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Can Quantum Computing Help Keep Two-Wheeler Riders Safe?

  Ask anyone who rides a motorcycle in a dense city and they'll tell you that traffic doesn't behave like independent events. One rider brakes hard, the one behind swerves, and the third has nowhere to go. Risk spreads from rider to rider. That observation is the starting point of research by Natarajan Shriethar , published in Cybernetics and Physics (Vol. 13, No. 4, 2024, pp. 302-322) under the title "Quantum Probabilistic Space Analysis for Enhanced Two-Wheeler Traffic Safety: From Classical Limitations to Advanced Quantum Circuits ." The problem with treating riders as independent Most collision-avoidance models compute a safe distance for each vehicle in isolation, using speed, reaction time and braking. That works reasonably well for cars on highways. Two-wheelers are different: they swerve, filter through gaps and lean, and they can't carry the heavy automation that cars can. Natarajan Shriethar's paper starts with a classical model. Safe distance...
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Can Quantum Computing Help Keep Two-Wheeler Riders Safe?

 



Ask anyone who rides a motorcycle in a dense city and they'll tell you that traffic doesn't behave like independent events. One rider brakes hard, the one behind swerves, and the third has nowhere to go. Risk spreads from rider to rider.

That observation is the starting point of research by Natarajan Shriethar, published in Cybernetics and Physics (Vol. 13, No. 4, 2024, pp. 302-322) under the title "Quantum Probabilistic Space Analysis for Enhanced Two-Wheeler Traffic Safety: From Classical Limitations to Advanced Quantum Circuits."

The problem with treating riders as independent

Most collision-avoidance models compute a safe distance for each vehicle in isolation, using speed, reaction time and braking. That works reasonably well for cars on highways. Two-wheelers are different: they swerve, filter through gaps and lean, and they can't carry the heavy automation that cars can.

Natarajan Shriethar's paper starts with a classical model. Safe distances in front, behind and to the sides depend on velocity and mass. For two and three bikes, the distances depend on the relative velocities and combined masses. Accident risk is modeled as the probability that the real gap between two bikes falls below the required safe gap.

The weak point of this classical setup is that, for simplicity, the pairwise overlaps are treated as independent. In real traffic, they aren't.

The idea: encode riders as qubits

The paper's central move is to map the accident question into a quantum probability space:

  • Each bike (or bike pair) becomes a qubit: |0⟩ means no accident, |1⟩ means accident.
  • Three bikes give three qubits and eight joint states, from |000⟩ (everyone safe) to |111⟩ (all in trouble).
  • Superposition lets the model hold all outcomes at once.
  • Entanglement ties the bikes together, so one rider's state changes the odds for the others without writing out every conditional probability by hand.

The paper also proves lemmas showing how a classical probability distribution can be mapped to a quantum density operator, and it examines the geometry of that space using Bloch-sphere, Fubini-Study and Bures-distance tools.

Running it on a circuit

The circuits were built with Qiskit and run on IBM's quantum computing backend. Three are worth knowing:

  1. Baseline circuit. Hadamard gates put three qubits in an even superposition. Each of the eight outcomes should appear about 12.5% of the time, and the measured values landed between roughly 9.7% and 14.1%. This shows the mapping works, not that it predicts anything about traffic.
  2. Accident-avoidance circuit. Small rotations bias each qubit toward "no accident," CNOT gates entangle the bikes, and counter-rotations refine the result. The safe state |000⟩ came out at about 89% of measurements. Combinations such as |011⟩ and |110⟩ stayed small, and |101⟩ never appeared in the run.
  3. Safety-velocity and six-bike circuits. These extend the idea to speed and distance qubits and to denser six-bike scenarios.

A practical detail: avoiding "blind spots"

The paper also studies where the mathematics can break down. When a density matrix has eigenvalues near zero, quantities like entropy and fidelity can become unstable, which the paper calls singularities. A model that hits one might miss a dangerous situation. Shriethar proposes a simple regularization, adding a small ε to the density matrix, so the eigenvalues stay positive and the measures stay well-behaved.

The vision: an add-on, not a redesign

The paper is careful about scope. The goal is an add-on for existing bikes, such as a helmet or handlebar gadget, rather than a redesigned vehicle. Sensor prototypes could run on Raspberry Pi-class boards, an area where Shriethar has earlier published work.

Honest limits

This is a theoretical framework and proof of concept, not a deployable safety system.

  • The circuit outputs demonstrate the method. They are not calibrated to real traffic data.
  • Constants in the safe-distance model still need empirical fitting.
  • Today's quantum hardware is costly and not suited to a helmet, so a real product would likely use classical hardware, or algorithms inspired by this approach, for the near term.

The paper names these next steps itself: real sensor and GPS data, more vehicle types, and empirical calibration.

Why SpaceQuanta cares

SpaceQuanta exists at the meeting point of quantum ideas and real-world problems. Road safety for two-wheelers, where risk is correlated, data is messy and cars' solutions don't transfer, is exactly the kind of problem worth testing new mathematics on. This paper is a first step, and the next ones will be about data.

Read the paper: Shriethar, N. (2024). "Quantum Probabilistic Space Analysis for Enhanced Two-Wheeler Traffic Safety: From Classical Limitations to Advanced Quantum Circuits." Cybernetics and Physics, 13(4), 302-322. https://doi.org/10.35470/2226-4116-2024-13-4-302-322

 

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