#3893 Chemistry DSL in Racket
#3893 Chemistry DSL in Racket
#3893 Create a DSL in Racket for chemistry
This is a great use case for Racket. Racket excels at Language Oriented Programming, allowing us to define custom syntax (macros) that makes chemical notation look natural, while handling the computation in the background.
Below is a complete, runnable DSL called Alchemy.
The "Alchemy" DSL Features
Natural Syntax: Define molecules like
(def-molecule Water (H 2) (O 1)).Stoichiometry: Write reactions using standard arrow notation:
(reaction (2 H2) + (1 O2) -> (2 H2O)).Computations: Automatically calculates molar masses and validates if a chemical equation is balanced.
The Implementation
Save this code in a file (e.g., alchemy.rkt) or run it in DrRacket.
#lang racket
;; ==========================================
;; PART 1: THE DATA LAYER (The Periodic Table)
;; ==========================================
;; A struct to hold atomic data
(struct element (symbol name atomic-mass) #:transparent)
;; Global registry for elements
(define periodic-table (make-hash))
;; Helper to define elements quickly
(define (register-element sym name mass)
(hash-set! periodic-table sym (element sym name mass)))
;; Let's populate a few common elements for the demo
(register-element 'H "Hydrogen" 1.008)
(register-element 'C "Carbon" 12.011)
(register-element 'N "Nitrogen" 14.007)
(register-element 'O "Oxygen" 15.999)
(register-element 'Na "Sodium" 22.990)
(register-element 'Cl "Chlorine" 35.45)
;; ==========================================
;; PART 2: MOLECULES & LOGIC
;; ==========================================
;; A molecule is a list of (element-symbol count) pairs
(struct molecule (name formula mass) #:transparent)
;; Calculate molar mass recursively
(define (calculate-mass formula)
(for/sum ([component formula])
(match-define (list sym count) component)
(define elem (hash-ref periodic-table sym
(lambda () (error "Unknown element:" sym))))
(* (element-atomic-mass elem) count)))
;; ==========================================
;; PART 3: THE MACROS (SYNTAX SUGAR)
;; ==========================================
;; Macro 1: Define a Molecule
;; Usage: (def-molecule Water (H 2) (O 1))
(define-syntax-rule (def-molecule name (atom count) ...)
(define name
(let ([formula (list (list 'atom count) ...)])
(molecule 'name formula (calculate-mass formula)))))
;; Macro 2: Define a Reaction
;; This validates conservation of mass (balancing)
;; Usage: (reaction (1 CH4) + (2 O2) -> (1 CO2) + (2 H2O))
(define-syntax (reaction stx)
(syntax-case stx (-> +)
[(_ (coeff1 mol1) ... -> (coeff2 mol2) ...)
;; Filter out the '+' signs using a helper or assume user omits them for raw list processing
;; For this DSL, we will accept the list but ignore '+' in the expansion logic below
#'(validate-reaction
(list (list coeff1 mol1) ...)
(list (list coeff2 mol2) ...))]))
