What Makes Qubits Quantum? From Physical to logical Qubits, and from Superposition to Error Correction
- SUPARNA
- Oct 11
- 12 min read
Understanding qubits is essential to grasping why quantum computing represents not just an incremental improvement over classical computing, but a paradigm shift that could transform fields from drug discovery to artificial intelligence. This guide explores what qubits are, how they're built, and why the distinction between physical and logical qubits is crucial to the future of quantum computing.
What Is a Qubit? The Quantum Analog of the Bit
Beyond Binary: The Power of Superposition
A qubit is the quantum analog of a classical bit, but with a crucial difference that gives quantum computers their extraordinary power. While a classical bit can be either 0 or 1—like a light switch that's either off or on—a qubit can exist in a superposition of both states simultaneously.
Think of it this way: a classical bit is like a coin lying flat on a table, showing either heads or tails. A qubit is like a coin spinning in the air—it's simultaneously both heads and tails until you measure it and it "collapses" into one definite state.
Mathematically, a qubit is described as:
|ψ⟩ = α|0⟩ + β|1⟩
Where:
|0⟩ and |1⟩ are the two basis states (like the classical 0 and 1)
α and β are complex numbers called probability amplitudes
|α|² gives the probability of measuring 0
|β|² gives the probability of measuring 1
|α|² + |β|² = 1 (the total probability must equal 100%)
This simple equation encodes profound capabilities. Because α and β can take any values (as long as they satisfy the probability constraint), a single qubit can represent an infinite number of quantum states—though measurement forces it to collapse into either 0 or 1.
Why Does Qubit Matter for Quantum Computing?
Three Quantum Properties That Change Everything
Qubits leverage three fundamental quantum mechanical properties that classical bits cannot:
1. Superposition A qubit can be in multiple states simultaneously, allowing quantum computers to explore many possibilities at once. With n qubits in superposition, you can represent 2ⁿ states simultaneously. Ten qubits can represent 1,024 states at once; 20 qubits can represent over a million states; 300 qubits could represent more states than there are atoms in the observable universe.
2. Entanglement Qubits can become entangled, meaning the state of one qubit instantly influences another, regardless of the distance between them. This correlation is stronger than any classical correlation—measuring one entangled qubit immediately determines properties of its partner, even if they're separated by light-years. Einstein famously called this "spooky action at a distance," but it's a real phenomenon that quantum computers exploit for certain algorithms.
3. Interference Quantum states can interfere with each other, like waves in water. Probability amplitudes can add constructively (amplifying certain outcomes) or destructively (canceling others out). Quantum algorithms cleverly orchestrate these interferences so that wrong answers cancel out while correct answers amplify, increasing the likelihood of measuring the right solution.
Together, these properties enable quantum computers to solve certain problems exponentially faster than classical computers—but only if we can build and control qubits reliably.
Quantum Hardware: How Are Qubits Built?
Qubits aren't abstract mathematical constructs—they're physical systems that must be carefully engineered and controlled. Several different technologies can implement qubits, each with distinct strengths and challenges:
Superconducting Qubits
Physical Basis: Superconducting circuits made from materials like aluminum or niobium, cooled to millikelvin temperatures (a few thousandths of a degree above absolute zero). These circuits exploit Josephson junctions—quantum tunneling devices that allow superconducting current to oscillate between two quantum energy levels.
Key Characteristics:
Coherence Time: 100-500 microseconds
Gate Speed: Fast (20-100 nanoseconds)
Fidelity: >99.5% for single-qubit gates, 98-99.5% for two-qubit gates
Connectivity: Limited to nearest neighbors in most architectures
Error Rate: 0.1-1% per operation
Advantages:
Most mature quantum computing technology
Fast operations enable many gates within coherence time
Leverages semiconductor manufacturing infrastructure
Scalable fabrication processes
Challenges:
Requires extreme cooling (dilution refrigerators)
Relatively short coherence times
Sensitive to electromagnetic noise and cosmic rays
Limited connectivity in 2D chip layouts
Maturity (2025): Most advanced commercial technology, with systems ranging from 20 to over 1,000 qubits deployed and accessible via cloud platforms.
