Quantum Computing: Companies and Tools
The quantum computing landscape has exploded in recent years, with major tech companies, well-funded startups, and research institutions all racing to build practical quantum computers. This article provides an overview of the major players, their approaches, and the tools they offer.
For detailed information on specific platforms and getting started, see:
- Quantum Computing - Cloud Platforms - Detailed guide to IBM Quantum, Azure Quantum, Amazon Braket, and more
- Quantum Computing - Programming Frameworks - Qiskit, Cirq, Q#, PennyLane, and other frameworks
- Quantum Computing - Getting Started - Practical guide to beginning your quantum computing journey
For information on hardware implementations, see Series 2: Hardware in the Quantum Computing Series Index.
Major Technology Companies
IBM Quantum
Approach: Superconducting qubits
Key Details:
- One of the longest-running quantum computing programs
- Offers cloud access through IBM Quantum Network
- Has built systems ranging from 5 qubits to over 1,000 qubits
- Provides Qiskit, an open-source quantum software development framework
- Regular roadmap updates showing progress toward fault-tolerant quantum computing
Tools and Platforms:
- IBM Quantum Network - Cloud platform for accessing quantum hardware
- Qiskit - Python-based quantum computing framework
- IBM Quantum Composer - Visual drag-and-drop quantum circuit builder
- IBM Quantum Lab - Jupyter notebook environment for quantum development
Current Status: IBM has been a leader in making quantum computing accessible. They've built systems with over 1,000 qubits and continue to improve error rates and coherence times.
Google Quantum AI
Approach: Superconducting qubits
Key Details:
- Claimed "quantum supremacy" in 2019 with their Sycamore processor
- Focuses on both hardware and algorithm development
- Part of Alphabet's research division
- Strong emphasis on quantum error correction research
Tools and Platforms:
- Cirq - Python framework for creating, editing, and invoking quantum circuits
- TensorFlow Quantum - Quantum machine learning library
- OpenFermion - Platform for quantum chemistry algorithms
- Quantum AI - Research platform (limited public access)
Current Status: Google continues to push the boundaries of quantum computing, with ongoing research into error correction and larger-scale systems.
Microsoft Azure Quantum
Approach: Multiple (including topological qubits)
Key Details:
- Pursuing topological qubits (still experimental) for better error rates
- Also partners with other quantum hardware providers
- Strong focus on quantum software and algorithms
- Integrated into Azure cloud platform
Tools and Platforms:
- Azure Quantum - Cloud platform with access to multiple quantum hardware providers
- Q# - Microsoft's quantum programming language
- Quantum Development Kit - Full development environment
- Azure Quantum Credits - Free credits for quantum computing
Current Status: Microsoft's topological qubit approach is still in research, but they provide access to other quantum systems through Azure Quantum.
Amazon Braket
Approach: Multi-provider platform
Key Details:
- Amazon's quantum computing service
- Provides access to quantum hardware from multiple providers
- Includes simulators for development and testing
- Integrated with AWS ecosystem
Tools and Platforms:
- Amazon Braket - Managed quantum computing service
- Braket SDK - Python SDK for quantum computing
- Braket Notebooks - Jupyter notebook environment
- Access to multiple hardware providers - IonQ, Rigetti, OQC, QuEra
Current Status: Amazon focuses on being a platform provider, giving users access to various quantum hardware through a unified interface.
Specialized Quantum Computing Companies
IonQ
Approach: Trapped ion qubits
Key Details:
- Publicly traded quantum computing company
- Uses trapped ytterbium ions as qubits
- Claims highest gate fidelities in the industry
- Focuses on high-quality qubits over quantity
Tools and Platforms:
- IonQ Cloud - Cloud access to trapped ion quantum computers
- IonQ API - Programmatic access to quantum hardware
- Available through - Amazon Braket, Microsoft Azure Quantum, Google Cloud
Current Status: IonQ has built systems with 20+ qubits and continues to scale. Their trapped ion approach offers longer coherence times and lower error rates.
Rigetti Computing
Approach: Superconducting qubits
Key Details:
- Publicly traded quantum computing company
- Focuses on full-stack quantum computing
- Develops both hardware and software
- Strong emphasis on hybrid quantum-classical algorithms
Tools and Platforms:
- Rigetti Quantum Cloud Services - Cloud platform
- Forest SDK - Python framework for quantum programming
- Quil - Quantum instruction language
- Available through - Amazon Braket
Current Status: Rigetti has built systems with 80+ qubits and continues to improve their superconducting qubit technology.
