What Is Quantum Computing and Why Nations Are Racing to Control It
As French startup Pasqal targets a $2 billion SPAC listing, the global race to achieve quantum advantage intensifies across competing hardware platforms and trillion-dollar applications.
Quantum computing harnesses the counterintuitive properties of quantum mechanics to solve problems that would take classical computers millennia, driving a global race worth billions in national investment and commercial ambition.
French quantum startup Pasqal’s $2 billion SPAC listing, announced this week, underscores the commercial urgency behind a technology that governments and corporations view as strategic infrastructure. China has committed approximately $15 billion to quantum information technologies, while France has pledged $1.8 billion, Germany $3.1 billion, and the UK over $1 billion to their national quantum programmes. The stakes extend beyond laboratory breakthroughs: countries at the forefront of quantum technology will surge forward in practically every other regard—research, manufacturing, transportation, finance, and beyond.
How Quantum Computers Work: Superposition and Entanglement
Quantum Computing harnesses the laws of quantum mechanics to solve problems too complex for classical computers. The fundamental difference lies in the basic unit of information. A traditional bit can be either 0 or 1, while a qubit in a state of superposition does not have a defined value because it holds many potential values at the same time.
Classical computers process information sequentially, evaluating one possibility at a time. According to IBM, quantum computers manipulate information in fundamentally different ways, enabling dramatic speed-ups for certain problem classes—particularly when combined with high-performance classical supercomputers in hybrid workflows.
Because a qubit can be in a superposition of 0 and 1, the quantum computer can perform multiple computations in parallel by processing all possible states of the qubits at once. The computational power scales exponentially: the number of states that a quantum computer can hold is represented by two to the power of qubits, n: 2^n.
Entanglement—the second pillar of quantum computing—creates correlations between qubits that have no classical equivalent. A pair or group of particles is entangled when the quantum state of each particle cannot be described independently of the quantum state of the other particle(s). The outcome of the measurement on one qubit will always be correlated to the measurement on the other qubit, even if the particles are separated from each other by a large distance.
A complete classical description of all the quantum correlations among as many as 300 entangled particles would require more bits than the number of atoms in the visible universe. This exponential information density is precisely what makes quantum computers potentially transformative—and extraordinarily difficult to build.
Competing Quantum Architectures: Superconducting vs Neutral Atom
Unlike classical computers, which share a common silicon-based architecture, quantum systems can be built using fundamentally different physical platforms. The two leading approaches—superconducting qubits and neutral atoms—represent distinct engineering trade-offs.
| Architecture | Advantages | Challenges | Leading Players |
|---|---|---|---|
| Superconducting | Fast gate operations (~microseconds); established fabrication processes | Requires millikelvin temperatures; limited coherence times (~300 μs); scaling difficulties | IBM, Google, Rigetti |
| Neutral Atom | Long coherence times; high scalability (256+ atoms demonstrated); operates at warmer temperatures | Slower gate operations; more recent platform maturity | Pasqal, QuEra, Atom Computing |
Superconducting quantum computers use macroscopic circuits that work as artificial atoms, relying on Josephson junctions that work at very low temperatures near absolute zero, excelling at fast gate operation times but struggling to maintain quantum coherence beyond 300 microseconds.
Neutral atom quantum computing utilizes individual atoms, typically alkali atoms like rubidium or cesium, suspended and isolated in a vacuum and manipulated using precisely targeted laser beams; these atoms are not ionized, meaning they retain all their electrons and do not carry an electric charge, and the quantum states of these neutral atoms, such as their energy levels or the orientation of their spins, serve as the basis for qubits.
One of the most important advantages of neutral atoms is the good scalability compared to other platforms such as ion traps but also superconducting qubits; devices with 100 and more qubits are available already today and these can be scaled up to multiple thousands of qubits using a single trap array or lattice. The atoms are cooled and held in place using laser beams, a process that requires far less cooling than the deep cryogenic temperatures needed for superconducting circuits; while superconducting qubits operate near absolute zero, neutral atom quantum computers use laser cooling techniques to reach only a few microkelvins above absolute zero, meaning that the elaborate and energy-intensive infrastructure necessary to achieve and maintain millikelvin temperatures in superconducting systems is not needed.