Trapped Ion Qubits
Physical Basis: Individual atoms (typically ions like ytterbium or barium) trapped by electromagnetic fields in ultra-high vacuum and manipulated with precisely controlled laser pulses. The quantum information is stored in the internal energy levels of these ions.
Key Characteristics:
Coherence Time: Milliseconds to seconds (100-1000x longer than superconducting)
Gate Speed: Slower (microseconds to milliseconds)
Fidelity: >99.9% for single-qubit gates, >99% for two-qubit gates (highest available)
Connectivity: All-to-all (any ion can interact with any other through shared motion)
Error Rate: 0.01-0.1% per operation
Advantages:
Highest gate fidelities available
Longest coherence times
Excellent connectivity (all-to-all)
Identical qubits (same isotope atoms are fundamentally identical)
Can be shut down and restarted without recalibration
Challenges:
Slower gate operations limit algorithm complexity
Complex laser systems required for control
Challenging to scale to very large numbers of qubits
Requires ultra-high vacuum systems
Maturity (2025): Advanced technology with commercial systems available, featuring prominently in recent logical qubit breakthroughs due to high fidelities.
Photonic Qubits
Physical Basis: Individual photons (particles of light) where quantum information is encoded in properties like polarization, spatial mode, or time bin. Photons propagate through optical circuits made of waveguides, beam splitters, and phase shifters.
Key Characteristics:
Coherence Time: Effectively unlimited (photons don't decohere easily)
Gate Speed: Very fast (picoseconds)
Fidelity: Varies; probabilistic operations challenging
Connectivity: Flexible through optical routing
Error Rate: Dominated by photon loss and gate success probability
Advantages:
Room temperature or modest cooling operation
Natural compatibility with telecommunications infrastructure
Minimal decoherence during propagation
Fast operations
Potential for quantum communication and networking
Challenges:
Difficult two-qubit gates (photons don't naturally interact)
Photon loss in components
Probabilistic gates require many physical resources
Large footprint for error-corrected systems
Maturity (2025): Rapidly developing with significant investment; commercial systems available via cloud access, though still behind superconducting and trapped ion technologies in maturity.
Neutral Atom Qubits
Physical Basis: Neutral (uncharged) atoms cooled to near absolute zero and held in place by optical tweezers—tightly focused laser beams that trap individual atoms at specific locations. Quantum information is stored in atomic energy levels.
Key Characteristics:
Coherence Time: Milliseconds to seconds
Gate Speed: Moderate (microseconds)
Fidelity: 95-99% (improving rapidly)
Connectivity: Excellent; atoms can be rearranged dynamically
Error Rate: 1-5% per operation (improving)
Advantages:
Excellent scalability (hundreds to thousands of qubits demonstrated)
Flexible connectivity through atom rearrangement
Long coherence times
Reconfigurable layouts for different algorithms
Identical qubits (same isotope atoms)
Challenges:
Lower gate fidelities than trapped ions
Atom loss during operations
Complex optical systems for trapping and control
Requires vacuum systems
Maturity (2025): Emerging technology showing rapid progress, with demonstrations of thousands of physical qubits and 24+ logical qubits. Increasingly competitive with more established technologies.
Topological Qubits
Physical Basis: Theoretical approach that encodes quantum information in exotic quasi-particles called anyons (specifically, Majorana fermions). The quantum information is stored in the global topological properties of the system rather than local properties of individual particles.
Key Characteristics:
Coherence Time: Potentially very long (topologically protected)
Gate Speed: Unknown (still experimental)
Fidelity: Potentially very high (intrinsic error protection)
Connectivity: To be determined
Error Rate: Potentially very low due to topological protection
Advantages:
Inherent error protection from topology
Could require dramatically less error correction overhead
Fundamentally different approach could bypass current limitations
Potentially transformative if realized
Challenges:
Still largely experimental
Creating and controlling anyons is extremely difficult
Requires exotic materials and conditions
No large-scale demonstrations yet
High technical risk
Maturity (2025): Experimental stage. New architectures announced but still years from practical implementation. Represents a high-risk, potentially high-reward approach.
