Aglais Xqvm Quantum Vm logo

Aglais Xqvm Quantum Vm

Organization
reason-machines
aglais-xqvm-quantum-vm

Expertise in Aglais XQVM, a hardware-agnostic Rust quantum virtual machine for QUBO/Ising binary optimization models targeting quantum annealers.

Overview

Publisherreason-machines
Repositorytrending-skills
Skill nameaglais-xqvm-quantum-vm
Stars
80
Forks
15
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by reason-machines on GitHub. Read the source before you install it.

Installation

Install the Aglais Xqvm Quantum Vm AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/reason-machines/trending-skills.git /tmp/trending-skills
mkdir -p .claude/skills
cp -r /tmp/trending-skills/skills/aglais-xqvm-quantum-vm .claude/skills/aglais-xqvm-quantum-vm
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aglais Xqvm Quantum Vm in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Aglais Xqvm Quantum Vm on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Aglais Xqvm Quantum Vm is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Aglais XQVM Skill

Skill by ara.so — Daily 2026 Skills collection.

Aglais XQVM is a hardware-agnostic virtual machine for quantum computing written in Rust. It provides a unified bytecode intermediate representation for binary optimization problems (QUBO/Ising formulations) targeting quantum annealers — think LLVM for quantum computing. The VM is stack-based with a 256-slot register file, supports no_std + alloc for WASM/bare-metal deployment, and ships four crates: bytecode, assembler, disassembler, and interpreter.

Installation & Setup

Prerequisites

sh
# Install Rust stable
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Install dev tools (cargo-nextest, clippy, etc.)
make deps

Build from source

sh
git clone https://github.com/QuipNetwork/xq-rs
cd xq-rs
cargo build --release
# Binaries: target/release/xqasm, target/release/xqdism, target/release/xqvm

Add as a library dependency

toml
# Cargo.toml
[dependencies]
aglais-xqvm-bytecode = { path = "crates/bytecode" }
aglais-xqvm-vm       = { path = "crates/vm" }

For no_std environments (WASM, bare-metal):

toml
[dependencies]
aglais-xqvm-bytecode = { path = "crates/bytecode", default-features = false, features = ["alloc"] }

Workspace Crate Overview

CrateBinaryRole
aglais-xqvm-bytecodeOpcode table, instruction types, builder, binary codec, stream reader
aglais-xqvm-asmxqasmText assembler: .xqasm.xqbc bytecode
aglais-xqvm-disasmxqdismBytecode → human-readable listing
aglais-xqvm-vmxqvmBytecode interpreter: stack, registers, QUBO/Ising execution

CLI Commands

xqasm — Assembler

sh
# Assemble a source file to bytecode
xqasm program.xqasm -o program.xqbc

# Assemble with verbose output
xqasm program.xqasm -o program.xqbc --verbose

xqdism — Disassembler

sh
# Inspect bytecode encoding as human-readable listing
xqdism program.xqbc

# Pipe to file
xqdism program.xqbc > listing.txt

xqvm — Interpreter

sh
# Execute bytecode
xqvm program.xqbc

# Run with debug output (if supported)
xqvm program.xqbc --debug

Full pipeline

sh
xqasm problem.xqasm -o problem.xqbc && xqdism problem.xqbc && xqvm problem.xqbc

XQASM Language Reference

The assembler accepts .xqasm text files. The VM is stack-based; most instructions pop operands from the stack and push results.

Basic stack operations

asm
; push two integers and add them
PUSH 10
PUSH 32
ADD
HALT

Registers (0–255)

asm
PUSH 42
STORE 0        ; pop stack → register 0
LOAD  0        ; push register 0 → stack

Arithmetic

asm
PUSH 10
PUSH 3
ADD            ; stack: [13]
PUSH 7
SUB            ; stack: [6]
PUSH 2
MUL            ; stack: [12]
PUSH 4
DIV            ; stack: [3]

Vectors / integer arrays

asm
; build a 3-element vector [1, 2, 3]
PUSH 1
PUSH 2
PUSH 3
PUSH 3         ; length
VEC            ; stack: [Vec([1,2,3])]
STORE 1

