5 Things Quantum Computers Can Actually Do Right Now

Quantum computing has been the subject of breathless headlines for years, promising to revolutionize everything from drug discovery to artificial intelligence. But separating quantum reality from quantum hype can be challenging, especially when explanations quickly devolve into discussions of superposition, entanglement, and other quantum phenomena that seem lifted straight from science fiction.

While we’re still in the early days of quantum computing—what many experts call the “NISQ era” (Noisy Intermediate-Scale Quantum)—these machines aren’t just theoretical curiosities. They’re already doing meaningful work in specific domains, albeit with significant limitations.

Let’s cut through the quantum mystique and look at five things quantum computers can actually do today, the real-world impact of these applications, and what this tells us about the future of this revolutionary technology.

1. Number Factoring: The Cryptography Game-Changer

When most people think of practical quantum computing applications, cryptography is usually first to mind, thanks largely to Shor’s algorithm. This quantum algorithm, developed by mathematician Peter Shor in 1994, can factor large numbers exponentially faster than the best known classical algorithms.

How It Works

The security of widely-used RSA encryption relies on the fact that while multiplying two large prime numbers is computationally simple, working backward to determine those prime factors from their product is extraordinarily difficult for classical computers. Shor’s algorithm leverages quantum properties to turn this hard problem into a much easier one, potentially breaking encryption that would take classical supercomputers millennia to crack.

Current State

While the full implementation of Shor’s algorithm would require fault-tolerant quantum computers with millions of qubits (we currently have machines with a few hundred at most), researchers have demonstrated the algorithm’s principles on small numbers. In 2019, researchers successfully factored 21 into 3 and 7 using a quantum computer—a trivial problem mathematically, but an important proof of concept.

Real-World Impact

The potential to break current encryption standards has already spurred significant investment in quantum-resistant cryptography. The National Institute of Standards and Technology (NIST) has been running a competition since 2016 to develop and standardize post-quantum cryptographic algorithms that can withstand attacks from quantum computers.

As cybersecurity expert Bruce Schneier puts it: “When quantum computers come, all secrets encrypted with RSA will be revealed. It’s not a matter of if, but when.”

This looming “Q-Day” (when quantum computers become powerful enough to break current encryption) is driving organizations to adopt “crypto agility”—the ability to rapidly switch cryptographic algorithms without major system overhauls.

Financial institutions and government agencies are particularly invested in this transition. As one cybersecurity executive at a major bank told me, “We’re storing financial data that needs to remain secure for decades. If we encrypt it with algorithms that quantum computers might break in 10-15 years, we’re already vulnerable today.”

2. Quantum Simulation: Understanding the Microscopic World

Perhaps the most natural application for quantum computers is simulating other quantum systems—something classical computers struggle with fundamentally.

How It Works

Quantum systems grow exponentially more complex with each particle added. To fully describe the quantum state of just 50 particles would require more classical bits than atoms in the observable universe. Quantum computers, however, can represent these states naturally using qubits, making them ideal for modeling molecular and atomic behavior.

Current State

Today’s quantum computers can simulate simple molecules and chemical reactions. IBM’s quantum computers have modeled the energy states of molecules like lithium hydride and beryllium hydride. Google and Harvard researchers recently used a quantum computer to simulate a simplified version of a chemical reaction.

D-Wave’s quantum annealers—a specialized type of quantum computer—have been used to simulate aspects of condensed matter physics that are challenging for classical methods.

Real-World Impact

These capabilities are already revolutionizing materials science and chemistry. Quantum simulations help researchers understand molecular interactions at a fundamental level, potentially accelerating the discovery of new materials and chemical processes.

“We’re using quantum simulations to explore catalyst designs that could make carbon capture more efficient,” explains a researcher at a major chemical company. “Problems that would take us years to solve classically can potentially be addressed in days with quantum approaches.”

Pharmaceutical companies are also watching this space closely. Understanding protein folding and drug-molecule interactions at the quantum level could dramatically accelerate drug discovery. Quantum simulation startup QSimulate has partnered with several pharmaceutical giants to apply their algorithms to drug development challenges.

As one pharmaceutical researcher noted, “Each new drug typically costs over $2 billion and takes 10+ years to develop. If quantum computing can shave even a year off that timeline through better simulations, the impact would be enormous.”

3. Optimization Problems: Finding the Best Solution

Many real-world problems involve finding the optimal solution among countless possibilities—the perfect route for delivery trucks, the ideal portfolio allocation, or the most efficient manufacturing schedule. Quantum computers offer new approaches to these optimization challenges.

How It Works

Quantum computers can potentially evaluate many possible solutions simultaneously through quantum superposition, and algorithms like Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing can efficiently navigate complex solution landscapes to find optimal or near-optimal answers.

Current State

D-Wave’s quantum annealers have been solving optimization problems commercially for several years. Volkswagen used D-Wave systems to optimize traffic flow in Beijing. Telecommunications companies have applied quantum optimization to network configuration problems.