Superconducting qubits can calculate particularly fast; trapped ions are exceptionally stable; neutral atoms are especially scalable; and photonic qubits are remarkably well-suited to integration with classical technology and the industrial manufacturing infrastructure, according to the Chicago Quantum Exchange.
Why Nations Are Racing for Quantum Advantage
Quantum advantage—the point at which a quantum computer solves a problem faster than the best classical supercomputer—remains the defining milestone that governments and corporations are pursuing across three high-value application domains.
- Cryptography: Quantum computers threaten current encryption standards while enabling quantum-resistant cryptographic protocols and quantum key distribution
- Drug Discovery: Quantum simulation of molecular interactions could accelerate pharmaceutical R&D timelines and improve treatment efficacy predictions
- Optimization: Financial modeling, supply chain management, and materials science problems involving vast solution spaces
Breaking—and Securing—Encryption
Quantum computing breaks RSA encryption using Shor’s algorithm, posing a significant threat to current public-key systems, driving development of post-quantum cryptography to resist quantum attacks and enabling simulation of quantum systems at the molecular level to discover new drugs and materials. While quantum computers can break current encryption methods like RSA and ECC, they also enable stronger security through quantum-resistant cryptography and unbreakable encryption keys.
Organizations are exploring quantum key distribution (QKD) to create secure communication channels that detect interception attempts and ensure data confidentiality, according to Quantum in South Carolina.
Accelerating Drug Discovery and Materials Science
Quantum approaches to molecular simulation and lead identification and optimization offer exponential advantages over classical methods through native quantum state representation. Google’s collaboration with Boehringer Ingelheim demonstrated quantum simulation of Cytochrome P450, a key human enzyme involved in drug metabolism, with greater efficiency and precision than traditional methods, advances that could significantly accelerate drug development timelines and improve predictions of drug interactions and treatment efficacy.
Developing a new drug costs approximately one to three billion dollars and takes around ten years, with only a ten percent success rate, according to research published in Future Pharmacology. Quantum computing offers a path to reduce both timelines and failure rates by enabling more accurate molecular simulations before physical trials begin.
Financial Modeling and Optimization
Financial services has emerged as an early adopter sector, with JPMorgan Chase partnering with IBM to explore quantum algorithms for option pricing and risk analysis; early studies indicate quantum models could outperform classical Monte Carlo simulations in both speed and scalability, with the financial industry anticipated to become one of the earliest beneficiaries of commercially useful quantum computing.
The Geopolitical Dimension: Investment and Strategic Competition
China’s investment in quantum research and tech is nearly double that of the EU and triple that of the United States, though the U.S. model relies heavily on private-sector R&D that raw government spending figures don’t capture. The U.S. leads in private investment for quantum, and its public funding (while significant) is comparatively smaller because it expects industry to carry a large share of the load; America’s corporate R&D spending contributes heavily to quantum innovation, so raw public funding figures understate total U.S. effort.
The race to lead in quantum technologies is not just about scientific bragging rights; it carries real implications for economic leadership, military strength, cybersecurity, and the ability to set the rules in the emerging quantum era, with common themes of heavy investment, public-private collaboration, and national strategies explicitly linking quantum tech to national power.
While significant challenges remain in scaling systems, improving error rates, and developing applications that reliably outperform classical approaches, the trajectory suggests that meaningful commercial quantum computing applications could emerge within the next five to ten years for specific problem classes in drug discovery, materials science, optimization, and cryptography.
Related Coverage
For the latest developments in quantum computing commercialization, see our coverage of Pasqal’s $2 billion SPAC listing. The quantum race sits within broader geopolitical technology competition detailed in Europe’s defense tech investment surge and connects to computing infrastructure challenges explored in AI’s energy demands and advanced nuclear reactor development. For parallel hardware innovation, see RISC-V’s open architecture momentum and hardware testing infrastructure.