QUBO / Ising model construction

asm
; XQMX_NEW n creates an n-variable QUBO model
PUSH 4
XQMX_NEW       ; stack: [XqmxModel(4 vars)]
STORE 2

; set quadratic coupling Q[i][j] = weight
LOAD  2
PUSH  0        ; i
PUSH  1        ; j
PUSH  -1       ; weight (integer encoding)
XQMX_SET_Q    ; modifies model in reg 2

; set linear bias h[i] = weight
LOAD  2
PUSH  0
PUSH  5
XQMX_SET_H

; evaluate energy of a candidate solution
LOAD  2        ; model
PUSH  0        ; sample register (XqmxSample)
XQMX_EVAL     ; pushes energy onto stack

Control flow & iteration

asm
; RANGE lo hi → loop stack entry, ITER steps through it
PUSH 0
PUSH 5
RANGE          ; loop i in 0..5
ITER           ; advance; jumps past matching END_ITER when done
  LOAD 0
  PUSH 1
  ADD
  STORE 0
END_ITER

HALT

Labels and jumps

asm
  PUSH 0
loop:
  PUSH 1
  ADD
  DUP
  PUSH 10
  LT
  JMP_TRUE loop
HALT

Rust API: Bytecode Builder

Use aglais-xqvm-bytecode to construct programs programmatically:

rust
use aglais_xqvm_bytecode::{BytecodeBuilder, Instruction, Opcode};

fn build_add_program() -> Vec<u8> {
    let mut builder = BytecodeBuilder::new();

    builder.emit(Instruction::Push(10));
    builder.emit(Instruction::Push(32));
    builder.emit(Instruction::Add);
    builder.emit(Instruction::Halt);

    builder.finish()
}

Decoding bytecode (stream reader)

rust
use aglais_xqvm_bytecode::StreamReader;

fn decode(bytes: &[u8]) {
    let mut reader = StreamReader::new(bytes);
    while let Some(instr) = reader.next_instruction().unwrap() {
        println!("{:?}", instr);
    }
}

Rust API: Running the VM

rust
use aglais_xqvm_vm::Vm;

fn main() {
    // Load bytecode from a file
    let bytecode = std::fs::read("program.xqbc").expect("read bytecode");

    let mut vm = Vm::new();
    vm.load(&bytecode).expect("load");
    vm.run().expect("run");

    // Inspect top of stack after execution
    if let Some(val) = vm.stack_top() {
        println!("Result: {:?}", val);
    }
}

Accessing registers after execution

rust
use aglais_xqvm_vm::{Vm, Value};

fn run_and_inspect(bytecode: &[u8]) -> Value {
    let mut vm = Vm::new();
    vm.load(bytecode).unwrap();
    vm.run().unwrap();
    vm.register(0).cloned().unwrap_or(Value::Int(0))
}

Real-World Pattern: TSP as QUBO

The crates/vm/examples/tsp/ directory contains a complete Travelling Salesman Problem encoded as a QUBO driven by a Rust harness. The pattern is:

  1. Generate coefficients in a Rust harness (problem-specific math).
  2. Emit .xqasm files parameterised by those coefficients.
  3. Assemble + run with xqasm / xqvm.
rust
// crates/vm/examples/tsp/main.rs pattern
use std::process::Command;

fn assemble_and_run(src: &str, out: &str) {
    let asm = Command::new("xqasm")
        .args([src, "-o", out])
        .status()
        .expect("xqasm failed");
    assert!(asm.success());

    let run = Command::new("xqvm")
        .arg(out)
        .status()
        .expect("xqvm failed");
    assert!(run.success());
}

fn main() {
    assemble_and_run("init.xqasm",    "init.xqbc");
    assemble_and_run("problem.xqasm", "problem.xqbc");
    assemble_and_run("eval.xqasm",    "eval.xqbc");
}

