Quantum computing for the rest of us

What’s next after AI? For more companies, the answer is quantum computing.

Quantum mechanics explains how the tiniest things in the universe behave—atoms, electrons, and light particles. Now, thanks to its synergy with artificial intelligence, quantum’s role in computing is inching closer to mainstream for communications, media and technology companies.

The AI explosion has demonstrated to enterprises, investors, researchers and governments that advanced computing equals power. With classical computing nearing its physical limits, there’s a mad dash for what's next. That “what’s next” could be quantum, and AI is essentially lighting a fire under that movement. How can companies prepare? We show how the lessons learned during the mass adoption of AI over the last couple years can provide a solid path forward to quantum, especially for the CMT sector’s focus on digital infrastructure and converging business models.

Quantum computing prepares to go mainstream

Quantum mechanics have long been at work in everyday technologies such as lasers, LEDs and smartphones. But it’s in computing that quantum mechanics is set to break free from the limits of classical computing. Instead of using bits in the form of 1s and 0s, quantum computers use quantum bits, or qubits. Each qubit can be a 0, 1, or both at the same time. That massive parallelism—or the ability to explore many possibilities at once—speeds things up and opens up new kinds of algorithms. Quantum computing isn’t just faster; it’s fundamentally different.

Small wonder, then, that the quantum race is on. Semiconductor makers Intel and TSMC are investing heavily in quantum. So are tech giants Google, Microsoft and Amazon. Media and entertainment leaders are discussing it and exploring quantum’s potential to enhance video processing, compression and streaming. Telecom providers have partnered to offer quantum key distribution in data centers. Government research programs include the U.S. National Quantum Initiative, the Quantum Flagship in the European Union, and similar programs in China and other countries.

AI needs quantum, and quantum needs AI

Providing additional intensity to the quantum race is AI—and the breathtaking speed at which it has redefined technology and the companies that lead it. When it comes to complex computations, AI and quantum computing benefit each other. The synergy between the two technologies is already clear: AI needs quantum, and quantum needs AI.

For example, in machine learning (ML), quantum algorithms can speed up the training of complex models, enabling faster insights and improved image recognition and natural language processing. Quantum techniques can also increase the accuracy of AI predictions by handling larger and more complex datasets. Moreover, quantum-resistant encryption algorithms enhance the security of AI systems and are crucial for protecting sensitive data used in AI applications, such as financial information. 

Similarly, the AI gold rush is helping to pave the way for quantum computing. For example, Google AlphaQubit is an AI-based decoder that increases detection of quantum computing errors—a crucial aspect of building large-scale, fault-tolerant quantum computers. Accurately identifying errors will enable quantum computers to perform long computations at scale and open the doors to scientific breakthroughs.

Lessons learned from AI adoption

But you’re not alone if all of this leaves you feeling that your company is unprepared for yet another technological revolution. We’re in the age of volatility, uncertainty, complexity and ambiguity, or VUCA. The good news is that the AI explosion is giving us a head start on quantum: Not only is the technology of AI helping to boost quantum computing, but so are the lessons learned by enterprises as they’ve adopted AI.

One of the smartest moves companies can make as they explore a sensible quantum strategy is to studiously avoid the missteps of AI. Here’s how:

• Lesson #1: Adopt a strategic and realistic mindset. One of the biggest takeaways from the AI revolution is the importance of setting realistic expectations. Quantum computing gives companies the chance to approach a new technology—but to resist the urge to overpromise and underdeliver. Instead, our guidance for quantum is to set achievable expectations by emphasizing its long-term potential and acknowledging both its current limitations and the time it will take for the technology to mature.
  
  Focus on strategy over technology. Keeping your company’s business goals in mind, prioritize applications for which quantum may offer competitive advantage. Develop a 10-year vision. This proactive, integrated preparation is crucial for navigating quantum's eventual impact.
  
  
• Lesson #2: Build awareness within your organization. Quantum isn’t yet about prototyping, and go-to-market strategies and scaling concerns are years off. But quantum is coming, and you need to prepare. Discussion should start at the top. Hold CX-level discussion forums to familiarize leaders with the technology. Attend forums. Join industry conversations. Check out quantum leaders in your industry. Include VPs or directors of business, IT and engineering in your conversations and have them investigate potential use cases. Identify where you want to be with quantum in 10 years.
  
  
• Lesson #3: It’s never too soon to prioritize data quality. Invest in data infrastructure. While the type of data may differ, high-quality data will be crucial for quantum applications, especially in areas like quantum ML. Begin preparing your data infrastructure now. 
  
  
• Lesson #4: Assess and inventory your skills. Build a quantum-ready workforce. Invest in training and education programs to develop quantum expertise in-house. Partner with universities and research institutions to access talent and stay at the forefront of quantum advancements. 
  
  
• Lesson #5: Prioritize ethical oversight and explainability. Proactively address the ethical and societal implications of quantum technology, developing guidelines for responsible development and deployment. Focus on transparency, security, fairness and explainability. As quantum algorithms grow more complex, invest in research and techniques to improve their transparency. This will be crucial for building trust and ensuring responsible use.
  
  
• Lesson #6: Adopt a phased approach. Start with pilot projects and gradually scale quantum initiatives across the organization as the technology matures and its value becomes clearer. Encourage collaboration between different departments to foster knowledge sharing and accelerate adoption. 
  
  
• Lesson #7: Consider potential partnerships. Given the cost and complexity of quantum, small and medium-size companies may question the business outcomes and ROI. For them, the smartest approach will be collaboration with larger players. While it may be too early to establish joint ventures and formal partnerships, it’s important to explore your options. Know who the big players are and, more importantly, who are the right ones for your organization.

Don't let quantum take you by surprise. It’s a five-, 10- or 15-year roadmap, and now is the time to start.