Other Qubit Technologies
Quantum Dot Qubits: Use electron spins confined in semiconductor quantum dots. Advantages include compatibility with existing semiconductor manufacturing and potential for dense integration. Still in early research stages.
Diamond NV Center Qubits: Exploit nitrogen-vacancy defects in diamond crystals. Excellent for quantum sensing applications and can operate at room temperature, but challenging to scale for computation. Niche applications in 2025.
NMR Qubits: Use nuclear spins in molecules. Historically important for early quantum computing research but not scalable due to initialization and measurement challenges. Primarily academic interest in 2025.
Technology Comparison Summary
Each technology is evaluated based on:
Coherence time: How long a qubit maintains its quantum state
Gate fidelity: Accuracy of quantum operations
Connectivity: How easily qubits interact
Error rate: Frequency of errors per operation
Scalability: Potential to reach thousands or millions of qubits
Quantum Hardware Specifications and Benchmarks
Qubit Counts: Quantity vs. Quality
As of 2025, commercial quantum processors range dramatically in qubit count—from systems with 20 high-quality qubits to those with over 1,000 qubits. However, qubit count alone is misleading. A system with 100 high-fidelity, well-connected qubits may outperform one with 1,000 noisy, poorly connected qubits.
Key Point: Raw qubit count matters less than effective computational power, which depends on fidelity, connectivity, and coherence.
Different approaches prioritize different tradeoffs:
Some systems focus on fewer, higher-quality qubits (trapped ion systems typically have 20-50 qubits with >99% fidelity)
Others pursue larger numbers of moderate-quality qubits (superconducting systems can exceed 1,000 qubits with 98-99% fidelity)
Emerging technologies demonstrate thousands of qubits with improving quality (neutral atom systems)
Coherence Times: The Quantum Clock
Coherence time measures how long a qubit maintains its quantum state before decohering—losing its quantum properties and behaving classically. This is perhaps the most critical specification because it limits how many operations you can perform.
Typical Coherence Times by Technology:
Superconducting qubits: 100-500 microseconds
Trapped ion qubits: Milliseconds to seconds (1,000-10,000x longer)
Neutral atom qubits: Milliseconds to seconds
Photonic qubits: Effectively unlimited (decoherence isn't the limiting factor)
Why does this matter? If a single quantum gate takes 100 nanoseconds and your coherence time is 100 microseconds, you can perform roughly 1,000 operations before quantum information is lost. Trapped ions, with coherence times of seconds, could theoretically perform millions of operations—though their slower gate speeds mean the practical advantage is smaller than the coherence time ratio suggests.
Error Rates and Gate Fidelities
Gate fidelity measures how accurately a quantum operation is performed. A fidelity of 99% means that 1% of the time, the operation fails or introduces errors.
Current State-of-the-Art Fidelities:
Single-qubit gates: 99.5-99.99% (depending on technology)
Two-qubit gates: 98-99.5% (more challenging due to qubit interactions)
Trapped ion systems currently achieve the highest fidelities:
99.9% for single-qubit gates
99% for two-qubit gates
Superconducting systems are close behind:
99.5% for single-qubit gates
98-99.5% for two-qubit gates
Why do these small differences matter? Error rates compound. Run 1,000 operations at 99% fidelity, and errors accumulate significantly. At 99.9% fidelity, the error accumulation is 10x less severe—a critical difference for running deep quantum algorithms.
Benchmarking Quantum Computers
Comparing quantum computers is challenging because performance depends on multiple factors. Several benchmark metrics have been developed:
1. Quantum Volume (QV) A holistic metric that combines qubit count, gate fidelity, connectivity, and cross-talk. Quantum Volume = 2ⁿ where n is the depth of the largest random quantum circuit the system can reliably execute.
Higher is better
Accounts for quality, not just quantity
Systems in 2025 range from QV of 32 to over 1,000
2. Algorithmic Qubits (#AQ) Some companies use this metric to represent the number of qubits that can be reliably used for algorithms, accounting for fidelity and connectivity. A system with 100 physical qubits might have #AQ 35 if only 35 qubits meet certain quality thresholds for algorithm execution.