Common Patterns

Pattern: build a QUBO model in assembly

asm
; 2-variable QUBO: minimise x0 - x1 + 2*x0*x1
PUSH 2
XQMX_NEW
STORE 0

LOAD 0
PUSH 0
PUSH -1        ; h[0] = -1  (linear)
XQMX_SET_H

LOAD 0
PUSH 1
PUSH -1        ; h[1] = -1  (linear)
XQMX_SET_H

LOAD 0
PUSH 0
PUSH 1
PUSH 2         ; Q[0][1] = 2 (quadratic)
XQMX_SET_Q

HALT

Pattern: iterate over model variables

asm
PUSH 4
XQMX_NEW
STORE 0

PUSH 0
PUSH 4
RANGE
ITER
  ; register 1 holds current loop index after ITER
  LOAD  0
  LOAD  1      ; index i
  LOAD  1      ; index i (diagonal → linear term)
  PUSH  -1
  XQMX_SET_Q
END_ITER

HALT

Pattern: no_std bytecode decoding (WASM)

rust
#![no_std]
extern crate alloc;

use alloc::vec::Vec;
use aglais_xqvm_bytecode::StreamReader;

pub fn decode_instructions(bytes: &[u8]) -> Vec<alloc::string::String> {
    let mut reader = StreamReader::new(bytes);
    let mut out = Vec::new();
    while let Ok(Some(instr)) = reader.next_instruction() {
        out.push(alloc::format!("{:?}", instr));
    }
    out
}

Development Workflow

sh
# Run all lints and tests (mirrors CI)
make all

# Run only tests
cargo test --workspace

# Run lints
cargo clippy --workspace --all-targets -- -D warnings

# Format
cargo fmt --all

# Run a specific example
cargo run --example tsp --manifest-path crates/vm/Cargo.toml

Instruction Set Quick Reference

The opcode table in crates/bytecode/src/types/table.rs is the single source of truth for all 76 instructions. Key categories:

CategoryInstructions
StackPUSH, POP, DUP, SWAP
RegistersLOAD, STORE
ArithmeticADD, SUB, MUL, DIV, NEG
ComparisonEQ, LT, GT, LE, GE
Control flowJMP, JMP_TRUE, JMP_FALSE, CALL, RET, HALT
IterationRANGE, ITER, END_ITER
VectorsVEC, VEC_GET, VEC_SET, VEC_LEN
QUBO/IsingXQMX_NEW, XQMX_SET_Q, XQMX_SET_H, XQMX_EVAL, XQMX_SAMPLE

All operands are big-endian. The binary format is a bare instruction stream with no file header.

Troubleshooting

xqasm: command not found

Ensure target/release is on $PATH or use the full path:

sh
export PATH="$PWD/target/release:$PATH"

Stack underflow at runtime

The VM is strictly stack-based. Every instruction that pops values requires them to be present. Check that PUSH / LOAD precedes every operation, and that loops don't consume values without restoring the stack balance.

ITER never terminates

RANGE pushes loop bounds onto the loop stack (separate from the value stack). Ensure every RANGE has a matching END_ITER and that the range bounds (lo, hi) are pushed in the correct order (lo first, hi second).

Build fails in no_std environment

Disable default features and enable the alloc feature on aglais-xqvm-bytecode:

toml
aglais-xqvm-bytecode = { ..., default-features = false, features = ["alloc"] }

The VM crate (aglais-xqvm-vm) requires std and is not suitable for bare-metal.

Inspecting unexpected bytecode

Use xqdism to verify the assembler output before running:

sh
xqasm suspect.xqasm -o suspect.xqbc
xqdism suspect.xqbc   # check instruction sequence and operand values
xqvm   suspect.xqbc

License

AGPL-3.0-or-later. Embedding in proprietary network services requires source disclosure under the AGPL.

Frequently asked questions

What does the Aglais Xqvm Quantum Vm AI skill do?

Expertise in Aglais XQVM, a hardware-agnostic Rust quantum virtual machine for QUBO/Ising binary optimization models targeting quantum annealers.

Why use Aglais Xqvm Quantum Vm on TypingMind?

Because you install it once and use it with any model. Aglais Xqvm Quantum Vm is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Aglais Xqvm Quantum Vm in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/reason-machines/trending-skills/tree/main/skills/aglais-xqvm-quantum-vm. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Aglais Xqvm Quantum Vm?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Aglais Xqvm Quantum Vm?

As many as you like. As long as a model supports skills, you can use Aglais Xqvm Quantum Vm with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Aglais Xqvm Quantum Vm AI skill free?

It is published on GitHub by reason-machines. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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