They are a revolutionary concept in computing that bridges the gap between classical deterministic bits (0 or 1) and quantum bits (qubits, which can be in superposition). Instead of being strictly binary, p-bits fluctuate probabilistically between 0 and 1, making them ideal for stochastic computing, AI, optimization, and cryptography.

They are probabilistic binary units that behave like a randomly fluctuating bit with a controllable bias. Unlike a qubit, it does not rely on quantum mechanics , instead it uses classical noise (thermal, electrical, or engineered) to achieve probabilistic behaviour. Mathematically, a p-bit’s behaviour is described b

P(1)=σ(I),P(0)=1−σ(I) : where σ(I) = sigmoid function (e.g., 1+e−I1​) and where I = input current or control signal (can be analogue or digital)

Challenges

Noise control: too much noise reduces reliability.

Scalability: building large p-bit arrays is still experimental.

Precision: bot ideal for exact deterministic computing.

P-Bits as the future of probabilistic hardware

I/ML (Bayesian learning, generative models).

Optimization (Ising machines, combinatorial problems).

Security (TRNG, post-quantum crypto).

Probabilistic computing & uncertainty modelling

Traditional computing relies on deterministic logic but real world problems (e.g. AI, finance, quantum simulations) often involve uncertainty.

Noise protocols (like those in cryptographic systems) inherently deal with probabilistic behaviour, making them suitable for modelling uncertain computations.

By integrating noise like constructs, probabilistic computers can natively handle randomness, approximations and stochastic processes without costly deterministic emulation.

Secure & private computation

Noise protocols (e.g. in Extropic`s work) are used for secure messaging and key exchange (similar to signal protocol).

In probabilistic computing, noise can be a feature rather than a bug adding controlled randomness and improves differential privacy and secures multi-party computation (MPC).

Future systems may use noise like frameworks to obfuscate computations while preserving correctness probabilistically.

Hardware applications of noise in probabilistic computing

Stochastic & probabilistic circuits : probabilistic bits (p-bits) (e.g. by Purdue/ARM) replace traditional binary logic with randomness driven computation.

Noise is used in invertible logic gates for energy efficient Bayesian inference.

Applications: low-power AI accelerators, optimization solvers (Ising machines).

Future: entropic noise modulation could stabilize memristor based neural networks.

Future outlook

Noise based AI for more robust, private and efficient neural networks.

Noise driven hardware for AI probabilistic chips, cryptography and edge computing.

Extropic`s role could lead in secure, noise enhanced probabilistic architectures.