Quantum - William D. Oliver

"Realizing the Promise of Quantum Computation," William D. Oliver, Director of the Center for Quantum Engineering, Massachusetts Institute of Technology

In November 2025, the Science, Technology, and Social Values Lab at the Institute for Advanced Study (IAS) hosted the second event in the:Quantum, Broadly Considered lecture series. The series explores how unresolved questions in physics and mathematics, alongside government investments, market dynamics, and social imaginaries have shaped, and continue to shape, the possibilities of quantum research.

In this lecture, Professor William D. Oliver, Director of MIT’s Center for Quantum Engineering, provided an engineer’s perspective on quantum computing—from its scientific foundations to its current capabilities and limitations—and what it will take to deliver commercially useful quantum systems.

Having traced the technology’s evolution back to the 1927 Solvay Conference and the birth of quantum mechanics for quantum 1.0, which ultimately enabled all modern electronics, computing, and telecommunications, Oliver then focused his discussion on what he termed quantum 2.0, which harnesses quantum mechanics as the mechanism of functionality.

Quantum 2.0 leverages quantum phenomena such as superposition (the ability of a quantum bit, or qubit, to exist in multiple states at the same time) and entanglement (where two or more particles become linked and share the same fate, regardless of the distance separating them) to build technologies such as quantum computers, quantum communication networks, and advanced quantum sensors.

This is a leap from Quantum 1.0, which used quantum effects such as electron tunneling in devices including transistors and lasers. Quantum 2.0 uses quantum principles directly, harnessing quantum phenomena in sensing, networking, and processing, to achieve capabilities that are impossible or impractical with classical computing technology.

A quantum leap, but gaps remain
Positioning himself as a “realist, not an evangelist,” Oliver asserted that, while quantum computing is valid and potentially transformative, we are much further from that future than the hype suggests.

Expectations around quantum computing are understandable. Quantum computers could theoretically perform certain tasks exponentially faster than is currently possible, Oliver noted. For example, simulating molecules for drug discovery, optimizing complex logistics, and breaking encryption codes.

Delivering that reality is the sticking point, and will involve overcoming significant engineering hurdles. While today’s quantum computers may be impressive as technological experiments, they are not yet practical tools. A critical problem is that they’re fragile; the quantum states they rely on are so delicate that they can lose their quantum properties, such as superposition, through interaction with the environment.

Oliver asserted that for quantum to “beat” classical computing in its value proposition, its application must satisfy three requirements: (1) that no fast classical solution exists; (2) that a fast quantum algorithm exists; (3) that solving the target problem offers commercial value. It also means solving some not insignificant engineering challenges, including data loading, error correction, and scaling.

The error-correction problem
While regular computer bits (which can hold a value of either 0 or 1) are stable, qubits (which can be 0, 1, or both simultaneously) constantly make mistakes. Error rates can range from 0.1% to 1% per operation; across millions of operations these could be catastrophic. Physical qubits must be grouped into large “teams” (logical qubits) to suppress errors.

Oliver observed that error correction is more than a technical challenge—it poses a fundamental barrier. Google researchers recently made important progress by demonstrating the logical error probability, and where adding more qubits reduces the error rate. Others, including IBM research teams, are also working on the problem. But error-corrected quantum computing requires high-quality qubits and extensive supporting infrastructure, intensifying the need for continued investment.

For now, quantum simulation (e.g., of chemistry, materials, etc) is the nearest-term important commercial use case. Oliver pointed to a very real risk of a “quantum winter” if expectations aren’t met and funding dries up.

The immediate prospects: more questions than answers?
In a Q&A session moderated by Professor Nelson the issues raised included ongoing funding: Where classical computing emerged from government and academic research and attracted commercial investment when use cases became clear, quantum computing’s immediate commercial value is less certain, and government remains its primary funder. Multisector interdisciplinary, global ecosystems are forming, however.

Currently, although commercial players such as IBM have modest quantum systems available, these are being used primarily for algorithm prototyping, and do not yet outperform classical computers. One of the questions yet to be resolved is what size quantum computer it would take to eventually outperform classical systems.

Professor Oliver was also asked about the energy needs of quantum computers. Quantum computing could be expected to save energy relative to classical computing if it reduces the time to solve very complex problems, he explained. The bigger consumption will come from the adjacent classical computers, which will continue to perform the inference. (Quantum computers will ultimately function as co-processors for complex sub-problems; classical computers will still carry the wider processing load.)

A question was also posed about hype around quantum AI, but Oliver cautioned that the data-loading bottleneck may limit quantum computers’ impact here. Classical AI, though, is being used to optimize quantum computing performance.

Quantum technology development may mirror classical computing's decades-long evolution. Success will hinge on engineering breakthroughs in areas such as materials science and cryogenic technology. Despite outstanding challenges, the perceived importance of quantum computing for national security and future economic competitiveness continues to drive strategic investment.