More recently, neutral-atom quantum computers from companies like QuEra have demonstrated promising results on certain classes of optimization problems.

These applications typically involve hybrid approaches that combine quantum and classical computing rather than pure quantum solutions.

Real-World Impact

The logistics industry stands to benefit enormously from better optimization. A senior logistics executive explained to me, “A 5% improvement in route optimization across our fleet would save millions in fuel costs and reduce our carbon footprint substantially.” Quantum approaches are already being tested for such applications.

Financial services firms are exploring quantum optimization for portfolio management and risk analysis. Multiverse Computing, a quantum software company, has worked with banking partners to develop quantum algorithms for financial optimization problems.

Manufacturing companies are investigating quantum-enhanced scheduling. “In complex manufacturing environments with thousands of interdependent processes, optimal scheduling can increase throughput by 15-20%,” notes an operations researcher at a major manufacturer. “That’s worth billions annually across the industry.”

4. Unstructured Search: Finding Needles in Haystacks

Searching through unstructured data—from finding specific database entries to identifying patterns in large datasets—is fundamental to computing. Quantum computers offer a quadratic speedup through Grover’s algorithm.

How It Works

Grover’s algorithm, developed by Lov Grover in 1996, provides a quadratic speedup for searching unsorted databases. While a classical computer might need to check an average of N/2 items in a database of size N to find a specific entry, Grover’s algorithm can do it in roughly √N steps.

This speedup is less dramatic than Shor’s algorithm’s exponential advantage, but still significant for large datasets.

Current State

Grover’s algorithm has been demonstrated on small datasets using current quantum computers. While these demonstrations are not yet practical for real-world applications, they prove the algorithm works as theorized.

Researchers have also developed variants of Grover’s algorithm for specific search problems, some of which have been tested on quantum hardware.

Real-World Impact

While full-scale implementations remain future technology, components of quantum search algorithms are already finding practical applications in database query optimization and pattern recognition problems.

“We’re implementing quantum-inspired search algorithms in our classical systems today,” explains a database architect at a major technology company. “The insights from quantum approaches are helping us rethink how we structure and query data, giving us performance improvements even before quantum hardware reaches maturity.”

The cybersecurity industry is particularly interested in quantum search capabilities. As one security researcher notes, “Identifying vulnerabilities in complex systems is fundamentally a search problem. Quantum advantages here could transform both offensive and defensive security operations.”

Healthcare researchers see potential in using quantum search algorithms to identify patterns in genomic data. “Searching through billions of genetic variations to find those associated with disease is computationally intensive,” explains a bioinformatics researcher. “Even modest quantum speedups could accelerate breakthrough discoveries.”

5. Solving Linear Systems: Quantum Linear Algebra

Many scientific and engineering problems involve solving large systems of linear equations. The Quantum Linear Systems Algorithm (QLSA), also known as the HHL algorithm after its creators Harrow, Hassidim, and Lloyd, offers exponential speedup for this task.

How It Works

The HHL algorithm can solve certain linear systems exponentially faster than classical methods. For a system with N variables, classical algorithms typically require time proportional to N, while HHL can potentially solve it in time logarithmic in N—an exponential speedup.

The catch? The algorithm provides the solution in quantum form, and extracting all the solution data classically eliminates the speedup. However, for applications that only need specific properties of the solution rather than the entire solution vector, the algorithm maintains its advantage.

Current State

The HHL algorithm has been demonstrated on small systems using current quantum computers. IBM researchers successfully implemented it on a 4-qubit system to solve 2×2 linear systems.

Researchers have also developed specialized variants of the algorithm for applications like quantum machine learning and differential equation solving.

Real-World Impact

The financial industry is exploring QLSA for risk analysis and portfolio optimization. These applications often involve solving large systems of linear equations, and even partial quantum advantages could provide significant benefits.

A quantitative analyst at a major investment firm explains, “We’re not waiting for fault-tolerant quantum computers to start integrating quantum approaches. We’re implementing hybrid quantum-classical algorithms today that give us incremental advantages while positioning us for larger quantum speedups in the future.”

Engineering firms are investigating quantum linear algebra for fluid dynamics simulations. “Modeling complex fluid flows requires solving enormous systems of linear equations,” notes an aerospace engineer. “Quantum methods could eventually allow us to simulate scenarios that are computationally prohibitive today, leading to better aircraft, vehicle, and wind turbine designs.”

Beyond the NISQ Era: What’s Coming Next?

The five applications above represent what quantum computers can do today or in the very near future. But quantum computing is advancing rapidly, with both hardware and algorithm development progressing at an accelerating pace.

Quantum Machine Learning

Quantum versions of machine learning algorithms show promise for specific problems. While quantum computers won’t replace classical GPUs for training large language models anytime soon, they may offer advantages for specialized machine learning tasks.

Researchers have demonstrated quantum neural networks that could potentially recognize patterns classical algorithms miss. Companies like Xanadu are developing photonic quantum computers specifically optimized for machine learning workloads.

