Learn

Quantum Computing from Spin Physics

From the Bloch equation to Grover – without a lab quantum computer

First cohort: January 2027

$2,999 · Asynchronous (8 weeks) · Weekly office hours · Lifetime access to this course

Unlike typical quantum courses that start from circuit diagrams and cloud notebooks, this course starts from the Bloch equation and NMR spin physics – the same framework used in the first experimental demonstrations of quantum computing. Those early implementations treated qubits as real spins, gates as RF pulses and couplings, and algorithms as coherent evolutions under a spin Hamiltonian. We follow that path: from pulses and product operators to Grover, with full understanding of state, measurement, and interference. Every lab runs on a laptop; no quantum computer required.

CIPHERICA develops classical platforms for molecular generation and target discovery. Those problems sit in chemistry and complex physical systems, precisely the domain quantum computing aims to transform over the long term.

This course does not claim that every drug will be designed on a QPU next year. It builds the foundation those future workflows will still require: state, measurement, interference, and spin Hamiltonians, taught from the NMR framework that powered the first quantum-computing experiments. The same organization that treats molecular design as structured, rule-constrained search today is teaching the quantum layer that may, over time, become part of how molecules and mechanisms are simulated and optimized.

Why learn quantum computing

The next computing substrate is being built in public

For sixty years, progress meant smaller transistors and more of them. That curve is bending. The companies that built today’s AI stack – Google, IBM, Microsoft, Amazon, and others – are not waiting for the last classical free lunch. They are investing in quantum computing as a different kind of machine: one that stores and processes information in superposition and entanglement, so certain problems do not scale like a taller data center, but like a wider space of amplitudes.

Quantum is not “faster Excel.” It is a bet that nature’s own operating system: spin, interference, many-body physics can be harnessed for computation. The same mathematics that describes an NMR pulse sequence describes a qubit register. The same interference that makes a spectrum readable is what makes algorithms like Grover and phase estimation possible.

Why top labs and platforms care

• Simulation of matter: chemistry, materials, catalysts, and magnetic systems are quantum. Classical AI approximates them; quantum hardware aims to speak their language.
• Hard search and structure: unstructured search, some optimization landscapes, and structured linear-algebra routines sit at the edge of what classical clusters can finish.
• A post-classical hedge: if AI remains hungry for compute, companies want a second substrate, not only more GPUs.
• Cryptography and security: long-term risk and opportunity around public-key schemes force governments and platforms to pay attention now.

The hype says “quantum AI replaces deep learning next year.” The serious position is sharper: quantum is a candidate next-generation platform for the parts of science and optimization where classical representation is the bottleneck — and the organizations building large-scale AI are funding that bet because they already feel the cost of pure classical scaling.

Why learn it anyway

You do not need a million-qubit machine on your desk to gain leverage. You need to understand state, measurement, and interference — what is actually new, what is still classical simulation, and what would change if hardware delivers. That literacy is rare. It is also portable: the same ideas clarify limits of AI, the meaning of “exponential,” and how physical systems compute.

This course starts where those ideas are concrete: spin physics and the Bloch equation, not a vendor dashboard. By the time you reach Grover, you will know what the amplitudes are doing — which is the part no press release can teach.

Positioning

Most quantum courses start from abstract qubits and circuit icons. This course starts from spin-½, the Bloch vector, RF pulses, and product operators, the same objects used in liquid- and solid-state NMR, and shows that quantum gates and algorithms are controlled evolutions of those spins.

You will not memorize vendor APIs. You will understand why amplitudes interfere, what a measurement does, and how a simple oracle algorithm actually works.

Labs are implemented in Clojure so every spin state and gate application remains inspectable data, not a black-box simulator. No prior Lisp experience is required; setup is provided.

Who it is for

• Scientists and engineers who want conceptual ownership of quantum computing, not only tool demos
• NMR / EPR / magnetic resonance practitioners who want the formal bridge to quantum information
• Software and algorithm learners who want the physics under the circuits
• Anyone comfortable with linear algebra who prefers derivation and simulation over hype

Not ideal for: people seeking only a cloud-QPU certificate or notebook walkthroughs with no math.

What you will learn

By the end of the course you will be able to:

• Map spin-½ states, Bloch vectors, and the Bloch equation onto qubits and single-qubit gates
• Treat RF pulses and free evolution as rotation operators and circuit elements
• Use product-operator reasoning for one- and two-spin systems and connect it to multi-qubit Paulis
• Work with density matrices, expectation values, and simple noise ($T_1$T1​, $T_2$T2​) as quantum channels
• Build small quantum circuits as explicit spin Hamiltonians and unitary evolutions
• Explain state, measurement, and interference without hand-waving
• Implement and analyze Deutsch–Jozsa and Grover on small qubit counts by direct simulation
• State clearly what early NMR quantum computing proved — and why scaling required other platforms

Optional bridge (late in the course): circuits and coupling networks as graphs, with gate identities as structured rewrite rules — the same formal idea as graph-rewrite methods used elsewhere in scientific computing.

Course structure (8 weeks)

Format: Asynchronous lectures + weekly simulation labs · optional office hours · lifetime access to materials for this course

Labs: Classical simulation only (laptop). Optional cloud-QPU look at the end — not required.

Prerequisites

• Comfort with linear algebra (matrices, vectors, basic eigenvalues)
• Basic complex numbers
• Curiosity about two-level quantum systems

NMR experience is helpful but not required. Every spin term is defined when it appears. No prior Clojure or quantum-computing course required.

How this is different

Unlike typical quantum courses that start from circuit diagrams and cloud notebooks, this course starts from the Bloch equation and NMR spin physics, builds gates as pulses and couplings, and reaches Grover with full understanding of state, measurement, and interference — with every lab runnable on a laptop and no quantum computer required.

Why Clojure (for the labs)

Quantum ideas are taught first; Clojure is the lab vehicle. Immutable, data-centric code makes each step of a pulse sequence or circuit a value you can print and check – closer to an NMR lab experiment than to a hidden simulator object. You do not need prior Lisp experience.

Instructor

Taught by Gershon Wolfe. His background is in physical chemistry, with emphasis on liquid- and solid-state nuclear magnetic resonance and spin physics, and formal training in quantum mechanics and statistical mechanics. That path is not a side interest in quantum computing – it is the same physics the quantum computing field is built on: two-level systems, coherent evolution under Hamiltonians, rotating frames, and ensemble statistics. This course teaches quantum computing from that foundation upward.

Early liquid-state NMR was one of the first platforms to run small algorithms (Deutsch, Grover sketches). The reason was simple: the control theory was already mature in chemistry. Vendor courses often start at the circuit and treat the pulse as a black box. A physical-chemistry / NMR path starts at the Hamiltonian and shows why the circuit is a valid compression of continuous time evolution.

Statistical mechanics closes the loop: real qubits are open systems, readout is often ensemble-averaged, temperature and decoherence are not footnotes. Someone trained to move between microstates, density operators, and macroscopic observables is already thinking in the language that serious quantum information uses when the ideal circuit model stops being enough.

Closing

Learn quantum computing in the language of spin. Eight weeks from the Bloch equation to Grover state, measurement, and interference included. No quantum computer required. Every lab runs on a laptop.

Next cohort: January 2027

$2,999 · Asynchronous (8 weeks) · Weekly office hours · Lifetime access to this course