. PennyLane's differentiable programming model allows quantum circuits to be trained like neural networks, directly accelerating Physical AI perception, planning, and control systems with quantum-native machine learning.

QuEra Computing operates Aquila, a 256-qubit neutral-atom quantum processor available on Amazon Braket — one of the largest gate-model quantum computers publicly accessible. QuEra's neutral-atom architecture uses laser-cooled rubidium atoms as qubits, enabling programmable connectivity and long coherence times that are difficult to achieve with superconducting systems. QuEra's collaboration with Harvard University produced landmark results in error-corrected logical qubits, demonstrating that neutral-atom platforms can achieve fault-tolerant quantum computation at commercially relevant scales.

QuEra's Aquila processor provides Physical AI systems with access to quantum optimisation and quantum simulation capabilities via cloud API — enabling real-time integration of quantum-computed solutions into autonomous decision pipelines. QuEra's neutral-atom architecture is particularly suited to solving graph-based optimisation problems that arise in Physical AI applications such as logistics routing, sensor network configuration, and multi-robot task allocation. QuEra's error correction advances bring the reliability threshold closer to the level required for Physical AI systems to depend on quantum computation in safety-critical environments.

Infleqtion (formerly ColdQuanta) operates Sqale, a neutral-atom quantum computer integrated with NVIDIA NVQLink for hybrid quantum-classical acceleration — making it one of the first quantum systems natively coupled to GPU-accelerated classical compute. Infleqtion's cold-atom technology spans quantum computing, quantum sensing, and quantum networking, providing a vertically integrated quantum platform that addresses the full stack from hardware to algorithms. Infleqtion's Barium-133 ion trap technology and neutral-atom arrays deliver high-fidelity gates and long coherence times suited to complex quantum algorithms.

Infleqtion's quantum sensing portfolio — including atomic clocks, quantum gravimeters, and quantum inertial sensors — provides Physical AI systems with navigation and positioning capabilities that operate without GPS, enabling autonomous operation in GPS-denied environments such as underground facilities, deep-sea installations, and contested military zones. Infleqtion's integration with NVIDIA GPU infrastructure allows Physical AI systems to seamlessly invoke quantum compute resources within existing GPU-accelerated AI pipelines, lowering the barrier to quantum-enhanced Physical AI deployment. Infleqtion's quantum networking research supports the development of quantum-secured communication channels for Physical AI systems operating in sensitive industrial and defence environments.

Anyon Systems is Canada's leading superconducting quantum computing company, developing on-premises quantum computers designed for deployment in high-performance computing centres and research institutions. Anyon's Yukon and Helios processors use transmon superconducting qubits with a focus on practical deployment — providing quantum hardware that organisations can operate within their own data centres rather than relying solely on cloud access. Anyon's $23M CAD government-backed programme targets the development of fault-tolerant quantum processors for Canadian industry and defence applications.

Anyon Systems' on-premises deployment model enables Physical AI operators in sensitive industries — defence, critical infrastructure, and advanced manufacturing — to integrate quantum compute directly into their operational environments without data leaving their secure perimeter. Anyon's quantum processors provide Physical AI systems with low-latency quantum optimisation capabilities for real-time production scheduling, logistics coordination, and resource allocation in industrial settings. Anyon's Canadian government partnerships position its technology as a key enabler of sovereign quantum-Physical AI capabilities for national infrastructure and defence applications.

Quantum Circuits Inc. (QCI) was founded by Yale University quantum computing pioneers and developed a modular superconducting quantum architecture with integrated quantum error correction — designed from the ground up for fault-tolerant operation. QCI's unique approach uses microwave photon-based qubit readout and a modular chip architecture that enables scaling without the interconnect bottlenecks that limit monolithic superconducting processors. QCI was acquired by D-Wave in 2025, combining QCI's gate-model error correction expertise with D-Wave's quantum annealing and hybrid solver capabilities to create a comprehensive quantum computing portfolio.

QCI's modular quantum architecture provides Physical AI systems with a scalable quantum compute substrate that can grow with application requirements — from near-term hybrid quantum-classical algorithms to future fault-tolerant quantum computation. QCI's integration into D-Wave's ecosystem connects its gate-model capabilities to D-Wave's Leap quantum cloud platform, enabling Physical AI developers to access both annealing-based optimisation and gate-model quantum computation through a unified API. QCI's Yale heritage and error correction focus ensure that its quantum processors meet the reliability standards that Physical AI systems in industrial and safety-critical applications require.

Quandela is Europe's leading photonic quantum computing company, developing quantum processors based on semiconductor single-photon sources that achieve near-perfect photon indistinguishability — a critical requirement for photonic quantum computation. Quandela's Mosaiq cloud platform provides access to its photonic quantum processors, and its Lucy system was delivered to EuroHPC as part of Europe's quantum computing infrastructure initiative. Quandela's mRIO photonic chip technology enables room-temperature quantum photon generation, reducing the infrastructure requirements for photonic quantum systems compared to cryogenic alternatives.

Quandela's photonic quantum processors are inherently suited to quantum communication and quantum networking applications — enabling Physical AI systems to operate over quantum-secured communication channels that are resistant to classical and quantum eavesdropping. Quandela's Perceval open-source photonic quantum programming framework allows Physical AI developers to build quantum algorithms that run on photonic hardware, enabling quantum-enhanced sensing, imaging, and signal processing for autonomous systems. Quandela's European base and EuroHPC integration make it a key enabler of sovereign quantum-Physical AI capabilities for European industry and critical infrastructure.