Quantum Error Correction

Perhaps the most significant near-term milestone will be achieving practical quantum error correction—the ability to create logical qubits that remain coherent much longer than their physical counterparts by distributing quantum information across multiple physical qubits.

“Quantum error correction is the key that unlocks the full potential of quantum computing,” explains a quantum physicist at a major research institution. “Once we can reliably implement it at scale, many of the current limitations of quantum computers will fall away.”

Quantum Networking

Quantum networks that can distribute quantum information between distant quantum computers are also under active development. Such networks could enable distributed quantum computing and secure quantum communication channels.

China has already launched quantum communication satellites, and quantum network testbeds are operating in the US, Europe, and Asia.

The Quantum Landscape: Major Players and Approaches

The quantum computing landscape features multiple competing technologies, each with unique strengths and challenges:

Superconducting Qubits

Key Players: IBM, Google, Rigetti Advantages: Relatively mature technology, strong industry support Challenges: Requires extremely low temperatures, limited qubit connectivity

IBM’s quantum roadmap calls for a 1,121-qubit machine by the end of 2023, with plans for more sophisticated systems in subsequent years.

Trapped Ions

Key Players: IonQ, Quantinuum Advantages: Excellent qubit quality and coherence times Challenges: Slower gate operations, scaling challenges

IonQ’s systems have demonstrated the highest quantum volume (a measure of quantum computer capability) among current technologies.

Silicon Spin Qubits

Key Players: Intel, Silicon Quantum Computing Advantages: Potential for manufacturing using modified semiconductor processes Challenges: Still relatively early in development

“Silicon spin qubits could potentially leapfrog other approaches in scalability,” suggests a semiconductor industry analyst. “The ability to leverage existing manufacturing infrastructure would be a game-changer.”

Photonic Quantum Computing

Key Players: Xanadu, PsiQuantum Advantages: Can operate at room temperature, natural connectivity Challenges: Generating and detecting single photons reliably

PsiQuantum is pursuing an ambitious goal of building a million-qubit fault-tolerant quantum computer using photonic technology.

Neutral Atoms

Key Players: QuEra, Pasqal Advantages: Naturally scalable to large qubit counts, good coherence Challenges: Gate implementation complexity

QuEra has already demonstrated systems with over 250 qubits, though with limited gate capabilities.

The Practical Path Forward: Quantum’s Business Impact

For businesses and organizations evaluating quantum computing, the key is developing a practical strategy that balances near-term opportunities with long-term preparations.

Quantum Readiness Assessment

Organizations should start by identifying problems within their operations that align well with quantum capabilities—optimization challenges, simulation needs, or data analysis bottlenecks.

“We conducted a quantum opportunity assessment across our business units,” explains the CTO of a Fortune 500 company. “We identified three high-value use cases where quantum computing could provide a competitive advantage and built a roadmap for each, including intermediate steps using quantum-inspired classical algorithms.”

Building Quantum Literacy

Companies serious about quantum computing are investing in training existing staff and recruiting quantum talent. The quantum workforce shortage is a significant challenge—far more quantum-related jobs are open than qualified candidates available.

“We’ve established internal quantum literacy programs for our software engineers and data scientists,” notes a technology director at a pharmaceutical company. “Even basic quantum knowledge helps teams identify potential applications and collaborate effectively with quantum specialists.”

Practical Hybrid Approaches

The most successful quantum implementations today combine quantum and classical computing, using each for what it does best.

“We’re not replacing our classical infrastructure,” emphasizes a computational chemist at a materials company. “We’re augmenting it with quantum capabilities for specific problems where quantum approaches offer advantages.”

Conclusion: Quantum Computing’s Reality Check

Quantum computing represents one of the most significant technological shifts on the horizon, but it’s important to maintain perspective. Today’s quantum computers are powerful specialized tools for specific problems—not general-purpose replacements for classical computing.

As quantum pioneer John Preskill notes, “Quantum computers aren’t just faster classical computers, any more than airplanes are faster cars. Quantum computing is an entirely different paradigm, better suited for certain types of problems.”

The five applications discussed here—number factoring, quantum simulation, optimization, unstructured search, and linear systems solving—represent areas where quantum computing is already demonstrating practical capabilities or is poised to do so in the near future.

Organizations that understand these specific strengths, rather than viewing quantum computing as a magical solution to all computational challenges, will be best positioned to extract real value from this revolutionary technology.

The quantum future isn’t just coming—it’s already here, in focused, specialized applications that leverage the unique capabilities of these remarkable machines. The question isn’t whether quantum computing will transform industries—it’s which specific problems within each industry are most ripe for quantum advantage, and how organizations can position themselves to capitalize on these opportunities.

As quantum pioneer Richard Feynman once said, “Nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical.” We’re finally building the machines that can do exactly that, opening doors to discoveries and capabilities that were previously beyond our reach.