🏆 2025 Nobel Prize in Physics: Breakthrough in Macroscopic Quantum Mechanics
On October 7, 2025, the Royal Swedish Academy of Sciences announced the Nobel Prize in Physics would be awarded to:
• John Clarke – University of California, Berkeley
• Michel H. Devoret – Yale University & UC Santa Barbara
• John M. Martinis – UC Santa Barbara
Award citation:
“For the discovery of macroscopic quantum tunneling and energy quantization in electrical circuits.”
Their chip-based experiments showcased the real-world power of quantum physics, revealing how quantum mechanics can manifest in surprisingly large systems.
What They Did — Explained in Plain Talk (with Math Flavor)
Background: Where Does Quantum Mechanics Break Down?
Quantum mechanics usually describes weird behaviors of tiny particles (like electrons), such as:
• Tunneling: particles pass through barriers like ghosts through walls
• Energy quantization: energy exists only in fixed chunks
But here’s the big question: How large can a system be and still show quantum effects?
Normally, when systems get bigger (like a bunch of particles), quantum effects fade away.
These three laureates built a palm-sized circuit to prove quantum mechanics can still flex its muscles at the macroscopic scale!
Core of the Experiment: Josephson Junction & Superconducting Circuit
Back in 1984–1985, they used superconductors (materials with zero resistance) to build a circuit featuring a key component: the Josephson junction.
This junction is made by sandwiching a thin insulating layer between two superconductors. Current flows only via quantum tunneling.
Mathematically, the current 𝐼 and voltage 𝑉 across the junction relate as:
𝐼 = 𝐼𝑐 × sin(𝜙)
Where:
• 𝐼𝑐 is the critical current
• 𝜙 is the phase difference, reflecting the quantum behavior of Cooper pairs (paired electrons in superconductors)
Their circuit behaves like a macroscopic particle — made of countless Cooper pairs but acting like a single quantum entity.
Its state is described by a wavefunction ψ, just like a lone particle in a potential well.
Quantum Tunneling
Initially, the circuit sits in a zero-voltage state, with current flowing freely — like being trapped in a potential well.
Quantum mechanics allows the system to tunnel out and generate measurable voltage.
Tunneling probability depends on barrier height 𝑉₀ and width 𝑎, simplified as:
𝑃 ∝ e^(−2 × ∫√(2𝑚 × (𝑉(𝑥) − 𝐸)) dx ÷ ℏ)
Where:
• 𝑚 is the effective mass
• 𝐸 is the energy
• ℏ is the reduced Planck constant
Even large systems can “ghost” through barriers thanks to quantum tunneling!
Energy Quantization
They also showed the circuit’s energy is quantized — it can only absorb or release energy in fixed amounts, like electrons jumping between atomic levels.
Energy levels follow:
𝐸ₙ = 𝑛 × ℏ × ω
Where:
• 𝑛 is the quantum number
• ω is the system’s characteristic frequency
This means energy isn’t continuous — it’s like a staircase, one step at a time.
Everyday Analogy
Imagine a bucket of water (Cooper pairs) with a tiny hole (Josephson junction).
Normally, water stays trapped. But quantum mechanics lets it teleport through the wall (tunneling), and it flows out in fixed portions (quantization).
These scientists simulated this on a chip — proving even palm-sized circuits can pull off quantum tricks!
Why It Matters
Scientific impact:
This experiment shattered the belief that quantum mechanics only applies to tiny systems. It expanded our understanding of quantum physics into the macroscopic realm.
Real-world applications:
• Quantum computers: Their tech laid the groundwork for superconducting qubits — like those used by Google
• Quantum cryptography: Ultra-secure encryption using quantum properties
• Quantum sensors: Ultra-precise measurements, like detecting weak magnetic fields or gravitational waves
Bonus: How Wavelet Theory Connects
While wavelet theory wasn’t the main topic of the prize, it’s super useful for analyzing data from these kinds of experiments.