;; ==========================================
;; PART 4: REACTION LOGIC
;; ==========================================
;; Helper to sum atoms in a side of the equation
;; Returns a hash: { 'H: 4, 'O: 2 }
(define (sum-atoms component-list)
(define total-atoms (make-hash))
(for ([item component-list])
(match-define (list coeff mol) item)
(unless (eq? coeff '+) ; Skip the '+' symbols if passed
(for ([atom-pair (molecule-formula mol)])
(match-define (list sym count) atom-pair)
(hash-update! total-atoms sym
(lambda (v) (+ v (* count coeff)))
0))))
total-atoms)
(define (validate-reaction reactants products)
(define left-side (sum-atoms reactants))
(define right-side (sum-atoms products))
(displayln "--- Analyzing Reaction ---")
(if (equal? left-side right-side)
(begin
(displayln "✅ BALANCED")
(displayln (format "Reactant Mass: ~a g/mol" (calculate-side-mass reactants)))
(displayln (format "Product Mass: ~a g/mol" (calculate-side-mass products))))
(begin
(displayln "❌ UNBALANCED")
(displayln "Left side atoms:")
(displayln left-side)
(displayln "Right side atoms:")
(displayln right-side))))
(define (calculate-side-mass list-of-comps)
(for/sum ([item list-of-comps])
(match-define (list coeff x) item)
(if (eq? coeff '+) 0 (* coeff (molecule-mass x)))))
;; ==========================================
;; PART 5: DSL DEMO
;; ==========================================
;; 1. Define Molecules
(def-molecule Methane (C 1) (H 4))
(def-molecule Oxygen (O 2))
(def-molecule Water (H 2) (O 1))
(def-molecule CarbonD (C 1) (O 2))
(displayln (format "Molar Mass of Methane: ~a" (molecule-mass Methane)))
;; 2. Define a Reaction (Combustion of Methane)
;; Equation: CH4 + 2 O2 -> CO2 + 2 H2O
(displayln "\nTest 1: Balanced Combustion")
(reaction (1 Methane) + (2 Oxygen) -> (1 CarbonD) + (2 Water))
(displayln "\nTest 2: Unbalanced Attempt")
;; Missing an oxygen molecule on the left
(reaction (1 Methane) + (1 Oxygen) -> (1 CarbonD) + (2 Water))
How it works
1. The Periodic Table (Hash Map)
We rely on a simple hash to store element data. In a production DSL, you would import a JSON or CSV file containing the entire periodic table.
2. def-molecule (The Structure Macro)
This macro takes a readable syntax:
(def-molecule Water (H 2) (O 1))
And transforms it into a standard Racket variable definition equivalent to:
(define Water (molecule 'Water '((H 2) (O 1)) 18.015))
It pre-calculates the molar mass ($M$) during definition so lookups are fast.
3. reaction (The Syntax Macro)
This utilizes Racket's syntax-case to parse a custom infix notation.
It looks for the
->token to separate reactants from products.It accepts
+tokens for readability but ignores them during calculation.It passes the data to
validate-reaction, which sums the atoms on the Left Hand Side (LHS) and Right Hand Side (RHS).
Example Output
When you run the code, the output will look like this:
Molar Mass of Methane: 16.043
Test 1: Balanced Combustion
--- Analyzing Reaction ---
✅ BALANCED
Reactant Mass: 80.041 g/mol
Product Mass: 80.041 g/mol
Test 2: Unbalanced Attempt
--- Analyzing Reaction ---
❌ UNBALANCED
Left side atoms:
#hash((C . 1) (H . 4) (O . 2))
Right side atoms:
#hash((C . 1) (H . 4) (O . 4))List and describe Quantum Computing DSLs that should be created
The current quantum ecosystem relies heavily on "assembly-level" thinking—placing individual gates (Hadamard, CNOT) on wires. To move from experimentation to engineering, we need DSLs that abstract away the quantum mechanics in favor of domain logic.
Here are the high-impact Quantum Computing DSLs that should be created, categorized by their abstraction layer.
1. QEC-Lang (Quantum Error Correction DSL)
The Problem: Currently, implementing error correction (like Surface Codes) requires manually wiring thousands of physical qubits to form a single logical qubit. Changing the code distance or geometry requires rewriting the entire circuit.
The Solution: A declarative DSL where you define the topology and stabilizers, and the compiler handles the physical qubit mapping and syndrome extraction circuits.
Key Features:
Primitives for defining lattice geometries (hexagonal, square).
Automatic generation of syndrome measurement circuits.
Built-in decoders definition (e.g., "Use Minimum Weight Perfect Matching for X-errors").
Conceptual Syntax:
define code SurfaceCode(distance=5): lattice: square_grid stabilizers: vertex: Z_type plaquette: X_type logic: logical_0: Z_chain(boundary_left, boundary_right)
2. Q-Spec (Quantum Verification & Assertion DSL)
The Problem: Debugging quantum circuits is nearly impossible because measuring the state destroys it. Developers need a way to define "invariants" without collapsing the wavefunction.
