Every bit stored in a computer is physical.

A zero or one may look abstract on a screen, but underneath the software it ultimately corresponds to a physical state that must be created, maintained or changed. That means computation has an energy cost—and physics places boundaries on how small that cost can become.

Researchers at the University of Edinburgh have developed a mathematical approach that could make magnetic switching substantially more energy efficient. Their peer-reviewed work in Advanced Materials applies optimal control theory to precisely shaped magnetic-field pulses. The simulations found switching energies up to two orders of magnitude lower than conventional field protocols, with optimized field amplitudes more than tenfold smaller; under favorable material parameters, the models reach low-femtojoule switching energies.

That makes the research especially interesting for the intersection of theory, computing hardware and power efficiency.

The Landauer Limit

The theoretical foundation begins with an idea associated with physicist Rolf Landauer: information processing is constrained by thermodynamics.

In simplified terms, logically irreversible operations such as erasing a bit have a minimum possible energy cost related to temperature. That does not mean today’s processors operate at that minimum. Real devices generally consume far more energy because switching, moving data, operating memory and maintaining the surrounding system introduce additional losses.

The Landauer limit is therefore better understood as a physical floor than as a specification engineers can simply order from a semiconductor manufacturer. The magnetic-switching research asks a related engineering question: how much unnecessary energy can be removed from a physical state transition?

Optimization Instead of Brute Force

Magnetic memory stores information using different magnetic states. Changing the stored bit requires changing that state.

Instead of simply applying a conventional switching pulse, researchers Mohammad H. Badarneh, PeiYu Cai and Elton J. G. Santos mathematically optimized how the magnetic field evolves over time. Their published study reports picosecond-scale uniform spin rotations in representative van der Waals magnetic systems and finds that carefully shaped fields can sharply reduce switching energy.

This is an important distinction. The theory is not proposing that information can be manipulated with zero energy. It is trying to identify a more efficient route between physical states.

Why Memory Efficiency Matters

Processor efficiency receives enormous attention, particularly as AI accelerators become larger and more power hungry. But computation is not only arithmetic. Modern systems constantly move information between processors, caches, memory and storage.

As AI models and data-center workloads increase the volume of information being processed, the energy associated with memory operations becomes increasingly important. A highly efficient arithmetic unit cannot eliminate the cost of repeatedly moving and changing enormous quantities of data.

That is why advances in memory architecture belong in the same efficiency conversation as the processors covered in BitcoinVersus.tech’s Semiconductors reporting.

The Big Efficiency Numbers Are Simulation Results

There is an important boundary between the theory and commercial hardware.

The reported efficiency improvements come from computational modeling and optimal-control calculations. They do not mean today’s DRAM or MRAM can immediately be replaced by memory operating at a fundamental thermodynamic limit.

Real hardware introduces fabrication tolerances, thermal effects, control circuitry, signal delivery, reliability requirements and other energy costs. The research instead establishes a theoretical and computational route worth testing experimentally.

The Mathematics Could Extend Beyond Magnetic Fields

The researchers also describe a broader design space in which optimized field-driven switching could complement or hybridize with current- and light-driven approaches. The research paper specifically compares the approach with spin-transfer-torque and spin-orbit-torque methods and argues that optimal control can make field-driven reversal competitive in some regimes.

The same University of Edinburgh research program is also investigating magnetic reservoir computing, connecting spin dynamics, photonics and low-energy information processing for future neuromorphic and edge-computing systems.

The Bigger Theory

For decades, computer performance improved partly by making electronic components smaller and faster.

The next phase of computing may require another dimension: making the physical transition underlying each operation more deliberate. Instead of asking only how small a device can become, engineers can ask how closely its physical behavior can approach the minimum energy required to accomplish the task.

That is where information theory, thermodynamics, semiconductor engineering and computing architecture begin to overlap.

Fundamental thermodynamic limits remain theoretical boundaries—not promises about the next generation of commercial memory. But research that reduces the modeled energy cost of real switching mechanisms helps reveal how much efficiency may still be hidden inside the physics of computation.

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