D-Wave Systems
Approach: Quantum annealing (specialized for optimization)
Key Details:
- Pioneered commercial quantum computing
- Uses quantum annealing rather than gate-based quantum computing
- Specialized for optimization problems
- Has systems with thousands of qubits (though not directly comparable to gate-based qubits)
- Implements programmable spin glass Hamiltonians using superconducting qubits
How Quantum Annealing Works: D-Wave's quantum annealers use quantum tunneling to find the ground state of optimization problems. The system evolves through a time-dependent Hamiltonian that gradually transforms from a simple initial state to the problem Hamiltonian. This allows quantum fluctuations to tunnel through energy barriers, potentially finding lower-energy states than classical simulated annealing. The problems are encoded as Ising spin glass Hamiltonians, making D-Wave processors essentially programmable spin glass simulators. For more on the connection to spin glass physics, see Quantum Computing - Hybrid Approaches.
Tools and Platforms:
- Leap - Cloud platform for quantum annealing
- Ocean SDK - Python framework for quantum annealing
- D-Wave Advantage - Latest quantum annealing system with 5000+ qubits
- Problem embedding tools - Help map optimization problems to Ising form
Current Status: D-Wave has been commercial for years, focusing on optimization problems rather than general-purpose quantum computing. They've demonstrated coherent quantum dynamics on large-scale spin glass systems, showing potential advantages over classical methods for certain problem classes.
Xanadu
Approach: Photonic quantum computing
Key Details:
- Uses photons (light particles) as qubits
- Focuses on quantum machine learning and optimization
- Open-source software approach
- Based in Canada
Tools and Platforms:
- PennyLane - Open-source quantum machine learning library
- Strawberry Fields - Photonic quantum computing framework
- Xanadu Cloud - Cloud access to photonic quantum computers
Current Status: Xanadu has built photonic quantum computers and continues to develop both hardware and software for quantum machine learning.
PsiQuantum
Approach: Photonic quantum computing
Key Details:
- Well-funded startup (raised over $600M)
- Building large-scale photonic quantum computers
- Focuses on fault-tolerant quantum computing
- Less public about current capabilities
Tools and Platforms:
- Limited public access currently
- Focused on building large-scale systems
Current Status: PsiQuantum is building toward million-qubit systems for fault-tolerant computing, but details are limited.
Quantinuum (formerly Honeywell Quantum Solutions)
Approach: Trapped ion qubits
Key Details:
- Formed from merger of Honeywell Quantum Solutions and Cambridge Quantum
- Uses trapped ion technology
- Strong focus on quantum chemistry and optimization
- Integrated hardware and software approach
Tools and Platforms:
- Quantinuum H-Series - Trapped ion quantum computers
- TKET - Quantum software development kit
- Quantum Origin - Quantum-enhanced random number generation
- Available through - Microsoft Azure Quantum
Current Status: Quantinuum has built systems with 20+ qubits and continues to scale, with strong performance in quantum chemistry applications.
Atom Computing
Approach: Neutral atom qubits
Key Details:
- Uses neutral atoms (strontium) trapped in optical tweezers
- Claims good scalability potential
- Focuses on both gate-based and analog quantum computing
Tools and Platforms:
- Limited public access currently
- Developing cloud access platform
Current Status: Atom Computing has demonstrated systems with 100+ qubits and continues to develop their neutral atom approach.
ColdQuanta
Approach: Neutral atom qubits
Key Details:
- Uses cold atoms as qubits
- Focuses on both quantum computing and quantum sensing
- Developing cloud-accessible quantum computers
Tools and Platforms:
- Hilbert - Quantum computing platform (in development)
- Available through - Amazon Braket (planned)
Current Status: ColdQuanta is developing neutral atom quantum computers and quantum sensing technologies.
Extropic
Approach: Thermodynamic computing (probabilistic circuits)
Key Details:
- Pioneering thermodynamic computing using probabilistic circuits
- Uses energy-based models to define probability distributions
- Hardware directly samples from programmable energy landscapes
- Focuses on energy efficiency and generative AI applications
- Implements spin glass-like Hamiltonians in hardware
How Thermodynamic Computing Works: Extropic's Thermodynamic Sampling Units (TSUs) are probabilistic circuits that sample from energy-based models. Instead of using quantum effects, they leverage physical evolution toward low-energy states. The hardware encodes energy functions (similar to spin glass Hamiltonians) and naturally evolves toward configurations that minimize energy. This approach is particularly suited for generative AI, where sampling from learned probability distributions is essential. The connection to spin glass physics is direct: TSUs can encode spin glass Hamiltonians and sample from their equilibrium distributions. For more details, see Quantum Computing - Hybrid Approaches.
