💼 Quantum-Tech Beveridge Curve: Search & Matching Model

Watch unemployment and vacancies in the quantum-tech labor market flow through a real Cobb-Douglas matching function and trace out the empirical Beveridge curve live.

EconomicsLabor Market2D
Left: live (U, V) trace tracing the Beveridge curve · Right: U(t) and V(t) time series

How it Works

This is a search-and-matching labor market model (Diamond–Mortensen–Pissarides style), applied to the quantum-technology job market: unemployed physicists and quantum engineers (U) search for open roles at quantum-computing, quantum-sensing and quantum-communications firms (V). The two pools meet through a real Cobb-Douglas matching function:

Matching rate: M = A · U^α · V^(1-α) Unemployment flow: dU/dt = s·(1-U) - M Vacancy flow: dV/dt = c·(1-V) - M A = matching efficiency (quantum-skills training investment) s = job separation rate, c = vacancy-creation intensity α = matching elasticity w.r.t. unemployment

Job separations feed workers into unemployment at rate s; successful matches drain both U and V simultaneously. Vacancy creation feeds new openings into V at rate c, likewise drained by matches. Raising the training investment A slider means quantum-skilled workers are matched to relevant vacancies faster for the same U and V — this pulls the whole steady-state relationship between U and V inward toward the origin, a real and empirically documented effect of improved labor-market matching efficiency. Small cyclical wobbles in s and c (representing quantum-industry hiring cycles) keep the (U, V) point moving, tracing out the downward-sloping Beveridge curve rather than sitting at a single dot.

Frequently Asked Questions

What is the Beveridge curve?

The Beveridge curve is the empirically observed downward-sloping relationship between the unemployment rate and the job vacancy rate: when the labor market is slack, unemployment is high and vacancies are scarce; when it is tight, unemployment is low and vacancies are plentiful. It is a real, widely used diagnostic in labor economics, not a theoretical curiosity.

What is the matching function?

The matching function M = A × U^α × V^(1-α) is a Cobb-Douglas style function (Diamond-Mortensen-Pissarides / DMP model) that converts a stock of unemployed workers U and open vacancies V into a flow of new hires M each period. A is matching efficiency, α is the elasticity of matching with respect to unemployment.

Why does quantum-skills training shift the curve?

Training raises matching efficiency A: better-prepared quantum engineers and physicists are recognized and hired faster for the same pool of unemployed workers and open vacancies. A higher A means more hires per period at any given U and V, which pulls the steady-state Beveridge curve inward toward the origin — fewer unemployed workers and fewer unfilled vacancies sit around simultaneously.

Why does the curve trace out a loop instead of a single point?

Job separations s and vacancy creation c oscillate slightly to represent business-cycle-like fluctuations in the quantum-tech sector. Because unemployment and vacancies adjust with some inertia rather than jumping instantly to a new steady state, the (U, V) path traces a loop around the underlying curve rather than sitting still — the same pattern seen in real labor-market data.

What do s and c represent?

s is the job separation (layoff) rate: the fraction of employed workers who lose or leave their job each period, feeding into unemployment. c is vacancy-creation intensity: how aggressively firms open new quantum-tech positions, feeding into the vacancy rate. Both are drained by successful matches M.

About this simulation

Written by MySimulator Team · Reviewed by MySimulator Editorial Review

Last updated: 4 September 2026

A real search-and-matching (Diamond–Mortensen–Pissarides) model of the quantum-tech labor market: unemployed quantum specialists and open quantum-industry vacancies meet via a Cobb-Douglas matching function, and the resulting unemployment and vacancy rates trace out the empirical Beveridge curve as the simulation runs forward in time.

🔬 What it shows

A live (U, V) trace on the left tracing the downward-sloping Beveridge curve, with the theoretical steady-state curve for the current parameters drawn faintly beneath it, plus U(t) and V(t) time series on the right.

🎮 How to use

Raise the training-investment slider (A) to watch the whole curve shift inward, or change separation rate s, vacancy creation c, and matching elasticity α to see how each redraws the underlying relationship.

💡 Did you know?

Real Beveridge curves shifted outward across many economies after 2020 — more vacancies sat unfilled at any given unemployment rate, widely attributed to skills mismatch. This simulation's training-investment slider is a direct model of the opposite policy lever: better matching efficiency pulls the curve back in.