Quantum computing has long seemed like a technology of the distant future. But in recent years, rapid advances have brought us much closer to having fully operational quantum computers. So how close exactly are we in 2024? Let’s examine the latest breakthroughs and challenges that remain.
Major Tech Companies Lead the Charge
Some of the biggest strides toward quantum computers have come from major technology companies pouring resources into quantum computing research.
Google Achieves Quantum Supremacy
In 2019, Google technically achieved quantum supremacy performing a computation impossible for normal computers in a reasonable amount of time. They used a 53-qubit quantum processor named Sycamore.
However, many experts argue that while a technical milestone, Sycamore has limited practical application so far. The calculation was specifically designed to demonstrate an advantage on that limited hardware rather than solve a problem.
IBM Unveils 127 Qubit Processor
In late 2021, IBM unveiled its Eagle processor with 127 qubits, a big jump over its previous 65-qubit processor. While error rates remain too high for full error correction, it is progressing up the quantum volume scale towards useful near term applications in finance, logistics, chemistry, and optimization problems.
Startups Push New Approaches
Alongside the computing giants, various quantum computing startups have unique technologies that could help the field advance more rapidly.
IonQ Makes 32 High Quality Qubits
In 2022, the startup IonQ reported developing 32 high quality qubits using trapped ion technology. While fewer qubits than other approaches, trapped ions provide stability and low error rates necessary to scale meaningfully.
Quantinuum Commercializes Quantum Cybersecurity
Another startup Quantinuum (formed by merging Honeywell Quantum Solutions and Cambridge Quantum) is focused on using near term quantum computing advances for cybersecurity applications like encryption key generation.
In 2023, they announced commercial availability of the world’s first commercial quantum natural language processing toolkit, indicating meaningful applications are coming sooner than expected.
When Will We Have a Full Scale Quantum Computer?
As quantum computing hardware and software continue advancing rapidly, when can we expect to see a fault tolerant, fully scalable quantum computer capable of solving complex real world problems?
~2029: Large Scale, Error Corrected Quantum Computer
Based on projections of technical breakthroughs required and continued investment growth, many experts estimate we are still on track for a large scale, error corrected quantum computer by around 2029.
However there is still uncertainty, as hardware challenges can create unanticipated bottlenecks. The most optimistic projections put the timeline only a few years earlier by 2026-2027.
>2035: Broad Commercialization Takes Off
After we achieve that first milestone of a scalable quantum computer, there will still be substantial work across hardware, software, and integration before the technology can develop into a broad commercial ecosystem.
Most projections do not show this widespread commercialization taking off until well into the 2030s.
Key Technical Challenges Remaining
Reaching the full potential of quantum computing to revolutionize computation in areas like chemistry, optimization, machine learning, and beyond will require overcoming critical technical obstacles across both hardware and software.
Hardware Stability Needs Major Improvement
The fragile state of quantum bits (qubits) makes them prone to errors and limits the stability for computations. Current error rates in qubits would accumulate too rapidly for large complex computations. Substantial improvements in qubit technology and control hardware are essential to enable error correction and accurate scaling to thousands and millions of qubits.
Algorithms Must Become Far More Sophisticated
While hard algorithmic problems related to optimization show the most promise currently, algorithms cannot yet take full advantage of what should theoretically be possible with quantum computers.
Teams of theorists must make major algorithmic innovations tailored to quantum computing’s unique capabilities in order for unambiguous quantum advantage over classical computers to be demonstrated.
|Type of Quantum Computer
|Google, IBM, Rigetti
|Trapped Ion Qubits
Engineering Productization Presents Challenges
The precise controls and isolation required stretch the limits of mechanical and electrical engineering. As with any novel technology going from lab research to commercial product, substantial engineering hurdles must be solved related to reliability, thermal management, interconnects, and manufacturability.
Software Needs Abstraction Layers
To enable more rapid development and hide the underlying complexity from users, robust software abstraction layers need to be built similar to classical computing. Frameworks will be required bringing together tools for composing algorithms, compiling code, mapping to hardware, and managing / visualizing results.
Pace of Innovation Makes the Future Hard to Predict
Given the unprecedented pace of progress in quantum computing recently, it is difficult to extrapolate too far when disruptive new ideas or bottlenecks may emerge. If breakthroughs in areas like substantially reducing qubit errors or developing complex quantum algorithms happen earlier than expected, mainstream application of these powerful computers could arrive sooner than the current 2029-2035 timeline. Or if certain engineering challenges prove more intractable than anticipated, progress could move slower during these early days of quantum computing.
The only certainty is that quantum computing technology remains dynamic and rapidly evolving into the 2020s, so we must continue tracking developments in both R&D and business landscapes closely rather than making overly confident predictions. The quantum era is coming but precisely when is still being written.
In 2024, we find that viable, large scale quantum computers likely remain several years in the future but the pace is faster than ever. Major investments from the likes of Google, IBM, Honeywell, and multiple venture backed startups have led to rapid hardware advances recently in the quality and count of qubits. Combine this with software innovations in quantum algorithms and applications, and we could see revolutionary capabilities emerge before 2030.
However realizing the full theoretical promise of quantum computing to exponentially speed up computations in many areas of optimization, chemistry, finance, and more remains further out as more complex engineering and commercialization occurs through the 2030s. The next several years promise to be an incredibly dynamic period pushing quantum computers towards useful applications. While forecasting an precise arrival time is difficult, we inch ever closer each year to harnessing the power of quantum physics for computation.
What is the current state of quantum computers?
The current state as of 2024 includes processors with over 100 qubits but error rates remain too high for fault tolerance and complex error correction. Rapid improvements on multiple hardware approaches are being made that could enable a fully error corrected, scalable quantum computer before the end of the decade.
How soon could quantum machine learning happen?
Useful quantum machine learning likely requires thousands of logical qubits that can only come with error correction not achievable until at least the late 2020s based on current roadmaps. But some narrow machine learning applications tailored to NISQ (Noisy Intermediate Scale Quantum) era hardware could emerge even earlier.
When will average people start using quantum computers regularly?
It likely will not be until well into the 2030s that quantum computing truly goes mainstream for practical applications in business and daily life for regular end users. This requires substantial progress across algorithms, software infrastructure, and integration before commercial accessibility.
What industries will first adopt quantum computing?
Experts anticipate industries like finance, pharmaceuticals, chemicals, logistics, and security/encryption will be among the first adopters of quantum computing for narrow use cases as the technology matures. Widespread adoption across even these industries remains over a decade away however.
Could progress in quantum computing slowdown?
Yes, there are definitely scenarios where quantum computing hardware and commercialization could slowdown from the rapid pace we have seen recently. Unforeseen engineering challenges, lack of financing, shortages in technical talent, or other bottlenecks could push timelines out further. The timing remains quite uncertain still in these early days.
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