Last month, the White House Quantum Summit brought together dozens of quantum policy officials and industry leaders as the U.S. strives to become the first nation to build a fault-tolerant quantum computer.
The summit, which convened on July 7 in the Eisenhower Executive Office Building next to the West Wing, lasted over two hours and covered everything from the supply chain to how the U.S. can meet the deadline the Trump administration has laid out for delivering a quantum supercomputer by 2028.
Much of the urgency surrounding quantum computing centers around the explosive growth of artificial intelligence (AI) and related fields like machine learning, which have evolved quickly from novelty into necessity, and are ramping up with researchers pushing AI models to new extremes.
In the long term, however, AI’s role in electronic design will be limited by the constraints of classical computing, particularly its power and run time requirements.
How can it get to the next level? By employing an equally esoteric technology called quantum computing.
Quantum Computing Basics
The primary difference between classical and quantum computers is that quantum computers use quantum bits (qubits) instead of regular bits (zeros and ones).
A qubit can act like a bit, storing either a zero or a one, but it can also exist as a combination of both simultaneously.
This is worth repeating: Qubits can be both 0 and 1 at the same time, at least until it is measured. So, it can represent a range of possibilities rather than a single definite value. This is called superposition, and it is a basic feature of any quantum state.
Because of this capability, quantum computing can help AI rapidly process extensive data sets, thereby accelerating AI processes and training models.
When a classical computer uses multiple variables to solve a problem, it must conduct a new calculation every time one of those variables changes. Each calculation is a single path to a single result. Quantum computers, on the other hand, can explore many paths in parallel through superposition.
Here is another way to look at it. You are in a maze. To escape the maze, you could try every possible combination of paths. A quantum computer, on the other hand, might derive the correct path without needing to test all the possibilities.
In this way, quantum computers have the potential to solve certain problems in minutes or hours that would otherwise take conventional machines years to complete.
Key Principles
A quantum computer works primarily using four key principles of quantum physics:
Entanglement: This is the ability of qubits to correlate their state with other qubits. If one qubit changes, the other changes as well. Quantum objects can be closely tied together so that the state of a single quantum object cannot be described without describing the other. Entanglement allows qubits to scale exponentially. Two qubits, for example, can store and process four bits of information. Three qubits can process eight, and so on.
Superposition: As noted, qubits can hold many states at once. Superposition represents a combination of all possible configurations of the qubit.
Interference: Waves of probability overlap to cancel out wrong answers and combine and boost the amplitudes of the right ones
Physical qubits suffer from noise and what is called decoherence, the process in which a system in a quantum state collapses from a superposition of possibilities into a binary state, and as a result produces inaccurate results.
Quantum computing thus requires big time advancements in error correction before full-scale commercial deployment is possible.
Quantum processors also need to be cold – just hundredths of a degree above absolute zero – to minimize noise and avoid decoherence.
At these temperatures, certain materials exhibit an important quantum mechanical effect: electrons move through them without resistance. This effect makes them superconductors.
Industry Is Getting More Involved
Leading institutions like IBM, Alphabet (Google Quantum AI), Amazon, Microsoft and start-ups like IonQ and Rigetti Computing have launched multi-billion-dollar quantum computing initiatives.
In the commercial world, the chemical company BASF, in a collaborative project with Google and QSimulate, has examined the use of quantum computers to accurately simulate lithium nickel oxide (LNO), a material used in batteries. LNO offers a smaller environmental footprint than commonly used lithium cobalt oxide.
Simulating the quantum mechanical behavior of LNO could improve the industrial production process and ultimately help make better batteries.
Key Obstacles
One major challenge in the advancement of quantum computing is the volatility of qubits. When a qubit changes its status, inputs can be lost or altered, which throws off the accuracy of the results
The biggest hurdle to be overcome? Scaling quantum processors. While qubit processors used in quantum computing have the potential to massively outperform bit-based processors, current quantum processors can support only a few potential qubits.
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