The Solution: A testing-oriented DSL used alongside standard languages (like Qiskit/Q#) to define properties that must hold true. On simulators, these are exact checks; on hardware, they compile to "non-destructive measurement" gadgets.
Key Features:
Assertions for entanglement (e.g.,
assert_entangled(q1, q2)).Phase invariants (e.g., "The relative phase between state |0> and |1> must be pi").
Safe "uncompute" blocks to verify a register has returned to |0>.
Conceptual Syntax:
@invariant def BellStateCheck(q_pair): expect(q_pair).to_be_in_superposition() expect(measure(q_pair, basis='Bell')).to_be_deterministic()
3. MolSim (High-Level Chemistry DSL)
The Problem: Chemists want to simulate molecular ground states, but they have to think in terms of U3 gates and qubit connectivity. Existing tools (like Qiskit Nature) are libraries, not true DSLs, meaning the syntax is still Python/C++ rather than chemical notation.
The Solution: A DSL where the first-class citizens are electrons, orbitals, and Hamiltonians, not qubits.
Key Features:
Direct mapping of fermionic operators a ^dagger, a) to qubit gates (Jordan-Wigner/Bravyi-Kitaev) handled by the compiler.
Native syntax for Ansatz construction (e.g., "Apply UCCSD with depth 2").
Automatic resource estimation (e.g., "This molecule requires 50 logical qubits").
Conceptual Syntax:
molecule "LithiumHydride": geometry: [Li at (0,0,0), H at (0,0,1.6)] basis: sto-3g simulation GroundState: method: VQE ansatz: unitary_coupled_cluster(singles, doubles) optimizer: COBYLA
4. Orchestra (Hybrid Runtime DSL)
The Problem: Real-world advantage comes from hybrid algorithms (like VQE or QAOA) that loop between a Classical CPU and a Quantum QPU thousands of times. Latency kills performance here.
The Solution: A DSL focused on data flow and timing between the CPU and QPU. It treats the QPU as an accelerator (like a GPU shader) and optimizes the data transfer pipeline.
Key Features:
quantum_kernelblocks that compile to pulse sequences.classical_feedbackblocks that execute within the coherence time of the qubits.Explicit definitions for "shots" and readout error mitigation strategies.
5. Q-Fin (Quantum Finance DSL)
The Problem: Financial algorithms (Monte Carlo pricing) rely on loading probability distributions into quantum states, which is notoriously difficult ("State Preparation Problem").
The Solution: A DSL specialized in loading stochastic processes into quantum registers and performing amplitude estimation.
Key Features:
Primitives for standard distributions (Gaussian, Log-Normal).
Built-in "Oracle" generators for payoff functions (e.g., European Call Option payoff).
Abstracted Amplitude Estimation (AE) to retrieve the result without manually building the Grover operator.
6. TensoFlow (Visual Tensor Network DSL)
The Problem: Circuit diagrams are bad at representing entanglement flow and massive many-body states.
The Solution: A visual-first DSL based on Tensor Networks (MPS, PEPS). Instead of wires and gates, users connect "tensors" (shapes) with "legs" (indices).
Key Features:
Drag-and-drop tensor contraction.
Visual representation of "bond dimension" (how much entanglement is allowed).
Compiles down to quantum circuits or runs classically if entanglement is low.
Relevant Video Resource
To better understand the current "assembly" level of coding that these DSLs would replace, this video provides a clear visual explanation of the low-level logic we currently use.
This video is relevant because it demonstrates the complexity of current quantum programming (using gates like Hadamard and CNOT directly), highlighting exactly why the higher-level DSLs listed above are necessary to advance the field.
From <https://gemini.google.com/app/> Google Gemini 3.0 Pro (Thinking)
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