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Classical vs. Quantum Improvement Trajectories

This MicroSim uses a dual-axis logarithmic line chart to compare the historical improvement rates of classical and quantum computing from 2015 to 2025. The visualization highlights a critical asymmetry: classical computing has delivered steady, compounding performance gains (approximately 100x per decade in FLOPS per dollar), while quantum computing's headline qubit counts have grown without a corresponding improvement in practical computational capability.

View Improvement Trajectories MicroSim Fullscreen

How to Use This Chart

  • Hover over any data point to see the specific hardware, benchmark value, and source context.
  • Use the toggle buttons below the chart to show or hide individual data series. Try viewing just "Quantum Useful Ops" alongside the "Advantage Threshold" to see how far quantum hardware remains from commercially relevant capability.
  • The left axis (green) tracks classical GPU performance in FLOPS per dollar. The right axis (orange) tracks quantum metrics: useful operations (qubit count times gate fidelity) or raw qubit count.

What to Notice

The classical trajectory shows a smooth, predictable exponential — the kind of curve that enables long-term engineering planning and reliable return on investment. The quantum trajectory, while impressive in raw qubit count growth, remains roughly six orders of magnitude below the threshold needed for commercially relevant quantum advantage.

The dashed gray line at \(10^6\) represents a rough minimum for useful error-corrected quantum computation. Even at the current rate of improvement, quantum hardware would need decades to close this gap — and that assumes the improvement rate does not plateau as noise, crosstalk, and engineering constraints intensify at scale.

Toggle on the "Raw Qubit Count" series to see that qubit count alone paints a misleadingly optimistic picture. When you factor in gate fidelity (the "Quantum Useful Ops" line), the effective capability is barely different from raw count — because error rates have improved only marginally. Quantity without quality does not produce computational advantage.