ORCA Computing develops photonic quantum processors based on quantum memory and time-bin encoding — a unique approach that uses optical fibre delay lines as quantum memory, enabling photonic quantum computation without the need for perfect single-photon sources. ORCA's PT-Series systems operate at telecom wavelengths, making them directly compatible with existing fibre optic infrastructure and enabling seamless integration with quantum networking applications. ORCA's approach to photonic quantum computing targets near-term quantum advantage in machine learning, optimisation, and simulation tasks that are relevant to Physical AI applications.

ORCA's telecom-wavelength photonic processors enable Physical AI systems to integrate quantum computation directly into fibre optic communication networks — supporting quantum-secured data transmission and quantum-enhanced signal processing for autonomous systems operating across distributed infrastructure. ORCA's quantum machine learning capabilities provide Physical AI agents with quantum-accelerated pattern recognition and anomaly detection for industrial monitoring, predictive maintenance, and quality control applications. ORCA's compact, rack-mounted form factor makes its quantum processors deployable in standard data centre environments alongside the classical compute infrastructure that Physical AI systems depend on.

Pasqal is a leading European neutral-atom quantum computing company, developing processors with up to 1,000 atoms arranged in 2D and 3D arrays using optical tweezers — enabling highly connected quantum processors with programmable geometry. Pasqal delivered Italy's first neutral-atom quantum computer to CINECA as part of the EuroHPC initiative, and its Fresnel processor provides cloud access to its neutral-atom technology. Pasqal's analog quantum simulation capabilities are particularly powerful for materials science, quantum chemistry, and optimisation problems that map naturally onto the spatial structure of neutral-atom arrays.

Pasqal's neutral-atom processors provide Physical AI systems with quantum simulation capabilities for materials discovery, drug design, and chemical process optimisation — enabling autonomous systems to predict material properties and reaction outcomes that would require years of classical computation. Pasqal's graph-based optimisation capabilities — enabled by the programmable connectivity of its neutral-atom arrays — directly address the combinatorial scheduling, routing, and resource allocation problems that Physical AI systems encounter in manufacturing, logistics, and energy management. Pasqal's European deployment at national supercomputing centres positions its quantum processors as accessible infrastructure for Physical AI developers across European industry.

Atom Computing developed the world's first 1,225-qubit quantum computer — a record-breaking neutral-atom system using nuclear-spin qubits in strontium-87 atoms that achieve some of the longest coherence times of any qubit technology. Atom Computing's nuclear-spin approach provides exceptional qubit stability, making it a leading candidate for fault-tolerant quantum computation that can sustain the long quantum circuits required for commercially relevant quantum algorithms. Atom Computing was acquired by Google in 2024, integrating its neutral-atom expertise into Google's quantum computing programme alongside its superconducting Willow processor.

Atom Computing's 1,225-qubit neutral-atom processor provides Physical AI systems with access to the largest publicly announced gate-model quantum computer — enabling quantum algorithms of sufficient depth and width to tackle real-world Physical AI optimisation and simulation problems. Atom Computing's Google integration positions its neutral-atom technology within Google's broader AI and cloud infrastructure, enabling Physical AI developers to access quantum compute alongside Google's classical AI accelerators through a unified cloud platform. Atom Computing's long-coherence nuclear-spin qubits are particularly suited to quantum simulation of physical systems — enabling Physical AI agents to model material behaviour, chemical reactions, and physical processes with quantum accuracy.

IQM Quantum Computers is Europe's leading quantum hardware company, specialising in on-premises superconducting quantum computers designed for integration into high-performance computing centres. IQM has delivered quantum computers to national supercomputing facilities in Finland, Germany, Spain, and Poland — making it the most widely deployed quantum hardware vendor in European HPC infrastructure. IQM's co-design approach — optimising quantum hardware and algorithms together for specific application domains — enables higher effective performance than generic quantum processors for targeted use cases in materials science, chemistry, and optimisation.

IQM's on-premises deployment model enables Physical AI operators in sensitive industries to integrate quantum compute directly into their HPC infrastructure — providing low-latency quantum acceleration for real-time Physical AI decision loops without cloud dependency. IQM's co-design methodology allows Physical AI application developers to work with IQM to optimise quantum algorithms specifically for their use case — whether robotic motion planning, manufacturing process optimisation, or materials discovery — achieving higher quantum advantage than off-the-shelf quantum cloud services. IQM's European HPC integration positions its quantum processors as accessible infrastructure for Physical AI developers across European manufacturing, energy, and defence sectors.

Oxford Quantum Circuits (OQC) developed the Coaxmon — a proprietary 3D superconducting qubit architecture that achieves high connectivity and low crosstalk by routing qubit interactions through a coaxial geometry rather than a flat chip. OQC's Lucy and Toshiko quantum processors are available via OQC Cloud and through AWS Braket, providing enterprise access to its application-optimised quantum compute. OQC's focus on application-specific quantum advantage — rather than raw qubit count — makes its processors particularly effective for near-term quantum algorithms in finance, logistics, and materials science.

OQC's application-optimised quantum processors provide Physical AI systems with quantum compute resources tuned for the specific algorithm classes most relevant to autonomous decision-making — including variational quantum eigensolvers, quantum approximate optimisation, and quantum machine learning. OQC's cloud platform enables Physical AI developers to integrate quantum computation into their AI pipelines via standard REST APIs, lowering the integration barrier for quantum-enhanced Physical AI applications. OQC's Oxford University heritage and UK government backing position it as a key enabler of sovereign quantum-Physical AI capabilities for UK industry, defence, and critical national infrastructure.