Tools and Platforms:
- Thermodynamic Sampling Units (TSUs) - Hardware for probabilistic sampling
- Energy-based model framework - Tools for programming energy landscapes
- Limited public access currently (early commercial stage)
Current Status: Extropic is in early commercial stages, developing hardware that can efficiently sample from complex probability distributions. Their approach offers potential energy efficiency advantages for generative AI and optimization problems.
Open-Source Tools and Frameworks
Qiskit (IBM)
- Python-based quantum computing framework
- Extensive documentation and tutorials
- Active community
- Works with IBM Quantum hardware and simulators
Cirq (Google)
- Python framework for quantum circuits
- Designed for near-term quantum devices
- Good for algorithm development
PennyLane (Xanadu)
- Quantum machine learning library
- Hardware-agnostic
- Works with multiple quantum backends
- Strong focus on differentiable quantum circuits
Q# (Microsoft)
- Domain-specific quantum programming language
- Integrated development environment
- Good for algorithm development and simulation
Forest SDK (Rigetti)
- Python framework for quantum programming
- Works with Rigetti hardware
- Includes quantum simulators
Cloud Platforms Summary
Most quantum computing companies offer cloud access to their hardware:
- IBM Quantum Network - Access to IBM's quantum computers
- Amazon Braket - Multi-provider platform (IonQ, Rigetti, OQC, QuEra)
- Microsoft Azure Quantum - Multi-provider platform (IonQ, Quantinuum, others)
- Google Cloud - Limited quantum computing access
- D-Wave Leap - Quantum annealing systems
Choosing a Platform
When choosing a quantum computing platform, consider:
- Problem Type - Optimization (D-Wave) vs. general quantum computing
- Qubit Count - How many qubits you need
- Error Rates - Quality of qubits (IonQ, Quantinuum often lead)
- Software Ecosystem - Available tools and frameworks
- Cost - Pricing models vary significantly
- Accessibility - Ease of use and documentation
The Competitive Landscape
The quantum computing industry is highly competitive, with companies pursuing different strategies:
- Full-Stack Approach - IBM, Google, Rigetti (hardware + software)
- Hardware Specialists - IonQ, Quantinuum (focus on qubit quality)
- Platform Providers - Amazon, Microsoft (access to multiple providers)
- Specialized Applications - D-Wave (optimization), Xanadu (quantum ML)
- Research Focus - Many startups still in R&D phase
Future Outlook
The quantum computing industry is rapidly evolving. Expect to see:
- Continued improvements in qubit count and quality
- More cloud-accessible platforms
- Better software tools and frameworks
- Lower costs and easier access
- More practical applications emerging
Many companies are still private or in early stages, so this landscape will continue to change as the technology matures and more companies enter the market.
Getting Started
To get started with quantum computing:
- Learn the Basics - Understand quantum mechanics fundamentals
- Choose a Framework - Start with Qiskit or PennyLane (good documentation)
- Use Simulators - Practice with quantum simulators before using real hardware
- Try Cloud Platforms - Many offer free credits for beginners
- Join Communities - Engage with quantum computing communities online
For an overview of quantum computing concepts, see Quantum Computing - Overview.
For information about hybrid approaches including quantum annealing and thermodynamic computing, see:
- Quantum Computing - Hybrid Approaches - Quantum annealing, thermodynamic computing, and connections to spin glass physics
Learning Resources
Platform-Specific Learning
IBM Quantum:
- IBM Quantum Learning - Comprehensive courses from basics to advanced: learning.quantum.ibm.com
- Qiskit Textbook - Interactive textbook with code examples: qiskit.org/textbook
- Qiskit YouTube Channel - Official tutorials and quantum computing content: youtube.com/@qiskit
Google Quantum AI:
- Cirq Tutorials - Learn Google's quantum computing framework: quantumai.google/cirq
- TensorFlow Quantum - Quantum machine learning tutorials: tensorflow.org/quantum
Microsoft Azure Quantum:
- Quantum Development Kit - Learn Q# programming language: learn.microsoft.com/azure/quantum
- Q# Documentation - Comprehensive Q# language reference: learn.microsoft.com/azure/quantum
D-Wave:
- D-Wave Learning Resources - Quantum annealing tutorials: docs.dwavesys.com
- Ocean SDK - Python framework for quantum annealing: docs.ocean.dwavesys.com
Xanadu:
- PennyLane Tutorials - Quantum machine learning with PennyLane: pennylane.ai/qml
- Strawberry Fields - Photonic quantum computing tutorials: strawberryfields.ai
General Quantum Computing Courses
See the comprehensive list of courses and resources in Quantum Computing - Overview#Learning Resources.