3. Circuit Layer Operations Per Second (CLOPS) Measures the speed at which quantum circuits can be executed, accounting for classical control overhead. Important for iterative quantum-classical algorithms.
4. Logical Qubits (Future-Looking) Increasingly, the field is moving toward logical qubit count as the key metric—the number of error-corrected, reliable qubits available for computation. This is discussed in detail below.
Physical vs. Logical Qubits: The Heart of Scalable Quantum Computing
Understanding the distinction between physical and logical qubits is crucial to grasping where quantum computing is today and where it's heading.
Physical Qubits: The Raw Hardware
Physical qubits are the actual hardware elements that store quantum information—a superconducting circuit, a trapped ion, a neutral atom, or a photon. These are the "real" qubits you build in a laboratory.
The Problem with Physical Qubits: Physical qubits are fragile and error-prone. Even the best systems have error rates between 0.01% and 1% per operation. This means:
Run 1,000 operations at 0.1% error rate → expect ~1 error
Run 10,000 operations at 0.1% error rate → expect ~10 errors
Run 100,000 operations at 0.1% error rate → expect ~100 errors
For simple quantum circuits with a few dozen operations, you might get away with these error rates. But many useful quantum algorithms require hundreds of thousands to millions of operations. At current physical error rates, the accumulated errors would render the computation meaningless—like trying to complete a 10,000-piece jigsaw puzzle where 1% of the pieces are the wrong shape.
Why Physical Qubits Have Errors:
Decoherence: Environmental interactions destroy quantum states
Gate imperfections: Quantum operations aren't perfectly precise
Measurement errors: Reading qubit states isn't 100% accurate
Cross-talk: Operations on one qubit accidentally affect neighbors
Control noise: Imperfect signals from classical control systems
Logical Qubits: Error-Protected Information
Logical qubits are abstract, error-protected qubits formed by encoding information across many physical qubits using quantum error correction (QEC). Think of a logical qubit as a single, high-quality qubit built from multiple noisy physical qubits working together.
The Key Idea: Just as you can transmit messages reliably over noisy channels using error correction codes (adding redundancy so errors can be detected and corrected), you can store and process quantum information reliably by spreading it across multiple physical qubits.
How Logical Qubits Work:
Encoding: Quantum information from one ideal qubit is encoded across many physical qubits using an error correction code
Syndrome Measurement: The system continuously monitors for errors by measuring "syndrome" information—detecting whether errors occurred without disturbing the encoded quantum state
Error Correction: When errors are detected, corrections are applied in real-time to restore the correct quantum state
Computation: Quantum operations (gates) are performed on the encoded logical qubit, which requires orchestrating operations across all the physical qubits
The Beautiful Result: A logical qubit can have an error rate dramatically lower than any of its constituent physical qubits. Recent breakthroughs have demonstrated logical qubits with error rates 800 times better than the underlying physical qubits—turning 0.8% error rates into 0.001% error rates.
What happens when errors accumulate faster than they can be corrected, and how do researchers overcome this fundamental challenge?
Why Logical Qubits Matter
Quantum algorithms for real-world applications require hundreds to thousands of logical qubits, each capable of participating in millions of operations with extremely low error rates.
Here's why:
Drug Discovery: Simulating a complex molecule might require 100-1,000 logical qubits and millions of quantum operations
Optimization: Solving large-scale logistics problems could need 500-2,000 logical qubits running deep algorithms
Cryptography: Breaking RSA encryption with Shor's algorithm would require thousands of logical qubits and billions of operations
Current Reality: Most systems in 2025 operate primarily with physical qubits, with only experimental logical qubit demonstrations reaching 24-28 logical qubits.
The Goal: Scaling to hundreds or thousands of logical qubits within the next 5-7 years, enabling fault-tolerant quantum computing that can tackle these transformative applications.