• Signal analysis: Wavelet transforms (especially CWT) help break down time-frequency signals from circuits, pinpointing tunneling or energy shifts
• Noise reduction: DWT filters out noise, highlighting key quantum features
• Mathematically, wavelet transforms decompose signals 𝑓(𝑡) into different scales of approximation and detail — revealing the “ripples” of macroscopic quantum behavior
Wavelet Theory — Taiwanese-style Breakdown (with Unicode Math)
Wavelet theory is like a genius data slicer — it chops messy stuff (like sound, images, seismic waves) into clean chunks for analysis.
Unlike traditional Fourier transforms that only look at frequency, wavelets capture both time and frequency, perfect for jumpy, non-stationary signals.
1. Image & Video Compression
Use case: Shrinks your phone photos or Netflix videos without blurring — saves bandwidth, stays smooth.
How it works:
• JPEG 2000 uses DWT to split images into frequency layers
• Signal 𝑓(𝑥, 𝑦) becomes:
𝑓(𝑥, 𝑦) = Σ𝑐ⱼ,ₖ × 𝜙ⱼ,ₖ(𝑥, 𝑦) + Σ𝑑ⱼ,ₖ,ᵈ × ψⱼ,ₖ,ᵈ(𝑥, 𝑦)
Where:
• 𝜙: scaling function (captures big shapes)
• ψ: wavelet function (captures fine details)
• ⱼ, ₖ: scale and position
• ᵈ: direction (horizontal, vertical, diagonal)
Math win: Fast algorithms like Mallat’s make DWT super efficient — complexity 𝑂(𝑛), better than Fourier’s 𝑂(𝑛 × log 𝑛)
2. Noise Filtering
Use case: Removes background noise (like traffic) so signals (speech, seismic waves) are crystal clear.
How it works:
• Decompose signal 𝑠(𝑡) into layers:
𝑠(𝑡) = Σ𝑐ⱼ,ₖ × 𝜙ⱼ,ₖ(𝑡) + Σ𝑑ⱼ,ₖ × ψⱼ,ₖ(𝑡)
• Use thresholding to zero out small coefficients (usually noise), then reconstruct clean signal
Math win: CWT formula:
𝑊(𝑎, 𝑏) = (1 ÷ √𝑎) × ∫𝑓(𝑡) × ψ((𝑡 − 𝑏) ÷ 𝑎) d𝑡
Where:
• 𝑎: scale (frequency)
• 𝑏: time shift
3. Data Mining & Pattern Recognition
Use case: Finds patterns in messy data (like stock prices or images) to guide decisions.
How it works:
• DWT splits time series 𝑥(𝑡) into:• Low-frequency (long-term trends)
• High-frequency (short-term jumps)
Math win: Wavelet bases like Daubechies are orthogonal — no overlap, perfect reconstruction.
4. Hardcore Science & Engineering
Applications:
• Turbulence & gravitational waves: LIGO uses CWT to spot faint signals
• Weather forecasting: DWT reveals cycles in temperature/rainfall
• Machine maintenance: Detects faulty vibrations (e.g., broken bearings)
Math win: Compact support keeps calculations local and efficient.
5. Trendy & Futuristic Uses
Applications:
• AI & deep learning: Wavelets + neural nets = multi-scale feature extraction
• Cybersecurity: Detects anomalies in network traffic
• Medical imaging: Enhances contrast in MRI/ultrasound
Math Recap
Wavelets decompose signal 𝑓(𝑡) using scaling 𝜙 and wavelet ψ functions:
𝑓(𝑡) ≈ Σ𝑐ⱼ,ₖ × 𝜙ⱼ,ₖ(𝑡) + Σ𝑑ⱼ,ₖ × ψⱼ,ₖ(𝑡)
• DWT: discrete, fast, great for computers
• CWT: continuous, ultra-detailed, great for science
• Common wavelets: Haar (simple square), Daubechies
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