Recent Breakthroughs: New decoder architectures have achieved 5x-10x reductions in computational requirements while maintaining accuracy. These efficient decoders can fit on field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs)—making real-time decoding practical for large-scale systems without requiring massive supercomputers.
Conclusion: Why the Physical-to-Logical Transition Matters
The distinction between physical and logical qubits is not just technical jargon—it represents the fundamental difference between experimental quantum computing and practical, scalable quantum computing that can solve real-world problems.
Physical qubits gave us the foundation: proving quantum computing is possible, demonstrating quantum algorithms, and achieving early quantum advantages for specific tasks. They're the building blocks, but they're too fragile and error-prone for most transformative applications.
Logical qubits are the path forward: enabling the deep, complex quantum computations required for drug discovery, materials science, optimization, and artificial intelligence. They transform quantum computing from a fragile laboratory curiosity into a reliable industrial tool.
The 2024-2025 shift from targeting physical qubits to targeting logical qubits marks a pivotal moment. For the first time, quantum error correction isn't just theoretical—it's being demonstrated at increasing scale, with error rates reduced by factors of hundreds.
As we look ahead, success won't be measured by how many physical qubits a system has, but by how many reliable, error-corrected logical qubits it can deploy. That's the metric that matters for solving humanity's most complex computational challenges.
The quantum revolution is built on qubits—both the physical quantum systems we engineer and the logical quantum information we protect. Understanding both is essential to understanding where quantum computing is going and why it matters.
Will the transition from physical to logical qubits finally unlock quantum computing's transformative potential, or do other challenges remain?
References and Further Reading:
Quantum Computing Fundamentals
Nielsen, M. A., & Chuang, I. L. (2010). Quantum Computation and Quantum Information. Cambridge University Press.
Preskill, J. (2018). Quantum Computing in the NISQ era and beyond. Quantum, 2, 79.
Qubit Technologies and Implementations
Arute, F., et al. (2019). Quantum supremacy using a programmable superconducting processor. Nature, 574, 505-510.
Bruzewicz, C. D., Chiaverini, J., McConnell, R., & Sage, J. M. (2019). Trapped-ion quantum computing: Progress and challenges. Applied Physics Reviews, 6(2), 021314.
Madsen, L. S., et al. (2022). Quantum computational advantage with a programmable photonic processor. Nature, 606, 75-81.
Ebadi, S., et al. (2021). Quantum phases of matter on a 256-atom programmable quantum simulator. Nature, 595, 227-232.
Quantum Benchmarking
Cross, A. W., Bishop, L. S., Sheldon, S., Nation, P. D., & Gambetta, J. M. (2019). Validating quantum computers using randomized model circuits. Physical Review A, 100(3), 032328.
Lubinski, T., et al. (2023). Application-oriented performance benchmarks for quantum computing. IEEE Transactions on Quantum Engineering, 4, 1-32.
Industry Reports and Technical Documentation
McKinsey & Company (2024). Quantum technology monitor. McKinsey Digital.
Boston Consulting Group (2024). The Next Decade in Quantum Computing—and How to Play. BCG Technology Report.
Quantum Economic Development Consortium (2024). The Quantum Industry Landscape Report.
Quantum Applications
Bauer, B., et al. (2020). Quantum algorithms for quantum chemistry and quantum materials science. Chemical Reviews, 120(22), 12685-12717.
Cao, Y., et al. (2019). Quantum chemistry in the age of quantum computing. Chemical Reviews, 119(19), 10856-10915.
Biamonte, J., et al. (2017). Quantum machine learning. Nature, 549, 195-202.
Recent Developments (2024-2025)
Various research publications on logical qubit demonstrations achieving 800x error rate improvements (April-December 2024)
Technical reports on quantum Low-Density Parity-Check codes and decoder architectures (2024-2025)
Industry roadmaps and quantum computing platform documentation from major quantum computing organizations (2024-2025)
Online Resources
Quantum computing platforms and cloud services documentation (accessed 2024-2025)
arXiv preprint server - quantum physics section (quant-ph) for latest research developments
Nature Quantum Information journal - various articles on quantum error correction and logical qubits (2023-2025)
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